## 1. Introduction

Although Nigeria recently lost its number one position as the largest economy in Africa to South Africa, restructuring of the Nigeria National Account through rebasing has helped to improve Nigerian economic prospects. According to Euromoney Institutional Investor Company (EMIS), Nigeria generated one-fifth of the African gross domestic product (GDP) in 2015, as its contribution to the Sub-Saharan African GDP increased from 18% in 2009 to around 32% in 2013. The National Bureau of Statistics (NBS) report (

NBS 2015a) showed that the Nigerian economy changed considerably in terms of the volume of activity in all economic sectors as the post-rebasing data in the construction sector was more optimistic, modern construction activities were captured, and prices were correctly deflated. Since the rebasing of the GDP,

PricewaterhouseCoopers Limited (

2017) showed that the Nigerian economic structure has become more diversified, with oil becoming less relevant, and accounting for 8.4% of the GDP, but only from an activity perspective.

Both the oil and construction sectors play significant roles in the development of any economy. According to

Khan et al. (

2013), the products of these two sectors are indispensable for industry, including industrial processes and outputs. Furthermore, demand for these commodities continues to grow due to their various uses and direct links to the industry and social well-being of a society.

PricewaterhouseCoopers Limited (

2017) reported that the oil sector remains the predominant source of fiscal and export revenue, highlighting a growing relationship between oil and non-oil sectors through the exchange rate channel.

Historically, the boom in the oil sector has impacted the economic growth in Nigeria; however, the current global oil market crisis and recent depression in the Nigerian economy has created serious problems for progressive economic growth. This has also presented a major risk for the construction industry, as budget revenues have been reduced and the government’s ability to invest in infrastructure has been restricted (

EMIS 2015). The latest 2017 GDP reports (

NBS 2017a,

2017b) indicate some signs of recovery, as does the2017–2020 medium-term Economic Recovery and Growth Plan (ERGP) of the Federal Republic of Nigeria (

Ministry of Budget and National Planning 2017), which aims to maximize the capacity of various sectors with the greatest potential to restore economic growth and diversification by, for example, facilitating foreign exchange and increasing economic resilience to external shocks. Despite this, the nation’s economic indices remain poor and NBS reports (

NBS 2017c,

2017d) show that the Nigerian economy is seriously dwindling. Additionally, the accuracy and truthfulness of current economic reports have been questioned.

Dlamini (

2012) posited that the construction industry has the potential to positively impact economic growth and is an important component of investment programs in developing economies,

Roodman and Lenssen (

1994) stated that 1/10 of the global economy was dedicated to constructing, operating, and equipping buildings, and this activity accounts for 40% of the material flow entering the world economy, destined for roads, bridges and vehicles to connect the buildings.

Du Plessis (

2001) noted that the construction industry is the world’s largest industrial employer with 111 million employees, and in most countries, it accounts for more than half of capital investment and as much as 10 per cent of the Gross National Product (GNP). Thus, in the race toward economic and social development in Nigeria, the scenarios above suggest that the construction and oil sectors are closely tied. Unfortunately, the rebasing of the national account, the drop in global oil prices, the economic depression and instability, the Dollar-Naira issue, and foreign direct investment flow are all economic issues that have seriously altered the Nigerian economic equation.

However, the available literature lacks consensus on the nature and extent of the interrelationship between oil prices, Nigeria’s economy, and the construction activities in Nigeria, and the correlation between GDP output, construction output, and oil prices. Studies (

Hamilton 1996;

Lescaroux and Mignon 2008;

Alper and Torul 2009;

Bolaji and Bolaji 2010;

Syed 2010;

Rasmussen and Roitman 2011;

Bouzid 2012;

Shaari et al. 2013;

Difiglio 2014) have shown that oil price shocks influence all sectors of the economy including construction. Specifically,

Shaari et al. (

2013) stated that oil price increases influence the construction sector by increasing the costs of raw materials. They further argued that suppliers inevitably increase raw material prices for contractors to cover higher transportation costs, increasing the cost of raw materials for contractors. Even in Nigeria,

Olatunji (

2010) found that the high cost of construction was due to the high cost of finance and wild volatility that were stimulated by issues in oil price regimes.

