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Cite as: Tonuchi, E, J. (2019). Impact of nonoil export on the economic growth of Nigeria. Available [Online] https://thesismind.com/nonoilexporteconomicgrowthnigeria
Title: Impact of NonOil Export on the economic growth of Nigeria
ABSTRACT
This research work examined the impact of NonOil Export on the economic growth of Nigeria within the sample period of 19852016. The research work employed multiple regression analysis using Vector Error Correction Model (VECM) to estimate the relationship. The unit root test revealed that none of the variable examined was stationary at level, but at first difference all the variables examined became stationary given the 5% level of significance. The Johansson cointegration test revealed that there is presence of cointegration with at least two cointegrating equation among the variables examined. The research further revealed that 93.53% of the variations in economic growth of Nigeria was influenced by the changes in the Nigeria NonOil Export variables. In conclusion, the research revealed that there was sustainable relationship between Nigeria NonOil Export and economic growth in Nigeria. The researcher therefore recommend among others that, government should endeavor to support various export promotion programmes and institutions. This could be achieved by encouraging financial institutions, both formal and informal, to make loans available at reduced rates of interest for investors as to increase the level of investment in this country.
CHAPTER ONE
INTRODUCTION
1.1 Background to the Study
Exportation is required by any economy to enhance revenue and usher in economic growth and development. Itis therefore, crucial for economic progress sand this has informed the idea of exportled growth. Export is a catalyst necessary for the overall development of an economy (AbouStrait, 2005). It was also noted that foreign trade creates an avenue for foreign capital to flow into a country (Kubalu & Hanif, 2016). This increases the earnings of the country thereby creating an avenue for growth by raising the national (Kromtit, Kanadi, Ndangra & Suleiman, 2017). In a country like Nigeria where the level of investment is low, foreign capital is very much needed in order to accelerate the creeping rate of economic growth. The Nigerian economy is one that depends largely on foreign trade for growth and is also one which depends majorly on one export commodity at a time. For instance, at independence, the major export commodity was cocoa and the leading sector in the economy was the agricultural sector but today, the major export commodity is crude oil and the leading sector is now the petroleum sector (Eze Onyebuchi Michael, 2017).
Export promotion has been argued by many economist as a major driver of economic growth in any country of the world. In Nigeria, the export sector is characterized by the dominance of a single export commodity. In the decades of the 1960’s and 1970’s the Nigerian economy was dominated by agricultural commodity exports. Such commodities included Cocoa, groundnut, cotton and palm produces. From the mid1970s crude oil became the main export product of the Nigerian economy (Ifeacho, Omoniyi, and Olufemi, 2014; Abogan, Akinola and Baruwa, 2014). The economy is said to be suffering from the Dutch disease resulting from her mono export of oil. Of course, Nigerian crude oil is of the light and sweet type and is highly sought after in the international oil market.
The export of crude oil now constitutes about 96% of total exports. The performance of the nonoil exports in the past two decades leaves little or nothing to be desired. The policy concern over the years has therefore been to expand nonoil export in a bid to diversify the nation’s export base. The diversification of the Nigerian economy is necessary for important reasons, first the volatility of the international oil market with the attendant volatility of government revenue gives credence to any argument for diversification of exports (Ifeacho, Omoniyi, and Olufemi, 2014; Kawai, 2017). Secondly, the importance of export to a nation’s economic growth and development cannot be overemphasized.
Exports are goods and services produced domestically and purchased by foreigners. Net exports are the difference between total exports and total imports. According to Afolabi (2011) Export can be defined as surplus goods and services of a country that are sent to other countries in the world for sale. Samuelson and Nordhaus (2010) see exports as the mirror image of imports. That one countries export is another’s imports. However, export is any goods or commodity transported from one country to another country in a legitimate fashion typically for use in trade (Oluchi, 2007).
Export is a catalyst necessary for the overall development of an economy. The primary objective of export policies in any economy is to increase the level of economic activities. It follows, therefore that export policies should be directed to the sector in which the impact of an increase in export demand will be both desirable and large. It is a source of foreign exchange earnings and since trade transaction followed by the ―oil boom period which arose from oil glut in the world oil market since 1981 only led to the neglect of nonoil export productive base (Noko, 2016).
This has also led to panic measures by successive government from the economic stabilization Act of 1982. Counter trade policy of Buhari/Idiagbon regime and the introduction of structural adjustment programme (SAP) by the Babangida Administration hence the need to diversify the export base of the economy. Prior to the phenomena emergence of the oil sector, Agriculture is one of oldest occupations in Nigeria and has been the main slay of the Nigerian economy contributing 80% of the export earnings and 75% of the Gross Domestic Product (GDP) (Eze Onyebuchi Michael, 2017; Kromtit et al, 2017; Kawai, 2017).
Consequently, this position has fallen consistently to date, the attendant fluctuation in the nonoil export promotion, the world prices of agriculture and manufacture products and the emergence of oil have helped in no small measure in diverging the role of agriculture in the nation’s development. This situation is worsened by the almost total neglect of the agricultural sector. The Nigerian economy has not recovered from the resultant disequilibria in both domestic and external sectors, this has therefore brought about the need for adjustment in Nigeria to diversify and restructure the productive base of the economy in order to reduce its dependence on oil export. It is this concerns the country nonoil exports (Kawai, 2017).
Furthermore, a welldeveloped sector will provide employment opportunity for the people with the attendant reduction in social costs of unemployment. Earnings from export reduce the strains on the balance of payment position and even improve it. A rewarding export drive can turn hitherto under developed economy into a prosperous economy. Export help in increasing the level of aggregate economic activities through its multipliers effects on the level of natural income. Income earned through exporting will help in increasing the level of demand within the economy. The Nigerian external sector has always been dominated by primary commodities which have the wellknown basic characteristic of low price and income elasticity of demand, low growth of demand, terms of trade and instability of export earnings. This monoculture situation brought untold hardship on the people of the country (Kawai, 2017; Noko, 2016).
For instance, from 1970 to date oil exporting has constituted on the average of 90% of the total foreign exchange earnings. The adversity of the fluctuation in oil prices has in no small measure slated the developmental efforts of the various governments. This has made the Nigerian economy to scoring from the ―oil boom era‖, as exemplified by the buoyant economy of the period with massive infrastructural development and the Udoji award (Ifeacho, Omoniyi, and Olufemi, 2014; Noko, 2016)
A robust and strong export trade is indicative of how competitive the commodities and services are, and how large the scale of the industrial base of an economy is, this is reflected by the comparative advantages possessed by the country. Also, exports of commodities are possible when domestic demand for such are satisfied and surpluses exist in commercial quantities. Thus, the nonoil export sector serves as the hub for exporting these surpluses produces by the nonoil base of the country’s economy. Okoh (2004) observed that global integration had positive but not significant relationship in explaining the behavior of nonoil exports in the longrun. Since the aggregate nonoil exports data used by previous studies may biased their conclusion and the need to correct the existing cultural distortions and put the economy on the path of sustainable growth is therefore compelling. This raises the question of what need to be done in order to diversify the economy and develop the nonoil sector to realize the potentials of the sector at large.
1.2 Statement to the Problem
Prior to the discovery of oil in Nigeria in commercial quantities, agriculture sector dominates the economy in terms of export earnings, contribution to gross domestic product, and employment generation. Government earnings also depended heavily on taxes on export. Thus, during the period, the current account and fiscal balances depended on the agricultural sector. Until the early 1970’s where reliance was shifted to crude oil with the discovery of oil and rise in the price of oil in the 1970’s (Noko, 2016; Kawai, 2017).
The challenges of nonoil export sector is not that it is being over shadowed by the oil export trade, but traceable to declining nonoil export and loss of market share in the nonoil trade globally is a clear evidence of how the nonoil sector competitiveness of the Nigerian economy has been consistently eroded over the last three decades. A robust and strong export trade is indicative of how competitive the commodities and services are, and how large the scale of the industrial base of an economy is, this is reflected by the comparative advantages possessed by the country (Kawai, 2017). Also, exports of commodities are possible when domestic demand for such are satisfied and surpluses exist in commercial quantities. Thus, the nonoil export sector serves as the hub for exporting these surpluses produces by the nonoil base of the country’s economy (Eze et al, 2017). There has been several research works which have examined the relationship between nonoil export and economic growth. Another important problem was the poor implementation of policy measures by the various government agencies. Most of the institutions involved in policies implementation were very ineffective and were not particularly oriented to the needs of majority of the small farmers. Such inadequacies were common in key institution like credit agencies, research institution, commodity board, river basin development authority and institution which handled input procurement and distribution. Many of them either did not have adequate facilities and funds or competent staff to enable them to work efficiently. Inappropriate (Kawai, 2017; Kromtit et al, 2017).
Some previous studies showed the relationship between nonoil exports and Nigeria economic growth while other showed the extent at which nonoil export individually affect the economic growth of Nigeria (Kromtit et al, 2017). This study, however, failed to analyze clearly the effect of Bank credit on nonoil export earning cum economic growth of Nigeria. This research work will attempt at verifying the effect of this variable and hence closed the gap in knowledge inherent in other studies.
1.3 Research Question
In attempt to address the various problems prominent with Nigeria nonoil export, various questions has been raised by the researcher. The following questions will guide the research work:
 To what extent does nonoil export impact on Nigeria economic growth?
 Is there any observed longrun relationship between nonoil export and economic growth of Nigeria?
1.4 Objectives of the Study
The objective of this study is to evaluate; the significant relationship between nonoil export and economic growth in Nigeria. Specifically, the objective of this study include to:
1. Examine the impact of nonoil export on the economic growth of Nigeria.
2. Investigate the long run relationship between nonoil export and economic growth of Nigeria.
1.5 Statement of Hypothesis
H_{o}: Nonoil export has no significant impact on the economic growth of Nigeria.
H_{o}: Nonoil export has no long run relationship with economic growth in Nigeria.
1.6 Significance of the Study
The impact of nonoil export on the sustainable growth of any nation cannot be overemphasized; since increase in export earnings (over its counterpart, import) would make any Nation betteroff in trade with other countries. Therefore, this work will be of immense importance to government and its agencies, and the general public. Also, it will be of great importance to ministry of trade and industry, investors as well as financial intermediaries or institutions. Above all, it will be a stream of knowledge for economist, students and researchers who have interest on issues relating to nonoil export in Nigeria.
1.7 Scope and Limitation of Study
This research analyze the impact of nonoil export in Nigeria economy, taking proper analysis on various ways and means put forward by the government of Nigeria to improve nonoil export earnings since 19812016.
The research work, however, is not void of constraints as the researcher encountered a number of constraints in the cause of this work. The constraints include data sourcing as well as data inconsistency due to poor nature of information management in Nigeria. However, host of other constraints that prevent the researcher to present a better work than this abound. Prominent among them are time factor, financial constraints and lack of electricity. In spite of the aforementioned constraint above, the researcher made adequate efforts to present a clear and wellarticulated research work.
CHAPTER TWO
2.1 Theoretical Literature
ExportLed Growth Theories