According to

Idrisov et al. (

2015), understanding and identifying the basic mechanics of the impacts that oil prices have on economic development, including the interrelationship with the construction sector, are important for understanding the reasons for the current slowdown in GDP growth and for developing a plan to accelerate growth or minimize the slowdown. Based on this premise, the need to determine the nature and extent of interrelationship between oil prices, the construction sector, and the aggregate GDP in Nigeria has become apparent, which was the goal of this study. Therefore, this study aimed to determine the empirical relationships between the real aggregate GDP, the construction sector output, and the annual oil prices in Nigeria.

## 2. Literature Review

Conversely,

Trade Invest Nigeria (

2012) argued that the key factors that contributed to the growth in the construction and property sector included: high demand for buildings across all sectors of the economy, the focus on infrastructural development by state and federal governments, the adoption of privatization and commercialization as instruments of federal government policy, and attempts at implementing regulations related to the oversight, process, and business dealings of the construction industry in the country. In addition, the Central Bank of Nigeria (

CBN 2013) reported that building and construction output lag value, government capital expenditure, the nominal exchange rate, and the maximum lending rate also drive the construction sector output. Consequently,

Isa et al. (

2013) asserted that the all-inclusive effects of this sector, and especially its employment generating potential, make it a platform for sustainable development, especially if proper mechanisms are implemented to stimulate the growth of the sector.

Saka and Lowe (

2010) used economic sectoral output data to investigate the relationships between the construction sector and other sectors in the Nigerian economy. To analyze the significance of construction linkages with other sectors of the economy, they applied econometric techniques such as the unit root test, co-integration test, and Granger causality test. Construction was found to significantly lead many sectors and almost all economic sectors fedback into the construction sector, highlighting the mutual interdependence of construction with other sectors of the economy. The study concluded that the Nigerian construction sector is important because of its significant forward and backward linkages and multipliers on sectors of the economy.

Salami and Kelikume (

2011) examined the linkage between the manufacturing sector and other sectors of the Nigerian economy using a more dynamic estimating tool. The impact of changes in manufacturing output on the output of the other sectors and the effects of changes in output of other sectors on the manufacturing sector were determined using the Granger causality test and vector auto regression. Using quarterly time series data for 1986 to 2010, a weak link between the manufacturing sector and other sectors of the Nigerian economy was established. The manufacturing sector output showed no causal relationship with real economic activities or the financial sector output as measured by the real GDP. However, only two major sectors, building and construction and hotel and restaurant, appear to be driving the manufacturing sector with the latter exhibiting a bi-directional relationship with the manufacturing sector (

Salami and Kelikume 2011).

To examine the relationship between GDP and agriculture, industry, building and construction, wholesale and retail, and trade and services for the period of 1960 to 2008,

Anyanwu et al. (

2013) applied multiple regression analysis. Their results showed that the agriculture share of the GDP was the highest, followed by the services sector, then the wholesale and retail trade sector, then the industry sector, whereas building and construction made the smallest contribution to GDP. The study revealed that building and construction consistently made the least contribution to the GDP from 1960 to 2008. This result is supported by the Nigeria Industrial Revolution Plan (

NIRP 2014) which recognized the disproportionate contribution of construction industry to the growth of Nigeria’s total GDP when compared to the growth of the sector.

Yusuf (

2016) revealed that the construction industry is significantly related to and plays significant roles in all the sectors of Nigeria economy, having a medium strength relationship with the Nigerian Annual % Growth Rate (NA%GR). This indicates that the construction industry adds to the gross value of the Nigerian economy.

Although

Okoye et al. (

2016a) found no significant difference between the growth rate of construction sector and GDP before and after the rebasing of Nigeria national accounts, econometric techniques were used to establish a strong positive and bi-directional causal relationship between the aggregate real GDP and the construction sector output of Nigeria, implying that the construction sector Granger causes the total GDP and vice versa. Both the construction sector and aggregate GDP influenced each other (

Okoye 2016). In another study,

Okoye et al. (

2016b) found that during periods of economic fluctuation, the construction sector growth rate is more volatile than the GDP growth rate, meaning instability in the activities of construction sector also exist, which invariably affects the aggregate economic activities.

Olatunji (

2010) found that construction costs are high due to the high cost of finance and intense volatility caused by issues in oil price regimes. The study further revealed that whereas the Nigerian construction industry shows positive growth and has significantly contributed to the aggregate GDP growth since 2000, the oil industry has persistently failed to contribute to positive GDP growth. Another empirical study conducted by

Akinlo (

2012) revealed that the oil industry can cause other non-oil sectors to grow. Specifically, bidirectional causality was found between oil and manufacturing, oil and building and construction, manufacturing and building and construction, manufacturing and trade and services, and agriculture and building and construction.

Akomolafe and Jonathan (

2014) revealed that industrial sectors including construction are not directly affected by oil prices, but are sensitive to oil price changes.

Nwanna and Eyedayi (

2016) reported a positive and significant relationship between oil price and economic growth, stating that oil price volatility does not positively impact the economy, but the oil price does. This is in contrary to the study of

Igberaese (

2013) who concluded that short-term economic growth in Nigeria is a result of the volatility in oil prices. For

Gummi et al. (

2017), no long-term relationship exists between oil prices and economic growth in Nigeria, but rather a significant unidirectional causality was found between the oil prices and the short-term economic growth in Nigeria.

No single study has been able to aggregate the interrelationship between these variables, and they lack consensus. Secondly, the change in the overall economic order and the recent depression, in both the oil prices and the Nigerian GDP in the rebased economy, need to be investigated. These scenarios and the failure of the previous studies to capture the current trends in the construction sector and Nigeria economy and their interrelationships with oil prices created the motivation to complete this investigation.

Thus, we measured the construction sector output and the Nigerian real GDP in relation to the changes in oil prices to ascertain if the construction sector has any causal effect on the Nigerian economy and/or oil prices, to which extent, and vice versa. Secondly, the direction of the effect, if any, was also determined, including whether the construction sector leads the Nigeria economy and/or oil prices, vice versa, or both, and to what extent.

## 3. Methodology

#### 3.1. Data and Data Description

To analyze the dynamic relationship between real economy (GDP), construction sector output, and real oil prices, the annual statistical rebased data from 1981 to 2016 for the construction sector output and total real GDP were extracted from the NBS publications in Million Naira, and the Annual Average Oil Prices were extracted from the OPEC Annual Statistical Bulletin 2017 and BP Statistical Review of World Energy June 2017. Contemporaneous correlation was examined, and evidence of Granger causality between these variables was determined.

Table 1 presents the total real GDP, total construction sector output, and the real annual average oil prices at 2010 constant basic price year-on-year from 1981 to 2016. Annual observations of GDP and construction sector data were extracted from the following NBS publications: Nigerian Construction Sector Summary Report 2010–2012, Nigerian Gross Domestic Product Quarterly Report, Quarter Four 2016 (

NBS 2017c), Nigerian Gross Domestic Product Quarterly Report, Quarter One 2017 (

NBS 2017d), Revised and Final GDP Rebasing Results by Output Approach (

NBS 2014), Nigerian Gross Domestic Product Quarterly Report, Quarter Four 2014 (

NBS 2015b), Nigerian Gross Domestic Product Quarterly Report, Quarter Four 2015 (

NBS 2016a), and Post GDP Rebasing Revision: 1981–2010 (

NBS 2016b).

#### 3.2. Unit Root Test

In determining whether economic data are stationary or integrated using classical methods,

Kwiatkowski et al. (

1992) noted that performing tests of the null hypothesis using stationarity and the null hypothesis of a unit root were effective. For this study, a unit root test was used to check the stationarity of a data series.

Ajide (

2014) stated that the order of integration is a pre-requisite for almost all time series analyses. This step is important because if non-stationary variables are not identified and used in the model, spurious regression problems are created (

Granger and Newbold 1974), whereby the results suggest that statistically significant relationships exist between the variables in the regression model even when evidence of contemporaneous correlation exist rather than meaningful causal relations (

Granger and Newbold 1974;

Harris 1995). However, the unit root test can equally be referred to as the augmented Dickey Fuller (ADF) test (

Dickey and Fuller 1979) and can be represented in the following mathematical formulation:

where

$\Delta {Y}_{t}={Y}_{t}-{Y}_{1}$,

α_{0} is a drift term, T is the time trend with the null hypothesis, H0:

α_{2} = 0 and its alternative hypothesis H1:

α_{2} ≠ 0, n is the number of lags necessary to obtain white noise, and

μ_{t} is the error term. However, the implied t statistic is not the student’s t distribution, but instead is generated from Monte Carlo simulations (

Engle and Granger 1987). Note that failing to reject H0 implies the time series is non-stationary. Unit-root test are classified into series with and without unit roots, according to their null hypothesis, to determine if each variable is stationary.