The literature on international trade which suggests that exports have a positive impact on economic growth is known as the export–ledgrowth (Giles and Williams, 2000). The present literature presents several plausible theoretical arguments supporting the view that exporting activities and overall economic growth are positively associated. On the one hand, exporting implies that a country gain access to the wider external demand, which act as a stimulus to domestic output and hence economic growth. Second, it is frequently argued that small domestic markets may not grow continuously and that any positive economic shock leading to the expansion of the domestic market is more likely to decay quickly. On the other hand, large external markets do not always encompass growth restrictions on the demand side, and this leads to the exploitation of economies of scale (Kromtit et al, 2017; Baale and Mutenuo, 2001).
The notion of trade as an engine of growth is given much emphasis by many economists. The idea that international trade brings economic growth and also increases the welfare of a nation started during the 17^{th} century by a group of merchants, government officials and philosophers who advocated on economic philosophy known as mercantilism. For a nation to become and also powerful, it has to export more than it imports where the resulting export surplus is used to purchase precious metals like gold and silver. The Government in its power has control on imports and stimulates the nation’s export (Afolabi, 2011). As known, mercantilists prohibited the ultimate good import (for it causes valuable mine output) and aimed to increase the import of valuable mine by increasing export. In contemporary phrasing, this situation necessarily means running the balance of payments surplus and this aim states the essential intend of mercantilism (Kucukasoy, 2001; Kromtit et al, 2017).
Adam smith, as he briefly wrote in the “wealth of Nations stated that foreign trade will cause welfare gain in this way by the sentence that” if a foreign country can supply a commodity to us cheaper than use produce, buying this commodity from that country is useful for the country’. This option is based on a simple and intuitive logic. The country itself must produce the products it produces with less cost but he mustn’t produce goods it produces with high cost compared to the other country and must buy goods its self produces by given them to that country. With this, both the two countries together will get welfare gain (Ravenhill, 2005; Noko, 2016). In the words of Adam Smith, “between whatever place foreign trade is carried out, they all drive two district benefits from it. It carried out the surplus part of the produce of their land and labour for which there is demand for the said product. It gives value to their superfluities by exchanging them for something else which may satisfy a part of their wants and increase their enjoyment. By means of it, the narrowness of home market does not hinder the division of labour in any particular branch of art or manufacture from being carried to the highest perfection. By opening a more extensive market for whatever part of the produce of their labour many exceed the home consumption. It encourages them to improve their production capacities as well augment its annual produce to the utmost thereby increase the revenue and wealth of the society”.
If getting goods form a foreign country is cheaper than those a domestic economy can accomplish is possible, it is more suitable to buy these goods in return for some part of goods in which domestic economy is advantageous. A country’s natural advantage to another can be relatively huge especially in the production of certain goods and so all other countries think that struggling with the given country will be a futile effort (smith, 2002; Kromtit et al, 2017).
According to the traditional Keynesian theory, an increase in exports is one of the factors that can cause increase in demand and thus will surely bring about increase in outputs, all other things being equal. Indeed, most people believe that the major constraints of modern economic growth lie on the supply side instead of on the demand side. In other words, they believe that only increase in factor input and improvements in economic efficiency can stimulate economic growth (Lin and li, 2007; Kromtit et al, 2017).
HuckscherOhlin export theory
Another theory on exportled growth is the HucksterOhlin theory. According to Souderton and Reed (1994) The HucksterOhlin theory postulates that international trade of which exports are expected to constitute the major component will significantly reduce the gap between the rich and poor countries. The theory contends that innercountry differences in factor endowments are the basis for foreign trade (Noko, 2016; Kawai, 2017). Comparative cost advantage comes as a result of different factor intensities in the production of various commodities. The HeckscherOhlin theory also implies that free trade specialization in production based on relative factor endowments will tend to bring about factor price equalization and thus will increase the return to labour in poor countries to the levels in rich countries; this suggests that international trade in general and export in particular have the ability to mitigate inequality in income and wealth distribution between and within nations as well as the ability to bring about a convergence in absolute poverty incidence between the rich and poor countries (ozughahu and Ajiayi, 2004)
John Stuart, mill (1848, as cited in Oluchi, 2007) in his principle of political economy: in addition to static growth of trade and dynamic gain from trade which include:
 Widen the extent of the market, inducer innovation and increase productivity.
 Have educative effect in instilling new ideas, wants, taste, and transfer of technology, skill and entrepreneurship.
 Increase savings and capital accumulation. Therefore, trade offers a poor nations the opportunity of removing domestic shortage to overcome the economics of the small size of its domestic market.
According to Lee and Huang (2002) export growth are vice versa. The theoretical justification for these hypotheses is discussed as follows: From the growththeory literature point of view, export expansion is the key factor promoting economic growth. There are various explanations that have been put forward to relate these two variables to each other. First, the growth through its impact on higher rates of capital formation. Second, the growth of export helps release the foreign exchange constraints, thereby facilitating import of capital goods and hence faster growth. Third, competition from overseas ensures an efficient price mechanism that fosters optimum resource allocation and increase the pressure on industries that export goods to keep cost relatively low and to improve technological change, thereby promoting economic growth. Clearly, these arguments lead us to hypothesis that exports contribute to economic growth (Kromtit et al, 2017).
The literature on exogenous growth theory also buttresses the export driven economic growth nexus. This theory posits that logrun economic growth due to increased exports allows for specialization in the sectors with economies of scale. Economics of scale may also arise from human capital accumulation, research and development. Increased export over imports also harness terms of trade and improve foreign exchange earnings. Relative to the nonexport sectors, enhanced export also has a derived effect as external economies could also lead to improved management styles and achieved efficient production techniques (technological transfers). This can be achieved as producers gain knowledge of best practice production through their contacts with buyers on the international market (Paul’s Reinhardt and Robinson, 2003; Lin and li, 2007; Kromtit et al, 2017). As noted by Giles and Williams (2000) that different reason have been proposed for explaining the evidence found in previous studies dealing with this issue on exportled growth. The simplest explanation is that as the contribution to growth made by domestic consumption is limited to the size of regional (or national) markets, sales to foreign markets represents an additional consumption demand which increases the amount of real output produced in the economy.
The Export Crop Sector In Nigeria
In 1977 the commodity marketing boards were established by the federal military Government with the purpose of talking care of specific crop such as cocoa rubber roots and tuber, etc. food imports were limited but crop production for export was intensified during the period of liberalization. poor to liberalization, the overall objectives of trade policy in Nigeria include a marketing Board Policy (1960 1977) through which all exportable agricultural products were purchased by the Government at prices far lower than world prices, and incentive were given to farmers to increase their acreage and adopt some important technologies (Lin and li, 2007; Kromtit et al, 2017).
The liberalization and diversification of the economy of Nigeria was a major aim of the structural adjustment programmed of 1986. The diversification of export was focused on moving the export base away from oil and the expansion of nonoil exports, especially agricultural exports cocoa increasingly accounted for the largest percentage of nonoil exports in Nigeria. In general, average figures for the period 1993 1995 show that cocoa rubber, fish and shrimps, and cotton were the major agricultural commodities being export from Nigeria.
However, between 1962 and 1968, Nigeria major foreign exchange earner was the agricultural sector. While the period 1976 1978 and 19781980 was characterized with restrictive trade policies. Such policies are as follows.
 General Ban on nonessential imports, especially food imports
 Tariff increases on some items
 New duties on certain items not hitherto taxed.
 Imposition of compulsory advance deposit on some classes of imports.
 Industrial raw materials which were previously under open general license were placed under specific import license.
 Export bans were imposed on certain items
 Export tariffs were revived upward for some other items.
 Centralized marketing of agricultural products was reinforced through the formation of commodity Boards which handled specific crops.
The Structure of NonOil Export during the Pre and Post SAP Era
Pre Sap Era
It was observed that most contribution of the nonoil sector was from agriculture whose largest contribution was in 1998 with 92.8% and the lowest in 1981 with 19.6%. The contribution of agriculture to total export is not something to be proud of, none of the years under review made a percentage of 10%. Before the introduction of SAP that is the year within 1981 and 1985, one would examine that there was a negative growth rate in agricultural export. It is generally known that agricultural performance was particularly unsatisfactory and this tend to increase the burden of the whole economic. Many factors responsible for this, the major frequent problem of agricultural production is its high propensity to weather changes. Whenever there was unfavourable weather, output decline substantially with adverse consequences of the economy. This in itself is a symptoms of an inefficient agriculture system, which is unable the economy unlike a developed agricultural system (Noko, 2016).
Another important problem was the poor implementation of policy measures by the various government agencies. Most of the institutions involved in policies implementation were very ineffective and were not particularly oriented to the needs of majority of the small farmers. Such inadequacies were common in key institution like credit agencies, research institution, commodity board, river basin development authority and institution which handled input procurement and distribution. Many of them either did not have adequate facilities and funds or competent staff to enable them to work efficiently.
Sap Era
According to Itegbe (2015), between 1984 to September 1986, successive military administrations started giving serious consideration to the need to urgently find other methods of sourcing foreign exchange, in addition to measures adopted to conserve what was already earned. This situation arose as a result of mounting obligation on the country to settle trade arrears and for debts servicing as well as to meet current trade bills. He further stated that by 1984, Nigeria had found herself in huge foreign debts in addition to being in serious arrears in settlement of foreign trade bills mainly on irrevocable letters of credit. Thus, it became clear to policy makers in Nigeria that additional effort had to be made by the nation to earn foreign exchange. It was for this reason that the government in 1986 adopted export oriented development strategy as a major comer stone of the structural adjustment programme.
SAP involved the formulation and adoption of a comprehensive export incentive legislation known as the export incentives and miscellaneous provision decree No.18 of 1986. The provisions of this decree were subsequently strengthened by the provision of the second tier foreign exchange market (SFEM) decree No.26 of September, 1986. The introduction of the export decree and SFEM decree could be described as ‘Watershed’ in the history of nonoil export policy development in Nigeria, according to Itegbe1989, pointing out for the first time, in the history of the country, export expansion and diversification strategy became a national policy objective. The removal of all bureaucracies and additional incentives through SAP did not however make any significant impact on the volume nonoil exports. Experts and academicians in the area of export promotion have tried to figure out why after 20 years of this export policy regime there is little significant positive results.
Post Sap Era
It is in the area of agriculture export that recent policy measures have produced the most visible impact so far. The growth rate of agriculture exports grew from negative figure apart from 1992 which was 10.8, all other years were positive. The share of agriculture in nonoil also grew with an average of 74.6. The highest contribution was in 1998 with 92.8%, the agricultural export from the total exports also increased making about 4.5% within 1986 to 1988, which was an improvement of 2.5% in the pre SAP period (Noko, 2016).
Apart from the significant rise in the agricultural export noted above, the upsurge in the sharp increase in local currency prices of the sharp export product, following the large depreciation in naira exchange rate and the removal of marketing and price control after the abolition of the commodity boards. Another source of increase was the new package incentives given to the nonoil exporters.