In conducting a unit root test,

Baumöhl and Lyócsa (

2009) argued that providing results of at least two tests is a convention in economic literature. Most frequently ADF, Phillips-Perron (PP) test, and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test are used, and are also incorporated into the majority of statistical or econometric software. However, since KPSS includes a transposed null hypothesis, which identifies a dataset as stationarity against alternative of a unit root, the results of this test could be mixed (

Baumöhl and Lyócsa 2009). Thus, the KPSS test was not included in this study; rather, a modified Dickey-Fuller (DF) unit root test transformed via a generalized least squares (GLS) regression that was proposed by

Elliott et al. (

1996) was used: the DF-GLS test.

Therefore, to compare the results in this study, Augmented Dickey- Fuller Test (ADF) (

Dickey and Fuller 1979), the Dickey-Fuller Test with GLS Detrending (DF-GLS) (

Elliott et al. 1996), and the PP test (

Phillips and Perron 1988) unit root tests were used to determine the existence of unit roots and the degree of differences to obtain the stationary series of total GDP, total construction output, and annual average oil prices.

Although Kulaksizoglu (

2014) observed that the ADF test is the most used unit root test in econometrics,

Kwiatkowski et al. (

1992) noted that the test is a reasonable first attempt to test stationarity, but the available methods all suffer from the lack of a plausible model in which the null of stationarity is naturally framed as a parametric restriction. Since the DF and ADF tests have low power for small samples (

Cheung and Lai 1995) and a high probability of an error of the second type (i.e., the probability of not rejecting a false H0), the PP unit root test was also applied to check the robustness of the estimation results.

Since all the series are not expressed in the same unit, they were transformed into their natural logarithm for uniformity. The logarithm values were then used to test the existence of the unit root. Thus, for each time series, the ADF, DF-GLS, and PP tests were run three times: with no constant included and no trend, with a constant included assuming that the series does not exhibit any trend and has a non-zero mean, and with a constant and a trend included, assuming that the series contains a trend. Also, the number of lagged first difference terms for the ADF test and the number of periods of serial correlation to include in the test regression for the PP test were determined for each time series, whereas the DF-GLS is a simple modification of the ADF test, in which the data are detrended so that explanatory variables are removed from the data prior to running the test regression (

IHS Global Inc. 2014).

A“1” indicates that the series is integrated at order one, i.e., has one unit root, and “0” denotes that the series is stationary at level. If the time series data of each variable are found to be non-stationary at level, then a long-term relationship between the variables may exist. The ADF approach controls for higher-order correlation by adding lagged difference terms of the dependent variable Y to the right-hand side of the regression (

IHS Global Inc. 2014). The PP test corrects the t-statistic of the coefficient from the first order autoregressive model to account for the serial correlation in the series by estimating the non-augmented DF test equation, and modifying the t-ratio of the α coefficient so that the serial correlation does not affect the asymptotic distribution of the test statistic. Conversely, the DF-GLS-ratio follows a Dickey-Fuller distribution only in the constant case, and the asymptotic distribution differs when both a constant and trend are included (

IHS Global Inc. 2014).

#### 3.3. Autoregressive Distributed Lag (ARDL) Cointegration Test

To maintain their long-term information, modeling a time series can be completed using cointegration (

Nkoro and Uko 2016). The cointegrating equation is also known as the stationary linear combination, which may be interpreted as a long-term equilibrium relationship between variables under consideration. However, several cointegration techniques are applicable for time series analysis: Autoregressive Distributed Lag (ARDL) cointegration technique, bound cointegration testing technique (

Pesaran and Shin 1999;

Pesaran et al. 2001), and Johansen’s cointegration technique (

Johansen 1991,

1995), but their common objective is to determine the most stationary linear combination of the time series variables. Therefore, ARDL bounds testing (

Pesaran et al. 2001), that handles mixed integration orders of the time series was used to investigate the stable long-term relationships between the variables in this study. According to

Nkoro and Uko (

2016), the ARDL model is preferable when dealing with integrated variables of different orders: I(0), I(1), or combination of both, and the model is robust when a single long-term relationship exists between the underlying variables in a small sample size. An ARDL model contains both lagged dependent and lagged other explanatory variable(s).

The AR part of ARDL refers to the regression of the dependent variable on its past values; whereas the DL refers to the distributed lag effect of the lagged other explanatory variable(s) in the model. The ARDL model captures the dynamic effects from lagged dependent variables and lagged other explanatory variable(s), eliminating error serial correlation by including sufficient lags. The ARDL model can also be transformed into one with only lagged order explanatory variable(s) that go back into the infinite past.

The general autoregressive distributed lag (ARDL) model is written as:

where

φ(

L) is an order-p polynomial that, for stability, has roots lying outside the unit circle and

θ(L) is an order-q polynomial. Expanding the lag polynomials, Equation (2) can be written as:

or

With a sample of T observations, this model can be estimated for T–max {p, q} observations. In this case, 34 observations were included in the model. Furthermore, both sides of Equation (2) can be divided by the autoregressive polynomial to obtain:

where

α and

v are the constant and error term defined in Equation (2). The ARDL model sometimes is called the rational lag because its lag distribution cause can be represented by the ratio of two finite lag polynomials, where the rational numbers can be represented as the ratio of two integers.

The lag structure of the ARDL models on the right-hand side of Equation (2) was applied to an explanatory variable x rather than to a white-noise error term ε as in Equation (1). However, the coefficients of the order-p polynomial θ(L) only affect the first q lags of the dynamic lag distribution of the effect of x on y. The behavior of the “tail” of the lag distribution beyond q entirely depends on the auto-regressive polynomial φ(L). The dynamic effect is stable only if the roots of φ(L) lie outside the unit circle are carried over from the autoregressive lag model. If the error term μ_{t} is assumed to be a white noise process, or more generally, is stationary and independent of x_{t}, x_{t}_{−1}, … and y_{t}, y_{t}_{−1}, …, the ARDL models can be consistently estimated by ordinary least squares.

The F-statistic was applied to the joint null hypothesis so that the coefficients of the lagged variables are zero. The hypothesis that the coefficients of the lag level variables are zero was tested. The null of the non-existence of the long-term relationship is defined by:

**H0.** δ_{1} = δ_{2}= 0 (null, i.e., the long-term relationship does not exist)

**H1.** δ_{1}≠ δ_{2}≠ 0 (alternative, i.e., the long-term relationship exists)

This was tested in each of the models as specified by the number of variables.

This can also be denoted as follows:

The hypothesis was tested using the F-statistic (Wald test) in Equations (7) and (8), respectively. The distribution of this F-statistics is non-standard, regardless of whether the variables in the system are I(0) or I(1). The critical values of the F-statistics for different numbers of variables (K), and whether the ARDL model contains an intercept and/or trend were generated from the E-view analysis, based on the study by Pesaran et al. (2001), which provides two sets of critical values. One set assumes that all the variables are I(0) (i.e., the lower critical bound assumes all the variables are I(0), meaning that no cointegration exists among the underlying variables), and another assumes that all the variables in the ARDL model are I(1)(i.e., the upper critical bound assumes all the variables are I(1), meaning that cointegration exists among the underlying variables). For each application, a band covers all the possible classifications of the variables into I(0) and I(1).

If the relevant computed F-statistic for the joint significance of the level variables in each of Equation (7) and (8) falls outside this band, a conclusive decision can be made without needing to know whether the underlying variables are I(0) or I(1), or fractionally integrated. That is, when the computed F-statistic is greater than the upper bound critical value, then the H0 is rejected, meaning the variables are cointegrated. If the F-statistic is below the lower bound critical value, then the H0 cannot be rejected, meaning there is no cointegration among the variables.