 Empirical Literature
As the world has become a global village, exports have been considered as growthenhancing within the traditional development literature. It has been affirmed that an increase of exports could be advantageous in terms of alleviating the foreign exchange constraint, ultimately exacting a positive effect on growth. It is Cristal clear that there is not a single country that can attain economic growth and development in isolation of trade with other nation. The speed at which less Developed countries (LDCs) attains economic growth and development greatly depends on how they manage their gains from trade with other countries.
Eze et al (2017) examined the causality between agricultural sector and economic growth, as well as the impact of the sector on the growth of the Nigerian domestic economy. Cointegration test, Vector Error Correction Model (VECM) and Granger causality test were utilized in the analysis. The variables employed in the investigation include real gross domestic product (RGDP), value of agricultural output (VAO), foreign private investment (FPI) and financial development (FD). A stationarity test was conducted through the application of the Augmented DickeyFuller (ADF) stationarity test, and the result showed that all the variables except RGDP were nonstationary at level; however, the variables such as VAO, FPI and FD became stationary after first differencing. The cointegration result indicated long run equilibrium relationship among the variables under study. The VECM result on the other hand, showed that value of agricultural output (VAO) has positive and insignificant contribution to real GDP. Thus, it is estimated on average that 1% increase in the value of agricultural sector output (VAO) would lead to 1.9% increase in real GDP. Furthermore, the Pairwise Granger causality result showed that significant causality exist between the two variables, with causality running from agricultural output to RGDP. It therefore, implies that agricultural sector output contributed positively and insignificantly to the growth of Nigerian domestic economy. They recommends that government should increase its budgetary allocation on agriculture in order to boost the growth performance of the sector. Similarly, the study recommends that government should strengthen agricultural credit agencies to enable them monitor and ensure efficient disbursement of fund disbursed to farmers in the country.
Alimi and Muse (2013) examined the role of export in the economic growth process in Nigeria for a period of 39 years and discovered that economic growth and export are integrated of order one, i.e.1(1) and as well are cointegrated, indicating an existence of long run equilibrium relationship between the two. This result was achieved by employing unit root test, cointegration analysis and VAR Granger causality/Erogeneity Wald tests.
Ifeacho, Omoniyi, and Olufemi (2014), investigated the effect of nonoil export on the economic development of Nigeria. The study used per capita income as proxy for economic development and expressed it as a function of nonoil export volume, trade openness, exchange rate capital formation and inflation rate. The study applied ordinary least square estimating technique and the result show that nonoil export exhibits a significant positive relationship with per capita income. However, other variables do not have individual significant impact of economic development but jointly they can significantly influence economic development. In addition, the result shows that the coefficient of trade openness is negative thus, indicating that Nigeria might not be benefiting enough by trading with outside countries. This calls for review of trade policy of Nigeria if the positive effect of nonoil export on Nigerian economic development is to be promoted.
Kawai (2017) evaluates the impact of Nigeria’s nonoil exports as to whether they have been effective in diversifying the productive base of the Nigerian Economy from Crude oil as the major source of foreign exchange. Expectedly, attention of scholars had shifted towards nonoil exports as a remedial forth is quagmire. This study investigates the specific impact of the nonoil exports to the growth of Nigerian economy using annual data between 1980to2016. The study adopted the Phillip Perron (PP), the EngelGranger Model (EGM) for cointegration were employed in its analysis. Findings revealed a strong evidence of cointegration relationship of nonoil exports in influencing rate of change in the level of economic growth in Nigeria. The study, apart from empirically providing information that has failed to give backing to recent claims of nonoil exports led growth in Nigeria, has also make some recommendations which include government should reemphasized and strengthen industrial revolution plan with a clear strategy to develop sectoral plan so that the planned should be working sector by sector for better outcome of these sectors.
According to Ogunkola et al., (2008), in the 1960’s Nigeria’s export trade was largely dominated by nonoil products such as groundnuts, palm kernel, palm oil, cocoa, rubber, cotton, coffee, copra, beniseed and others. Other nonoil exports of significant value then were tin ore, columbite, hides, skin and cattle. Over 66% of total exports on the average were accounted for by these commodities.
As a matter of fact, cocoa was the dominant export product at that time contributing about 15% of total exports in 1970. However, oil’s dominance of the country’s export basket began in 1973/74 and was greatly magnified during the 1980s. The crux of the problem was that while oil export was growing, nonoil exports were declining making the dominance much more rapid and pervasive.
Ogbonna (2010) emphasize that the contributions of the non – sector export to the GDP is still marginal and almost insignificant. What this implies is that all the export promotion strategies adopted failed to achieve the desired results, which is to improve the performance of the sector. In her research on “the impact of export promotion policies on Nigeria’s non –oil export” using ordinary least square (OLS) regression technique she noted that there is general need for policy frame work, otherwise, the non – oil sector will continue to make less contribution to the country’s balance of payments their research work however covered the period from 1981 – 2000.
Abogan et al (2014) investigated impact of nonoil export on economic growth in Nigeria between 1980 and 2010. The study examines the significant role of nonoil export on economic growth which the previous studies might have ignored and the aggregate nonoil exports data used by them might bias their conclusions. In achieving the objectives of the study, Ordinary Least Square Methods involving Error correction mechanism, overparametization and parsimonious were adopted. In testing for the time series properties, the evidence from estimated economic models suggests that all the variables examined are stationary at first difference I(Is) using the Augmented Dickey Fuller (ADF) and PhillipsPerron. Besides, Johansen Co integration test reveals that the variables are co integrated which confirms the existence of longrun equilibrium relationship between the variables. The study reveals that the impact of nonoil export on the economic growth was moderate and not all that heartening as a unit increase in nonoil export impacted positively by 26% on the productive capacity of goods and services in Nigeria during the period. This was evident in the study that the policies on nonoil sectors during the period in Nigerian do not sufficiently encourage nonoil export, thus reduce their contributions to growth. This study therefore predicts an imminent collapse of the Nigerian nonoil sector in the nearest future if immediate remedial measures are not taken to strengthen the sector. The study among other things encourages the government to strengthen the legislative and supervisory framework of the nonoil sectors in Nigeria and diversify the economy to ensure maximum contributions from all faces of the sectors to economic growth of Nigeria.
To Ajakaiye and Fakiyesi (2009), earnings from nonoil exports, such as finished leather products, cocoa and its products, sesame seeds and manufactured products like cosmetics and toiletries, rose to about US$1.38 billion in 2007. By the end of 2008, this value rose to $1.8 billion, the highest in the country’s history.
Ozoudo (2010) also discovered using econometric method that the dominance of petroleum / crude oil in the export sector’s export. He as well recorded that the inefficient performance of the non – oil marketing of board deterred progress the non – oil sector. His research covered the period from 1991 – 2008. Ezirim et al., (2010), observed that the economy, which was largely at a rudimentary stage of development at the first half of the last century, started experiencing some structural transformation immediately after the country’s independence in 1960.
Akinole (2001) in his study, he adopted the ordinary least square (OLS) regression technique. He investigated the prospects for Nigerian petroleum, groundnut, coca and palm oil in the expanded economic commodity. He discovered that the demand for Nigeria oil by the common market countries is price elastic. But the membership of Nigeria in the organization of petroleum exporting countries, a collective bargaining organization makes the exploitation of the high price elasticity of demand unlikely. He said that there exist an effective competition between Nigeria’s
Helleiner (2002) carried out a study using the Keynesian export multiplier approach and two variants of the two – gap frame work, incorporating, and the Harrod Domar model, which shows that only a small part if total agricultural out part of the developing countries receive elaborates local processing, since the bulk is usually sent abroad. He points out that the agriculture normally better in the supply of intermediate inputs to other rectors than in the use of other intermediate inputs.
Asanebi (2007) carried out a research using linear correlation co – efficient analysis and observed that the performance of non – oil sectors exports was below expectation in aggregate terms and so, has not made significant impact on the GNP of the country, cannot sustain the country in terms of economic growth and development. He also came up with the following findings;
– That primary commodities dominates Nigeria’s basket of nonoil export
– That introduction of the structural adjustment programme (SAP) came with export promotion policy that saw some improvement in the proportion of semi – manufactures and manufactures.
– Though the performance of nonoil exports below expectation in terms of market diversification, it however, recorded some success in terms of a gradual growth in the proportion of value added exports.
Okoro (2009), in his work on the impact of non – oil export the Nigeria economy” using econometric growth without the industrial, agricultural and manufacturing sectors improving from their present state. He states that a very strong link exists between these three sectors and other sectors of the economy. His period of study covered 1995 – 2005.
Limitation of Previous Studies
It is a common knowledge that on research work exists in a competence that is devoid of flaw and lapses, but the ability to reduce or make those lapses within the limit of this research work.
However, other works (period’s studies) are limited in the area of including the contribution of oil and the performance of other developed countries in compares with less developed countries especially Nigeria.
This study therefore intends to cover these lapses by focusing on Nigeria only and the impact of non – oil export earnings on the national’s cross domestic product from the period 1986 2010.
Agenri, this work seeks to advocate the policies or measures that would boast non – oil sector to the economic growth of Nigeria which previous studies neglected.
However, it is crystal clear from the analysis so far that there are some inheritable weaknesses in many works carried earlier on export earning relationship with economic growth in Nigeria as reviewed. The reason for this may not be farfetched as majority of the works did not subject the estimate parameters to some advance econometric test, like test of stationarity of the regression data, cointegration, VEM, etc., but only make use of ordinary least square (OLS).
Hence, this work will adopt various advance econometric test to ensure the reliability of the result and also prevent spurious result.
CHAPTER THREE
RESEARCH METHODOLOGY
3.1 Research Design
This chapter focuses on the research method that will be adopted, procedures employed in data collection as well as statistical and econometric analysis of the data for the purpose of examining the role of export earning on the economic growth of Nigeria.
The methodology to be adopted is the ordinary least square (OLS) method of regression. Ordinary least square (OLS) is adopted because of its simplicity and estimates obtained from this procedure have optimal properties including linearity, Unbiasedness, minimum variance, zero mean value of the random term, etc. (Gujarati, 2004).
3.2 Model Specification
Hypothesis has earlier been stated in this study with the view of evaluating the impact of export earnings fluctuation on economic growth of Nigeria. In capturing the study, we use the neoclassical growth model, otherwise referred to as the growth accounting framework to explain the source of growth in an economy. The national accounts form the basis of the economies to be analyzed and it is used in conjunction with aggregate production function. This application has been widely used (for example, Abogan et al, 2014, Akinlo and Odusola, 2003; Obstfeld, (1994; Ifeacho, Omoniyi, and Olufemi (2014). Using a production function approach, it states that the growth rate of output (GDP) is principally determined by the following factors: The rate of growth of gross labour/or the rate of growth of its quality, multiplied by the labour income share; the rate of growth of gross capital input and/or the rate of growth of its quality, multiplied by the capital income share; and change in technology or total factor productivity (TFP). This is given is:
g = f (L, K, T) ———————————3.0
Where: g = growth of GDP; L = Labour; K= capital formation/ investment; and T = technology.
In the highest of the above, the model for this study is shown by incorporating other determinants of economic activities which include the key variables to be considered in this study. These include, NonOil export, Inflation rate and Exchange rate, Thus, the model is symbolically represented in its functional form as:
GDP = f (NOXP, INF, EXR) 3.1
Where;
GDP = Gross Domestic Product
NOXP = Nigeria NonOil Export
INF = Nigeria Inflation Rate
EXR = Exchange Rate.
The linearized model specification for the analysis is given as GDP = b_{O}+ b_{1}NOXP + b_{2 }INF + b_{3 }EXR + U_{t }3.2
Where;
b_{O }= Constant term/ parameter intercept
b_{1}, b_{2}, and b_{3 }= Coefficients of the parameters estimates.
U_{t }= Error Term
As efforts will be made to rescale the data, and ensure consistence, the data will be further differenced as expressed as follow:
D(GDP) = b_{O}+b_{1 }D(NOXP) + b_{2} D(INF) + b_{3 }D(EXR) + U_{t}
3.3 Estimation Procedure
At this level of research using time series data; the researcher estimates the model with Ordinary Least Square (OLS) method. This method is preferred to others as it is best linear unbiased estimator, minimum variance, zero mean value of the random terms, etc (koutsoyiannis,2003).
In the preliminary test, following tests shall be conducted. They include:
 Unit Root Test
 CoIntegration Test
 Error Mechanism Test (ECM)
 Granger Causality test
Unit Root Test: It is used to test for the stationary of the time series data. This involves testing of the order of integration of the individual time series under consideration. These test are initially performed at levels and then in first difference form. Three different models with varying deterministic components are considered while performing the tests. These are (1) model with an intercept which assumes that there are no linear trends in the data such that the first differenced series has zero mean (2) model with a linear trend which includes a trend stationary variable to take account of unknown exogenous growth and (3) a model which neither includes a trend nor a constant. The most popular ones are Augmented DickeyFuller (ADF) test due to Dickey and Fuller (1979, 1981).Augmented Dickey Fuller (ADF) test statistics shall be compared with the critical value at 5% level of significance. A situation whereby the ADF test statistics is greater than the critical value with consideration on absolute values, the data at the tested order will be said be stationary. Augmented DickeyFuller test relies on rejecting a null hypothesis of unit roots (the series are nonstationary in favor of the alternative hypotheses of stationary. The test conducted with and without a deterministic trend (t) for each of the series.
The general form of ADF test is estimated by the following regression:
∆ y_{t} = β_{0} + β_{1} y_{t1}+∑ β∆ y_{t} +e_{t}– 3.4
∆ y_{t} = β_{0}+ β_{1} y_{t1}+∑ β∆ y_{t} +µ_{1}+e_{t} 3.5
Where: y is a time series, t is a linear time trend, ∆ is the first difference operator, β_{0 }is a constant, n is the optimum number of lags in the dependent variable and e is the random error term.
The null hypothesis is that β_{1}=0. If the null hypothesis β_{1}=1, then we conclude that the series under consideration ∆ (y_{t}) has unit root and is therefore nonstationary.
If the ADF test fails to reject the test in levels but reject the test in first differences then the series contain one unit root and is of integrated order one 1(1). If the test fails to reject the test in level and first differences but rejects the test in second differences, then the series contains two unit root and is integrated order two 1(2). The PhillipPerron (pp) unit root test is implementing to justify the results of ADF test.
The equation thus:
∆ y_{t} = β_{0} + β_{1}y_{t 1 }+ e_{t} 3.6
Error Correction Mechanism (ECM)
The purpose of the error correction model is to indicate the speed of adjustment from the shortrun equilibrium state. However, the greater the coefficients of the error term (ECM), the higher the seed of adjustment of the model form the shortrun to the longrun equilibrium.
The ECM (p) form is written as:
∆ y_{t} = ∂+py_{t1} +∑ø ∆ y_{t 1 }+ £_{t – }3.7
Where, ∆ is the differencing operator, such that ∆ y_{t1}=y_{t}=y_{t1}
Coefficient of Multiple Determinations (R^{2}): It is used to measure the proportion of variations in the dependent variables. The higher the (R^{2}), the greater the proportion in the dependent variable as brought about by the independent variable and viceversa.
Standard Error Test (S.E): It isused to test for the reliability of the coefficient estimates.
Decision Rule:
IfS.E<^{1}/_{2 }b_{i} or pvalue is less than 5%level significance reject null hypothesis and conclude that the coefficient estimate of the parameter is statistically significant. Otherwise accept null hypothesis.
Z TEST: Itis used to test for statistical significance of individual estimate parameter. In this research, Z test is chosen because the population variance is unknown and the sample side is more than 30.
Decision Rule
If Zcal.> Z tab, or Pvalue is less than 5% level of significance reject the null hypothesis and conclude that the regression coefficient is statically significant. Otherwise accept the null hypothesis.
3.4 Source of Data
The data for this research projects obtained from the following source:
 Central Bank of Nigeria (CBN) Statically Bulletin. Volume 27, 2016
CHAPTER FOUR
PRESENTATION AND ANALYSIS OF RESULTS
Having estimated the model, the variables considered are gross domestic product (dependent variable), Nigeria Nonoil export (NOXP), Nigeria Inflation Rate (INF), and Exchange rate (EXR) will all be used as the independent variables. The result covers the period of year 1985 – 2016.
4.1 Unit Root Test
In other to test for the presence or absence of unit root in the data used for the empirical analysis, Augmented DickeyFuller (ADF) test was employed and the test result is as presented below:
TABLE 1: UNIT ROOT
Augmented Dickey Fuller Result at Level and First Difference, Trend only
Variables 
ADF @ Level 
1^{st} difference 
Critical value (5%) 
Order of integration 
Remarks 
D(GDP) 
1.200016 
5.383955 
3.557759 
I(1) 
Stationary 
D(NOXP) 
0.298585 
5.989962 
3.557759 
I(1) 
Stationary 
D(INF) 
3.048442 
5.737084 
3.557759 
I(1) 
Stationary 
D(EXR) 
0.314150 
5.665275 
3.557759 
I(1) 
Stationary 
Source: Own Computation (See Appendix)
From the table 1 above, the result revealed that none of the variables were stationary at level while at first difference all the variables become stationary given the 5% level of significance, since the absolute value of the calculated ADF exceeds the absolute value of 5% and 1% critical value of the ADF. Hence, since all the variables are not stationary at the level, cointegration analysis is justified. We there proceed to conduct the long run relationship of the variables and their short term speed of adjustment to equilibrium.
4.2 Tests for Cointegration
This test is used to test for the long run relationship between the variables; it was carried out using the augmented eagle – Granger test on the residuals under the following hypothesis:
H_{0 :} δ = 0 (Not cointegrated)
H_{1 }: δ ≠ 0 (cointegrated)
Decision Rule:
Reject H_{0 } if t*.Adf (LR) > tAdf (CV), accept if otherwise
COINTEGRATION TEST: TABLE 2
Series: GDP NOXP INF EXR 