#### 3.4. Granger Causality Test

The standard Granger framework is usually used to test the direction of causation between two variables. The basic concept of the Granger causality tests is that future values cannot predict past or present values. If past values for the construction sector output significantly contribute to the explanation of the total GDP, then the construction sector output is said to Granger-cause Nigerian economy. This means that the construction sector output is Granger-causing Nigerian economy when the past values of the construction sector have predictive power for the current value of the real GDP, even if the past real GDP values are considered. The same can be applied to construction sector output and annual average oil prices, and to total real GDP and annual average oil prices. Conversely, if the Nigerian economy is Granger-causing construction sector output, the real GDP change would take place before a change in the construction sector output. This is the same for other comparable variables in this study. Thus, in this present study, the Granger causality test is used, and fitted with annual data from 1981 to 2016 to test the direction of causation between:

- (1)
the construction sector output and the Nigerian economy, to determine whether construction sector output stimulates Nigerian economy or vice versa,

- (2)
the construction sector output and annual average oil prices, to determine whether construction sector output stimulates annual average oil prices or vice versa, and

- (3)
the Nigerian economy and annual average oil prices, to determine whether the Nigerian economy stimulates annual average oil prices or vice versa.

In all cases, the test also determines if feedback effects occur between comparable variables. Therefore, the Granger causality test consists of estimating the following equations:

where

U_{t} and

V_{t} are the uncorrelated and white noise error term series, respectively. Causality may be determined by estimating Equation (1) and testing the null hypothesis that

$\sum _{i=1}^{n}{\beta}_{2i}}=0$ and

$\sum _{i=1}^{n}{a}_{2i}}=0$ against the alternative hypothesis that

$\sum _{i=1}^{n}{\beta}_{2i}}\ne 0$ and

$\sum _{i=1}^{n}{a}_{2i}}\ne 0$ for Equations (9) or (10), respectively.

If the β_{2i} coefficients are statistically significant, but those of α_{2i} are not, then the GDP output is said to have been uni-directionally caused by construction sector output. The reverse causality holds if the coefficients of α_{2i} are statistically significant whereas those of β_{2i} are not. However, if both α_{2i} and β_{2i} are statistically significant, then causality is bi-directional. This also holds for other variables combinations in this study.

Meanwhile, the entire analysis was completed with EViews, version 9.0, an econometric software package used for economic and financial data. The results are presented in the section below.

## 5. Discussion

The results of this study demonstrated the interrelationships between the aggregate economy, the construction sector, and the oil prices both historically and presently in the rebased economy in Nigeria. Basically, the construction sector is one of the main sectors of the economy and is one of the few that was estimated to have a significantly higher share in the GDP after rebasing the Nigerian national account (

World Bank 2014). As a result, the future of this sector is more optimistic because more modern construction activities have been captured and the prices correctly deflated (

NBS 2015a) in the new estimate.

However, the available literature suggests that no consensus exists about the nature and extent of the influence of the construction sector on the aggregate GDP output in Nigeria. However, as one of the economic activities in Nigeria, the global oil prices have been assumed to directly influence the construction sector output and the aggregate economy contrary to the findings of previous studies (

Akomolafe and Jonathan 2014;

Nwanna and Eyedayi 2016).

The result of this study shows that only the total GDP output that Granger Causes the construction output without feedback, and no Granger causal relationship exists between the total GDP output and the oil prices; and between the construction output and the oil prices. This implies that oil prices do not have any direct effect on both the GDP output and construction sector output under the current economic condition in Nigeria in the long term despite the fact that the result of Pearson correlation analysis reveals that the oil prices have very strong linear association with the real GDP output and the construction output under the same economic condition in Nigeria. It also suggests that economic activities of the non-oil sectors of the economy trigger construction activities in Nigeria. This particular result supports that report of

CBN (

2013).

The coefficient of the correlation further indicates that there is a very strong positive correlation between construction output and oil prices in Nigeria. Invariably, this implies that both construction sector and oil sector are ingredients of Nigerian economy which made them to be correlated among themselves, and linearly associated. However, the association is only in the short term. This can be evidently seen in

Table 1 which shows that during the period of oil boom in the recent past, both the GDP output and construction output marginally increased for a short while. This result supports that of (

Akomolafe and Jonathan 2014;

Nwanna and Eyedayi 2016).