Lags interval (in first differences): 1 to 1 

Unrestricted Cointegration Rank Test (Trace) 

Hypothesized 
Trace 
0.05 

No. of CE(s) 
Eigenvalue 
Statistic 
Critical Value 
Prob.** 
None * 
0.837726 
111.1755 
47.85613 
0.0000 
At most 1 * 
0.712347 
52.98453 
29.79707 
0.0000 
At most 2 
0.306263 
13.11250 
15.49471 
0.1107 
At most 3 
0.043145 
1.411298 
3.841466 
0.2348 
Trace test indicates 2 cointegrating eqn(s) at the 0.05 level 

* denotes rejection of the hypothesis at the 0.05 level 

**MacKinnonHaugMichelis (1999) pvalues 
Source: Own Computation (See Appendix)
From table 2 above, since the computed trace statistic i.e. (111.1755 and 52.98453) is greater than their respective TAdf . i.e. the critical value (47.85613, and 29.79707) at 5% levels of significance or since the probability value ( 0000< 0.05) are less than 5% level of significance, we reject H_{o }and conclude that there is at least two cointegrating equation and that all the variables are cointegrated. Put differently, there is a sustainable longrun relationship (i.e. steadystated path) between gross domestic products (GDP), Nigeria Nonoil export (NOXP), Nigeria Inflation Rate (INF), and Exchange rate (EXR).
The Longrun Equation Nigeria NonOil export
GDP = 3340.874 – 10.10148NOXP 22.52152INF + 120.8039EXR
(0.57639) (12.0763) (51.8030)
The result above is the coefficient of the explanatory variables which indicate the direction of strength of the relationship between explanatory variables and economic growth in the long run. The figures in the parenthesis were the asymptotic standard error. The result reveal that one million increase Nigeria Nonoil export will bring about N10101480 decrease on the gross domestic product, at the same time one million increase in Nigeria Inflation rate will bring about N22521520 decrease on gross domestic product, again one million increase in exchange rate will bring about N1208039 increase on the gross domestic product, all other factors affecting gross domestic product remaining constant.
Vector Error Correction Mechanism
The existence of a long run cointegrating equilibrium provides for shortterm fluctuations. In order to strengthen out or absolve these fluctuations, an attempt was made to apply the Vector Error Correction Mechanism (VECM). As noted, the VECM is meant to tie the shortrun dynamics of the cointegrating equations to their longrun static dispositions. Table 4 below shows the Vector error correction mechanism result.
TABLE 3
VECTOR ERROR CORRECTION MECHANISM RESULT
Sample (adjusted): 1986 2016 