Furthermore, the study found that all the unit root tests performed on the variables revealed that they are not stationary at the same order, thus the ARDL bound testing result shows that the variable are not cointegrated. As a result, the null hypothesis of no cointegration is not rejected. It therefore implies that there is no long-term equilibrium contemporaneous relationship between the variables. It can also be deduced that the effect of oil prices and construction output on the total real GDP output may be observed only in the short term, during the transition from a long-term equilibrium at one level of oil prices and construction output to a long-term equilibrium at another level of oil prices and construction output.

Generally, the result implies that the aggregate GDP output in Nigeria cannot be predicted in the long-term from both the oil prices and the construction sector output. The Nigeria GDP output (economy) is not mainly dependent on oil prices and the construction sector output, and simultaneously, oil prices do not directly affect the construction output and vice versa.

The overall results of the study aligned with the findings of

Akomolafe and Jonathan (

2014),

Nwanna and Eyedayi (

2016), and

Gummi et al. (

2017), but are contrary to those of

Olatunji (

2010),

Saka and Lowe (

2010),

Salami and Kelikume (

2011),

Akinlo (

2012),

Igberaese (

2013),

Okoye (

2016), and

Yusuf (

2016), who established a bi-directional causal relationship between the construction sector and the Nigerian economy. In terms of correlation, the results of this study are supported by the results of the above authors, even though

Yusuf (

2016) specifically found a medium positive correlation between the construction sector and the real GDP output.

## 6. Conclusions

The results of this study are not surprising, as the data collected from various NBS reports and the OPEC Annual Statistical Bulletin were used to empirically examine the interrelationships between the construction sector output, the total GDP output, and the oil prices in Nigeria to measure the nature and extent to which the construction sector and oil prices influence and relate to the Nigerian economy in terms of aggregate GDP output.

We found that although very strong positive and significant correlations exists between the construction sector output and the total GDP output, the construction sector output and oil prices, and the total GDP output and oil prices, these linear relationships only existed in the short term. Explicitly, none of the variables directly influence each another, except the total GDP output on the construction sector output, meaning only a uni-directional causal relationship flow from the total GDP output to the construction sector output without feedback.

Fundamentally, both the construction and oil sectors are ingredients for the national economy as suggested by the strong positive correlations. However, these relationships do not translate to any direct causal influence on each other, except for the total GDP output on the construction sector output, which further suggests that the economic activities of other non-oil sectors lead to improved construction activities in Nigeria. The causal effect is only marginal and in the short term, as it can only predetermine short term transitional trends from one long term equilibrium to another. Additionally, a critical look at the trend shows that in the long term, the short-term relationship is coincidental.

The empirical result affirms that although oil prices and the construction sector are economic variables, their influence in determining the outcome of the Nigerian economic output is not pronounced, and growth in the GDP through activities in other major sectors of the economy will instead trigger construction activities in Nigeria. Therefore, the oil prices should receive less attention from the government. The Nigerian government should re-strategize and refocus their attention to those sectors that contribute more substantially to the aggregate economy to address the current economic challenges.

Based on this premise, we argue that neither the construction sector nor the oil prices directly influence the aggregate economy; rather, the activities of other sectors of the economy stimulate the construction sector in Nigeria. Although Nigeria still believes and relies on the effect of oil prices on the economy, we propose that the effect of oil prices on the GDP output and construction has dramatically decreased under current economic conditions.

The results of study have challenged the status quo of the current economic management in Nigeria, providing a veritable tool in the hands of economic managers and policy makers in Nigeria. The country is still struggling to recover from the economic depression; the current study is a pointer toward the direction of economic rejuvenation in Nigeria. The Nigerian government needs to redirect its attention to formulating policies that ensure sustainable economic growth rather than relying on oil prices and construction sector activities.

From the above, two important policy recommendations are suggested: (1) the Nigerian government should de-emphasize over-reliance on the oil sector through policy readjustment, and (2) an urgent need exists for economic diversification in Nigeria, since the results of this study suggest that an increase in the aggregate GDP output is a result of the activities of other non-oil sectors. Optimization of other sectors of the economy will assist with the achievement of the federal government’s medium term Economic Recovery and Growth Plan (ERGP) in Nigeria.

Finally, these results underpin the

PricewaterhouseCoopers Limited (

2017) results which state that after the rebasing of the GDP series, the oil sector has become less relevant in determining the economic structure of Nigeria from an activity perspective, despite its dominance in the fiscal and export revenues of the country.