Included observations: 29 after adjustments 

Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 
Remark 
C 
5889.562 
599.987 
9.81615 
0.0000 
Reject 
D(GDP(2)) 
2.315599 
0.32156 
7.20115 
0.0000 
Reject 
D(NOXP(1)) 
17.81252 
2.30280 
11.0615 
0.0000 
Reject 
D(INF(1)) 
2.601630 
19.4607 
0.13369 
0.8971 
Accept 
D(EXR(1)) 
4.514974 
1.36332 
3.31176 
0.0000 
Reject 
VECM(1) 
2.181188 
0.18680 
11.6763 
0.0000 
Reject 
Source: Own Computation (See Appendix)
R^{2 }= 0.935255 DW = 1.90
F (3, 25) = 33.70, F*(Pvalue) = 0.0000
From the result the coefficient of vector error correction term is 2.181188. This shows that 218% of the errors in the short run are corrected each year. Thus, the coefficient captures the speed for adjustment at which the shortrun of GDP ties with its longrun. The result is significant since the coefficient of multiple determination (0.9353) is greater than zero. And also, the vector error correction coefficient has negative sign which indicate that there is feedback from the previous year’s disequilibrium or that the explanatory variables have power to correct the disequilibrium each year.
Coefficient of Multiple determinations: Also the computed R^{2 }value (0.9353) of which is the coefficient of multiple determinations indicates that our model satisfies the requirement for goodness of fit. The value showed that 93.53% the variation in the gross domestic product (GDP) are explained by the variation of the explanatory variables namely; Nigeria Nonoil export (NOXP), Nigeria Inflation rate (INF), and Exchange Rate (EXR) while the remaining 6.47% is explained by variable not included in the model.
Ttest: A mere observation of the individual’s parameters will reveal that all the variables used in the regression were statistically significant at 5% level of significance, since their Pvalue is less than the 5% level of significance i.e.(0.0000 < 0.05), except for that of inflation rate.
Ftest: Furthermore, the joint influence of the explanatory variables on the dependent variable is statistically significant. This is also confirmed by the Fprobability which is statistically zero i.e. the Pvalue of Fstatistics is less than 5%
DurbinWatson Test: At the same time the DurbinWatson is 1.90 approximately. Using 5% level of significance, 3 explanatory variables and 34 observations, the tabulated DurbinWatson statistics for lower and upper limit are 1.23 and 1.67, since the calculated DurbinWatson is greater than upper limit of DurbinWatson but less than 4du (2.33), we conclude that there is no evidence of first order serial correlation.
This was further confirmed by the LM serial correlation test. The result is presented below.
VEC Residual Serial Correlation LM Tests 

Lags 
LMStat 
Prob 
1 
10.01800 
0.8657 
2 
19.07625 
0.2647 
3 
14.75249 
0.5428 
Probs from chisquare with 16 df. 
Given the optimal lag length, we accept the null hypothesis of no serial correlation. The result clearly revealed the absence of no serial correlation as the LM probability value at second lag level (0.2647) is greater than 5% level of significance. The implication is that the result can be fully relied on to make sound policies.
4.3 Test of Hypothesis
Hypothesis I: The main objective of this study is to examine the impact of Nonoil export on Nigeria economic growth. With respect to this, the null hypothesis and alternative hypothesis are stated as fellows;
H_{O}: Nonoil Export has no significant impact on Economic Growth of Nigeria.
H_{1}: Nonoil Export has significant impact on Economic Growth of Nigeria.
F Test: Is employed in testing the hypothesis. This test will help to capture the joint influence of the explanatory variables on the dependent variable.
Decision Rule;
If Fcal. > Ftab reject the null hypothesis or if the Pvalue is less than 5% level of significance, otherwise accept the null hypothesis. Using 5% level of significance at 4 and 29 degree of freedom, the tabulated Fvalue is 2.76. Since, the calculated Fvalue (65) is greater than the tabulated Fvalue at 5% level of significance; we reject the null hypothesis and conclude that Nonoil export has significant impact on Economic Growth of Nigeria within the sample period.
Hypothesis II: The second objective of study is to determine the longrun relationship between Nonoil export and economic growth in Nigeria. In testing this, cointegration test was employed to determine the nature of the relationship. The null and alternative hypothesis is presented below;
H_{0}: There is no longrun relationship between Nonoil export and economic growth in Nigeria.
H_{1}: There is longrun relationship between Nonoil export and economic growth in Nigeria.
Decision Rule: Reject H_{0 } if t*.Adf (LR) > tAdf (CV), accept if otherwise
Reject H_{0 } if t*.Adf (LR) > tAdf (CV), accept if otherwise
From data in table 2, the computed trace statistic i.e. . (111.1755 and 52.98453) is greater than their respective TAdf . i.e. the critical value (47.85613, and 29.79707) at 5% levels of significance or since the probability value ( 0000< 0.05) are less than 5% level of significance, we reject H_{o }and conclude that there is at least two cointegrating equation and that all the variables are cointegrated. Put differently, there is a sustainable longrun relationship (i.e. steadystated path) between gross domestic products (GDP), Nigeria Nonoil export (NOXP), Nigeria Inflation Rate (INF), and Exchange rate (EXR).
4.4 Implication of the Result
Economic theory imposes a restriction on the signs and magnitudes of economic relationships. In view of this, the coefficients of the explanatory variables in the estimated model presented above all conform to the a priori expectations except for that of NOXP as analyzed below.
From the regression result presented in table 4 other factors (affecting: GDP) remaining constant, the researcher deduced as follows:
As Nigeria NonOil Export (NOXP) increases by, say, one percent, Gross Domestic Product (GDP) on the average decreases by 17.8 percent. As Nigeria Inflation Rate (INF) increases by, say, one percent, Gross Domestic Product (GDP) on the average increases by 26 percent. And as Exchange rate (EXR) increases by, say one naira Gross Domestic Product (GDP) on the average increases by N451497400.
From the analysis above, Nigeria Nonoil export has significant impact on Nigeria economic growth within the sample period. The reason for this may not be far from the fact that oil export revenue contribute to over 80% of Nigeria revenue. It should be noted that Nonoil export has negative relationship with economic growth both in the short run and longrun. The reason may not be farfetched as earning from the nonoil export are often not properly utilized.
Another reason for this negative trends may be due to poor investment in the country, high inflation and exchange rate making the export not profitable. A conducive business environment should encourage business sectors to thrive.
CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATION
5.1 Summary of findings
The paper investigated the impact of NonOil Export on economic growth in Nigeria from1985 to 2016. Vector error Correction Mechanism was used to estimate the regression result. Cointegration test and Unit root test was also conducted to determine the stationarity and longrun relationship between the variables.
The result of the Cointegration test revealed that there is a sustainable longrun relationship (i.e. steadystated path) between gross domestic product (GDP) and the explanatory variables (NOXP, INF, and EXR).
The Vector error Correction Mechanism result indicates that the coefficient of error correction term is 2.18. This revealed that 218% of the errors in the short run are corrected each year. Thus, the coefficient captures the speed for adjustment at which the shortrun of GDP ties with its longrun dynamics.
The Unit Root Test result revealed that none of the variables used in this research work was stationary at level. But after differencing the variables all of them became at first difference given the 5% and 1% level of significance. Hence, the result of the regression can be fully relied on to make policy analysis and recommendation.
The entire regression plane was statistically significant; this means that the joint influence of the explanatory variables (NOXP, INF, and EXR), on the dependent variable (GDP) is statistically significant.
The result of the coefficient of multiple determination indicates that our model satisfies the requirement for goodness of fit. The value shows that 93.53% the variation in the gross domestic product (GDP) are explained by the variation of the explanatory variables namely; Nigeria NonOil Export (NOXP), Nigeria Inflation Rate (INF), and Exchange rate (EXR) while the remaining 6.47% is explained by variable not included in the model.
5.2 Conclusion
The empirical research reveals that NonOil Export captured by changes in Nigeria NonOil Export (NOXP), Nigeria Inflation Rate (INF), and Exchange rate (EXR) had significant influence on Nigeria economic growth during the period under review. The result showed that NonOil export changes have negative relationship in the shortrun as well as longrun with economic growth in Nigeria.
The implication is that Nigeria NonOil Export has not been fully harnessed and have been grossly neglected by the successive government such that instead of NonOil export to be an agent of economic growth and development in Nigeria has become agent of economic instability in the country.
Nigeria needs to improve her trade policies with the rest of the world for the country to stabilize balance of payment equilibrium and sustained level of economic growth. Attention should be paid on how funds realized from NonOil export are utilized to ensure sustainable economic growth in the country. The negative sign of the VECM indicates that the dependent variable has power to adjust to short term fluctuations of the explanatory variables in the longrun. This sign is necessary giving the inconsistent nature of export earning in Nigeria, which are often distorted by the socialeconomic, political condition, regional policies and prevailing economic condition in the country.
5.3 Policy Recommendation
The research work recommends that for Nigeria industrial sector to take substantial benefits of broad participation in globalization, the following conditions need to be fulfilled.
 The Federal Government of Nigeria should revamp both local industries and agriculture through subsidies, concessions, uninterrupted power supply, technical assistance, improving security of lives and properties and the creation of enabling business operating environment.
 Also sound macroeconomic policies are needed to reinforce the globalization exercise for a better result. The positive sign is an indicator that Nigeria is benefitting from globalization; this could be a product of the oil export in Nigeria which makes Nigeria to enjoy a favourable balance of payment.
 Encouragement of Export Promotion: The government should endeavor to support various export promotion programmes and institutions. This could be achieved by encouraging financial institutions, both formal and informal, to make loans available at reduced rates of interest for investors as to increase the level of investment in this country.
 Reduction or Removal of Import Tariffs: Tariffs paid on imports of equipment necessary to boost nonoil production in Nigeria are so much that productions are averse to risk their resources. So there should be a downward review of tariff/tax structures to reduce the cost of production in Nigeria.
 Nigeria must look beyond the monoproduct type of business (oil sector) and research into other sectors for new products of international standard.
 The Federal Ministries of Commerce and Industries (FMCI) should focus more attention on the development of the home industry with a view to increasing the county’s share of nonoil trade.
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Usman, A. O. (2010). Nonoil export determinants and economic growth in Nigeria (19852008). European Journal of Business and Management Sciences, 3(3), 124.
APPENDIX I
REGRESSION DATA
YEARS 
NOXP (N’Billion) 
INF 
GDP N’Billion) 
EXR (N’$) 
1985 
7.500000 
3.226000 
134.5900 
0.629900 
1986 
5.600000 
6.250000 
134.6000 
0.615900 
1987 
16.80000 
11.76500 
193.1300 
0.626500 
1988 
20.40000 
34.29100 
263.2900 
0.646600 
1989 
29.10000 
49.02000 
382.2600 
0.606000 
1990 
42.90000 
7.895000 
472.6500 
0.595700 
1991 
86.40000 
12.19500 
545.6700 
0.546400 
1992 
127.8000 
44.56500 
875.3400 
0.610000 
1993 
129.5000 
57.14300 
1089.680 
0.672900 
1994 
125.8000 
57.41600 
1399.700 
0.724100 
1995 
622.4000 
72.72900 
2907.360 
0.764900 
1996 
423.8000 
29.29200 
4032.300 
0.893800 
1997 
708.0000 
10.67300 
4189.250 
2.020600 
1998 
695.6000 
7.862000 
3989.450 
4.017900 
1999 
670.3000 
6.618000 
4679.210 
4.536700 
2000 
789.0000 
6.938000 
6713.570 
7.391600 
2001 
1149.100 
18.86900 
6895.200 
8.037800 
2002 
1245.700 
12.88300 
7795.760 
9.909500 
2003 
1776.100 
14.03300 
9913.520 
17.29840 
2004 
1782.200 
15.00100 
11411.07 
22.05110 
2005 
2109.500 
17.85600 
14610.88 
21.88610 
2006 
2531.400 
8.218000 
18564.59 
21.88610 
2007 
3343.000 
5.413000 
20657.32 
21.88610 
2008 
4803.500 
11.88100 
24296.33 
21.88610 
2009 
4912.800 
12.54300 
24794.24 
21.88610 
2010 
7117.800 
13.72000 
54612.26 
92.69340 
2011 
8865.800 
10.80000 
62980.40 
102.1052 
2012 
7581.600 
9.200000 
71713.94 
111.9433 
2013 
8140.200 
7.900000 
80092.56 
120.9702 
2014 
9277.300 
8.700000 
89043.62 
129.3565 
2015 
9527.800 
9.200000 
94,144.96 
197.4566 
2016 
10134.68 
16.85000 
101,453.13 
253.8800 
SOURCE: CENTRAL BANK OF NIGERIA STATTISTICAL BULLETINE, VOL 26
APPENDIX II
REGRESSION RESULT
UNIT ROOT TEST
GDP AT LEVEL
Null Hypothesis: GDP has a unit root 

Exogenous: Constant, Linear Trend 

Lag Length: 0 (Fixed) 

tStatistic 
Prob.* 

Augmented DickeyFuller test statistic 
1.200016 
0.9999 

Test critical values: 
1% level 
4.262735 

5% level 
3.552973 

10% level 
3.209642 

*MacKinnon (1996) onesided pvalues. 

Augmented DickeyFuller Test Equation 

Dependent Variable: D(GDP) 

Method: Least Squares 

Date: 11/10/18 Time: 22:11 

Sample (adjusted): 1985 2016 

Included observations: 32 after adjustments 

Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 
GDP(1) 
0.070671 
0.058892 
1.200016 
0.2395 
C 
1761.044 
1912.265 
0.920921 
0.3644 
@TREND(“1985”) 
206.6215 
133.0712 
1.552714 
0.1310 
Rsquared 
0.353777 
Mean dependent var 
2695.433 

Adjusted Rsquared 
0.310695 
S.D. dependent var 
5606.351 

S.E. of regression 
4654.638 
Akaike info criterion 
19.81562 

Sum squared resid 
6.50E+08 
Schwarz criterion 
19.95167 

Log likelihood 
323.9578 
HannanQuinn criter. 
19.86140 

Fstatistic 
8.211798 
DurbinWatson stat 
2.225206 

Prob(Fstatistic) 
0.001431 

GDP AT FIRST DIFFERENCE
Null Hypothesis: D(GDP) has a unit root 

Exogenous: Constant, Linear Trend 

Lag Length: 0 (Fixed) 

tStatistic 
Prob.* 

Augmented DickeyFuller test statistic 
5.383955 
0.0006 

Test critical values: 
1% level 
4.273277 

5% level 
3.557759 

10% level 
3.212361 

*MacKinnon (1996) onesided pvalues. 

Augmented DickeyFuller Test Equation 

Dependent Variable: D(GDP,2) 

Method: Least Squares 

Date: 11/10/18 Time: 22:12 

Sample (adjusted): 1986 2016 

Included observations: 30 after adjustments 

Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 
D(GDP(1)) 
0.995018 
0.184812 
5.383955 
0.0000 
C 
3242.399 
1916.922 
1.691462 
0.1015 
@TREND(“1985”) 
343.3944 
109.9437 
3.123365 
0.0040 
Rsquared 
0.500359 
Mean dependent var 
279.5119 

Adjusted Rsquared 
0.465901 
S.D. dependent var 
6594.710 

S.E. of regression 
4819.552 
Akaike info criterion 
19.88781 

Sum squared resid 
6.74E+08 
Schwarz criterion 
20.02522 

Log likelihood 
315.2050 
HannanQuinn criter. 
19.93336 

Fstatistic 
14.52082 
DurbinWatson stat 
2.012503 

Prob(Fstatistic) 
0.000043 

NOXP AT LEVEL
Null Hypothesis: NOXP has a unit root 

Exogenous: Constant, Linear Trend 

Lag Length: 0 (Fixed) 

tStatistic 
Prob.* 

Augmented DickeyFuller test statistic 
0.298585 
0.9873 

Test critical values: 
1% level 
4.262735 

5% level 
3.552973 

10% level 
3.209642 

*MacKinnon (1996) onesided pvalues. 

Augmented DickeyFuller Test Equation 

Dependent Variable: D(NOXP) 

Method: Least Squares 

Date: 11/10/18 Time: 22:12 

Sample (adjusted): 1986 2016 

Included observations: 30 after adjustments 

Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 
NOXP(1) 
0.020180 
0.067585 
0.298585 
0.7673 
C 
264.7531 
245.9221 
1.076573 
0.2903 
@TREND(“1985”) 
34.24210 
18.55070 
1.845866 
0.0748 
Rsquared 
0.213564 
Mean dependent var 
280.7333 

Adjusted Rsquared 
0.161135 
S.D. dependent var 
624.1469 

S.E. of regression 
571.6535 
Akaike info criterion 
15.62145 

Sum squared resid 
9803632. 
Schwarz criterion 
15.75750 

Log likelihood 
254.7539 
HannanQuinn criter. 
15.66723 

Fstatistic 
4.073391 
DurbinWatson stat 
2.167308 

Prob(Fstatistic) 
0.027224 

NOXP AT FIRST DIFFERENCE
Null Hypothesis: D(NOXP) has a unit root 

Exogenous: Constant, Linear Trend 

Lag Length: 0 (Fixed) 

tStatistic 
Prob.* 

Augmented DickeyFuller test statistic 
5.989962 
0.0001 

Test critical values: 
1% level 
4.273277 

5% level 
3.557759 

10% level 
3.212361 

*MacKinnon (1996) onesided pvalues. 

Augmented DickeyFuller Test Equation 

Dependent Variable: D(NOXP,2) 

Method: Least Squares 

Date: 11/10/18 Time: 22:14 

Sample (adjusted): 1986 2016 

Included observations: 29 after adjustments 

Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 
D(NOXP(1)) 
1.112378 
0.185707 
5.989962 
0.0000 
C 
275.1014 
222.6929 
1.235340 
0.2266 
@TREND(“1985”) 
33.89849 
12.16683 
2.786139 
0.0093 
Rsquared 
0.553695 
Mean dependent var 
35.60937 

Adjusted Rsquared 
0.522915 
S.D. dependent var 
835.9846 

S.E. of regression 
577.4255 
Akaike info criterion 
15.64410 

Sum squared resid 
9669185. 
Schwarz criterion 
15.78151 

Log likelihood 
247.3055 
HannanQuinn criter. 
15.68964 

Fstatistic 
17.98900 
DurbinWatson stat 
2.062313 

Prob(Fstatistic) 
0.000008 

INF AT LEVEL
Null Hypothesis: INF has a unit root 

Exogenous: Constant, Linear Trend 

Lag Length: 0 (Fixed) 

tStatistic 
Prob.* 

Augmented DickeyFuller test statistic 
3.048442 
0.1351 

Test critical values: 
1% level 
4.262735 

5% level 
3.552973 

10% level 
3.209642 

*MacKinnon (1996) onesided pvalues. 

Augmented DickeyFuller Test Equation 

Dependent Variable: D(INF) 

Method: Least Squares 

Date: 11/10/18 Time: 22:15 

Sample (adjusted): 1986 2016 

Included observations: 30 after adjustments 

Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 
INF(1) 
0.469178 
0.153907 
3.048442 
0.0048 
C 
14.36339 
6.963617 
2.062633 
0.0479 
@TREND(“1985”) 
0.309286 
0.284782 
1.086047 
0.2861 
Rsquared 
0.237413 
Mean dependent var 
0.360606 

Adjusted Rsquared 
0.186573 
S.D. dependent var 
16.53804 

S.E. of regression 
14.91569 
Akaike info criterion 
8.329212 

Sum squared resid 
6674.332 
Schwarz criterion 
8.465258 

Log likelihood 
134.4320 
HannanQuinn criter. 
8.374987 

Fstatistic 
4.669875 
DurbinWatson stat 
1.712869 

Prob(Fstatistic) 
0.017153 

INF AT FIRST DIFFERENCE
Null Hypothesis: D(INF) has a unit root 

Exogenous: Constant, Linear Trend 

Lag Length: 0 (Fixed) 

tStatistic 
Prob.* 

Augmented DickeyFuller test statistic 
5.737084 
0.0003 

Test critical values: 
1% level 
4.273277 

5% level 
3.557759 

10% level 
3.212361 

*MacKinnon (1996) onesided pvalues. 

Augmented DickeyFuller Test Equation 

Dependent Variable: D(INF,2) 

Method: Least Squares 

Date: 11/10/18 Time: 22:15 

Sample (adjusted): 1986 2016 

Included observations: 30 after adjustments 

Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 
D(INF(1)) 
1.048137 
0.182695 
5.737084 
0.0000 
C 
2.780680 
6.471047 
0.429711 
0.6706 
@TREND(“1985”) 
0.154956 
0.327212 
0.473563 
0.6394 
Rsquared 
0.532060 
Mean dependent var 
0.484937 

Adjusted Rsquared 
0.499788 
S.D. dependent var 
24.14496 

S.E. of regression 
17.07668 
Akaike info criterion 
8.602365 

Sum squared resid 
8456.780 
Schwarz criterion 
8.739778 

Log likelihood 
134.6378 
HannanQuinn criter. 
8.647913 

Fstatistic 
16.48687 
DurbinWatson stat 
1.959848 

Prob(Fstatistic) 
0.000017 

EXR AT LEVEL
Null Hypothesis: EXR has a unit root 

Exogenous: Constant, Linear Trend 

Lag Length: 0 (Fixed) 

tStatistic 
Prob.* 

Augmented DickeyFuller test statistic 
0.314150 
0.9868 

Test critical values: 
1% level 
4.262735 

5% level 
3.552973 

10% level 
3.209642 

*MacKinnon (1996) onesided pvalues. 

Augmented DickeyFuller Test Equation 

Dependent Variable: D(EXR) 

Method: Least Squares 

Date: 11/10/18 Time: 22:09 

Sample (adjusted): 1985 2016 

Included observations: 30 after adjustments 

Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 
EXR(1) 
0.027459 
0.087408 
0.314150 
0.7556 
C 
5.062242 
4.735090 
1.069091 
0.2936 
@TREND(“1985”) 
0.557585 
0.310947 
1.793182 
0.0830 
Rsquared 
0.146661 
Mean dependent var 
3.898248 

Adjusted Rsquared 
0.089772 
S.D. dependent var 
12.43009 

S.E. of regression 
11.85904 
Akaike info criterion 
7.870565 

Sum squared resid 
4219.101 
Schwarz criterion 
8.006611 

Log likelihood 
126.8643 
HannanQuinn criter. 
7.916341 

Fstatistic 
2.578008 
DurbinWatson stat 
2.043392 

Prob(Fstatistic) 
0.092645 

EXR AT FIRST DIFFERENCE
Null Hypothesis: D(EXR) has a unit root 

Exogenous: Constant, Linear Trend 

Lag Length: 0 (Fixed) 

tStatistic 
Prob.* 

Augmented DickeyFuller test statistic 
5.665275 
0.0003 

Test critical values: 
1% level 
4.273277 

5% level 
3.557759 

10% level 
3.212361 

*MacKinnon (1996) onesided pvalues. 

Augmented DickeyFuller Test Equation 

Dependent Variable: D(EXR,2) 

Method: Least Squares 

Date: 11/10/18 Time: 22:16 

Sample (adjusted): 1985 2016 

Included observations: 30 after adjustments 

Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 
D(EXR(1)) 
1.050113 
0.185360 
5.665275 
0.0000 
C 
5.179592 
4.659744 
1.111561 
0.2755 
@TREND(“1985”) 
0.536490 
0.249016 
2.154436 
0.0396 
Rsquared 
0.525334 
Mean dependent var 
0.262659 

Adjusted Rsquared 
0.492598 
S.D. dependent var 
16.90536 

S.E. of regression 
12.04205 
Akaike info criterion 
7.903747 

Sum squared resid 
4205.320 
Schwarz criterion 
8.041160 

Log likelihood 
123.4599 
HannanQuinn criter. 
7.949295 

Fstatistic 
16.04778 
DurbinWatson stat 
2.008015 

Prob(Fstatistic) 
0.000020 

COINTEGRATION TEST
Date: 11/10/18 Time: 22:17 

Sample (adjusted): 1986 2016 

Included observations: 30 after adjustments 

Trend assumption: Linear deterministic trend 

Series: GDP NOXP INF EXR 

Lags interval (in first differences): 1 to 1 

Unrestricted Cointegration Rank Test (Trace) 

Hypothesized 
Trace 
0.05 

No. of CE(s) 
Eigenvalue 
Statistic 
Critical Value 
Prob.** 
None * 
0.837726 
111.1755 
47.85613 
0.0000 
At most 1 * 
0.712347 
52.98453 
29.79707 
0.0000 
At most 2 
0.306263 
13.11250 
15.49471 
0.1107 
At most 3 
0.043145 
1.411298 
3.841466 
0.2348 
Trace test indicates 2 cointegrating eqn(s) at the 0.05 level 

* denotes rejection of the hypothesis at the 0.05 level 

**MacKinnonHaugMichelis (1999) pvalues 

Unrestricted Cointegration Rank Test (Maximum Eigenvalue) 

Hypothesized 
MaxEigen 
0.05 

No. of CE(s) 
Eigenvalue 
Statistic 
Critical Value 
Prob.** 
None * 
0.837726 
58.19099 
27.58434 
0.0000 
At most 1 * 
0.712347 
39.87203 
21.13162 
0.0001 
At most 2 
0.306263 
11.70120 
14.26460 
0.1224 
At most 3 
0.043145 
1.411298 
3.841466 
0.2348 
Maxeigenvalue test indicates 2 cointegrating eqn(s) at the 0.05 level 

* denotes rejection of the hypothesis at the 0.05 level 

**MacKinnonHaugMichelis (1999) pvalues 

Unrestricted Cointegrating Coefficients (normalized by b’*S11*b=I): 

GDP 
NOXP 
INF 
EXR 

0.000475 
0.000914 
0.010995 
0.263683 

4.22E05 
0.001475 
0.013597 
0.040200 

1.93E05 
0.000294 
0.066503 
0.007567 

0.000466 
0.001862 
0.012421 
0.111979 

Unrestricted Adjustment Coefficients (alpha): 

D(GDP) 
513.6431 
4305.178 
208.2857 
128.2741 
D(NOXP) 
381.3364 
218.4302 
43.78510 
52.66628 
D(INF) 
0.020062 
0.467248 
8.912722 
0.302783 
D(EXR) 
2.273915 
10.16434 
0.268121 
0.115379 
1 Cointegrating Equation(s): 
Log likelihood 
737.6035 

Normalized cointegrating coefficients (standard error in parentheses) 

GDP 
NOXP 
INF 
EXR 

1.000000 
1.925712 
23.15976 
555.4117 

(0.27854) 
(12.6994) 
(21.8735) 

Adjustment coefficients (standard error in parentheses) 

D(GDP) 
0.243853 

(0.48014) 

D(NOXP) 
0.181040 

(0.03790) 

D(INF) 
9.52E06 

(0.00151) 

D(EXR) 
0.001080 

(0.00113) 

2 Cointegrating Equation(s): 
Log likelihood 
717.6675 

Normalized cointegrating coefficients (standard error in parentheses) 

GDP 
NOXP 
INF 
EXR 

1.000000 
0.000000 
5.727406 
643.3336 

(17.6284) 
(17.4226) 

0.000000 
1.000000 
9.052417 
45.65680 

(6.11549) 
(6.04410) 

Adjustment coefficients (standard error in parentheses) 

D(GDP) 
0.062108 
5.881917 

(0.26535) 
(0.96627) 

D(NOXP) 
0.171819 
0.026377 

(0.03211) 
(0.11692) 

D(INF) 
1.02E05 
0.000671 

(0.00151) 
(0.00551) 

D(EXR) 
0.000650 
0.012917 

(0.00061) 
(0.00224) 

3 Cointegrating Equation(s): 
Log likelihood 
711.8169 

Normalized cointegrating coefficients (standard error in parentheses) 

GDP 
NOXP 
INF 
EXR 

1.000000 
0.000000 
0.000000 
643.9219 

(16.5845) 

0.000000 
1.000000 
0.000000 
44.72685 

(6.53809) 

0.000000 
0.000000 
1.000000 
0.102730 

(0.29825) 

Adjustment coefficients (standard error in parentheses) 

D(GDP) 
0.066125 
5.820666 
39.03823 

(0.26485) 
(0.97740) 
(38.1793) 

D(NOXP) 
0.172663 
0.039253 
4.134689 

(0.03187) 
(0.11762) 
(4.59436) 

D(INF) 
0.000182 
0.003292 
0.598855 

(0.00126) 
(0.00466) 
(0.18201) 

D(EXR) 
0.000656 
0.012838 
0.095372 

(0.00061) 
(0.00227) 
(0.08863) 

VECM RESULT
Vector Error Correction Estimates 

Date: 11/10/18 Time: 22:18 

Sample (adjusted): 1987 2016 

Included observations: 29 after adjustments 

Standard errors in ( ) & tstatistics in [ ] 

Cointegrating Eq: 
CointEq1 

GDP(1) 
1.000000 

NOXP(1) 
10.10148 

(0.57639) 

[17.5255] 

INF(1) 
22.52152 

(12.0763) 

[1.86493] 

EXR(1) 
120.8039 

(51.8030) 

[ 2.33199] 

C 
3340.874 

Error Correction: 
D(GDP) 
D(NOXP) 
D(INF) 
D(EXR) 
CointEq1 
2.181188 
0.143267 
0.000189 
0.004917 
(0.18680) 
(0.02980) 
(0.00196) 
(0.00052) 

[11.6763] 
[4.80820] 
[ 0.09665] 
[9.42695] 

D(GDP(1)) 
0.686035 
0.060126 
0.001850 
0.000207 
(0.36799) 
(0.05870) 
(0.00385) 
(0.00103) 

[ 1.86428] 
[ 1.02435] 
[0.48025] 
[0.20162] 

D(GDP(2)) 
2.315599 
0.261510 
0.000972 
0.005379 
(0.32156) 
(0.05129) 
(0.00337) 
(0.00090) 

[ 7.20115] 
[ 5.09856] 
[ 0.28861] 
[ 5.99193] 

D(NOXP(1)) 
25.47231 
1.569795 
0.000393 
0.055732 
(2.30280) 
(0.36731) 
(0.02411) 
(0.00643) 

[11.0615] 
[4.27374] 
[0.01630] 
[8.66856] 

D(NOXP(2)) 
17.81252 
1.261995 
0.001403 
0.038382 
(1.91014) 
(0.30468) 
(0.02000) 
(0.00533) 

[9.32522] 
[4.14202] 
[ 0.07014] 
[7.19726] 

D(INF(1)) 
2.601630 
2.106076 
0.019720 
0.010967 
(19.4607) 
(3.10411) 
(0.20374) 
(0.05433) 

[ 0.13369] 
[0.67848] 
[0.09679] 
[0.20184] 

D(INF(2)) 
13.42127 
2.334033 
0.342961 
0.037052 
(19.1192) 
(3.04963) 
(0.20016) 
(0.05338) 

[ 0.70198] 
[ 0.76535] 
[1.71341] 
[ 0.69414] 

D(EXR(1)) 
451.4974 
35.23067 
0.750173 
1.572958 
(136.332) 
(21.7458) 
(1.42728) 
(0.38062) 

[ 3.31176] 
[ 1.62011] 
[ 0.52560] 
[ 4.13259] 

D(EXR(2)) 
239.3876 
90.97295 
0.318327 
0.714159 
(136.810) 
(21.8221) 
(1.43229) 
(0.38196) 

[1.74978] 
[4.16884] 
[0.22225] 
[1.86973] 

C 
5889.562 
450.1588 
0.039259 
12.71818 
(599.987) 
(95.7018) 
(6.28138) 
(1.67510) 

[ 9.81615] 
[ 4.70376] 
[0.00625] 
[ 7.59250] 

Rsquared 
0.935255 
0.867312 
0.149829 
0.898230 
Adj. Rsquared 
0.907507 
0.810445 
0.214530 
0.854615 
Sum sq. resids 
64124948 
1631489. 
7028.363 
499.8319 
S.E. equation 
1747.446 
278.7292 
18.29437 
4.878680 
Fstatistic 
33.70535 
15.25173 
0.411212 
20.59426 
Log likelihood 
269.3936 
212.4879 
128.0548 
87.08151 
Akaike AIC 
18.02540 
14.35405 
8.906760 
6.263323 
Schwarz SC 
18.48797 
14.81663 
9.369337 
6.725899 
Mean dependent 
2868.825 
298.9774 
0.436194 
4.151568 
S.D. dependent 
5745.779 
640.1992 
16.60020 
12.79506 
Determinant resid covariance (dof adj.) 
2.61E+14 

Determinant resid covariance 
5.50E+13 

Log likelihood 
666.3402 

Akaike information criterion 
45.82840 

Schwarz criterion 
47.86374 

SYSTEM EQUATION
System: UNTITLED 

Estimation Method: Least Squares 

Date: 11/10/18 Time: 22:19 

Sample: 1988 2016 

Included observations: 28 

Total system (balanced) observations 124 

Coefficient 
Std. Error 
tStatistic 
Prob. 

C(1) 
2.182380 
0.186941 
11.67418 
0.0000 
C(2) 
0.684889 
0.368021 
1.861005 
0.0662 
C(3) 
2.314353 
0.321650 
7.195245 
0.0000 
C(4) 
25.46779 
2.302949 
11.05877 
0.0000 
C(5) 
17.81158 
1.910506 
9.322967 
0.0000 
C(6) 
2.525136 
19.46359 
0.129736 
0.8971 
C(7) 
13.46233 
19.12049 
0.704079 
0.4833 
C(8) 
452.8883 
136.4231 
3.319733 
0.0013 
C(9) 
239.3099 
136.8592 
1.748584 
0.0840 
C(10) 
5887.669 
600.0152 
9.812534 
0.0000 
C(11) 
0.143267 
0.029797 
4.808196 
0.0000 
C(12) 
0.060126 
0.058697 
1.024348 
0.3086 
C(13) 
0.261510 
0.051291 
5.098557 
0.0000 
C(14) 
1.569795 
0.367311 
4.273745 
0.0001 
C(15) 
1.261995 
0.304681 
4.142024 
0.0001 
C(16) 
2.106076 
3.104108 
0.678480 
0.4993 
C(17) 
2.334033 
3.049632 
0.765349 
0.4462 
C(18) 
35.23067 
21.74579 
1.620115 
0.1090 
C(19) 
90.97295 
21.82214 
4.168838 
0.0001 
C(20) 
450.1588 
95.70183 
4.703764 
0.0000 
C(21) 
0.000189 
0.001956 
0.096654 
0.9232 
C(22) 
0.001850 
0.003853 
0.480253 
0.6323 
C(23) 
0.000972 
0.003366 
0.288606 
0.7736 
C(24) 
0.000393 
0.024108 
0.016300 
0.9870 
C(25) 
0.001403 
0.019998 
0.070138 
0.9443 
C(26) 
0.019720 
0.203738 
0.096789 
0.9231 
C(27) 
0.342961 
0.200162 
1.713412 
0.0903 
C(28) 
0.750173 
1.427283 
0.525595 
0.6006 
C(29) 
0.318327 
1.432294 
0.222249 
0.8247 
C(30) 
0.039259 
6.281383 
0.006250 
0.9950 
C(31) 
0.004917 
0.000522 
9.426955 
0.0000 
C(32) 
0.000207 
0.001027 
0.201619 
0.8407 
C(33) 
0.005379 
0.000898 
5.991933 
0.0000 
C(34) 
0.055732 
0.006429 
8.668563 
0.0000 
C(35) 
0.038382 
0.005333 
7.197265 
0.0000 
C(36) 
0.010967 
0.054332 
0.201843 
0.8405 
C(37) 
0.037052 
0.053379 
0.694139 
0.4895 
C(38) 
1.572958 
0.380623 
4.132587 
0.0001 
C(39) 
0.714159 
0.381959 
1.869726 
0.0650 
C(40) 
12.71818 
1.675098 
7.592498 
0.0000 
Determinant residual covariance 
5.50E+13 

Equation: D(GDP) = C(1)*( GDP(1) – 10.101480713*NOXP(1) – 

22.5215248963*INF(1) + 120.803853033*EXR(1) + 3340.87393996 ) 

+ C(2)*D(GDP(1)) + C(3)*D(GDP(2)) + C(4)*D(NOXP(1)) + C(5) 

*D(NOXP(2)) + C(6)*D(INF(1)) + C(7)*D(INF(2)) + C(8)*D(EXR(1)) + 

C(9)*D(EXR(2)) + C(10) 

Observations: 31 

Rsquared 
0.935246 
Mean dependent var 
2868.824 

Adjusted Rsquared 
0.907495 
S.D. dependent var 
5745.779 

S.E. of regression 
1747.562 
Sum squared resid 
64133436 

DurbinWatson stat 
1.900738 

Equation: D(NOXP) = C(11)*( GDP(1) – 10.101480713*NOXP(1) – 

22.5215248963*INF(1) + 120.803853033*EXR(1) + 3340.87393996 ) 

+ C(12)*D(GDP(1)) + C(13)*D(GDP(2)) + C(14)*D(NOXP(1)) + C(15) 

*D(NOXP(2)) + C(16)*D(INF(1)) + C(17)*D(INF(2)) + C(18)*D(EXR( 

1)) + C(19)*D(EXR(2)) + C(20) 

Observations: 31 

Rsquared 
0.867312 
Mean dependent var 
298.9774 

Adjusted Rsquared 
0.810445 
S.D. dependent var 
640.1992 

S.E. of regression 
278.7292 
Sum squared resid 
1631489. 

DurbinWatson stat 
2.549426 

Equation: D(INF) = C(21)*( GDP(1) – 10.101480713*NOXP(1) – 

22.5215248963*INF(1) + 120.803853033*EXR(1) + 3340.87393996 ) 

+ C(22)*D(GDP(1)) + C(23)*D(GDP(2)) + C(24)*D(NOXP(1)) + C(25) 

*D(NOXP(2)) + C(26)*D(INF(1)) + C(27)*D(INF(2)) + C(28)*D(EXR( 

1)) + C(29)*D(EXR(2)) + C(30) 

Observations: 31 

Rsquared 
0.149829 
Mean dependent var 
0.436194 

Adjusted Rsquared 
0.214530 
S.D. dependent var 
16.60020 

S.E. of regression 
18.29437 
Sum squared resid 
7028.363 

DurbinWatson stat 
2.083356 

Equation: D(EXR) = C(31)*( GDP(1) – 10.101480713*NOXP(1) – 

22.5215248963*INF(1) + 120.803853033*EXR(1) + 3340.87393996 ) 

+ C(32)*D(GDP(1)) + C(33)*D(GDP(2)) + C(34)*D(NOXP(1)) + C(35) 

*D(NOXP(2)) + C(36)*D(INF(1)) + C(37)*D(INF(2)) + C(38)*D(EXR( 

1)) + C(39)*D(EXR(2)) + C(40) 

Observations: 31 

Rsquared 
0.898230 
Mean dependent var 
4.151568 

Adjusted Rsquared 
0.854615 
S.D. dependent var 
12.79506 

S.E. of regression 
4.878680 
Sum squared resid 
499.8319 

DurbinWatson stat 
1.637621 

LM SERIAL CORRELATION TEST
VEC Residual Serial Correlation LM Tests 

Null Hypothesis: no serial correlation at lag order h 

Date: 11/10/18 Time: 22:20 

Sample: 1988 2016 

Included observations: 28 

Lags 
LMStat 
Prob 
1 
10.01800 
0.8657 
2 
19.07625 
0.2647 
3 
14.75249 
0.5428 
4 
13.11967 
0.6640 
5 
20.41634 
0.2021 
6 
13.92353 
0.6044 
7 
17.10099 
0.3791 
8 
10.67969 
0.8288 
9 
31.38871 
0.0120 
10 
10.63728 
0.8313 
11 
14.48802 
0.5624 
12 
20.56763 
0.1957 
Probs from chisquare with 16 df. 
GRANGER CAUSALITY
Pairwise Granger Causality Tests 

Date: 11/10/18 Time: 22:22 

Sample: 1985 2016 

Lags: 2 

Null Hypothesis: 
Obs 
FStatistic 
Prob. 
NOXP does not Granger Cause GDP 
32 
12.6243 
0.0001 
GDP does not Granger Cause NOXP 
3.12283 
0.0603 
