_{1}

^{*}

This study critically examines the effects of specific exogenous shocks —oil price shocks on Nigeria’s external sector. Employing a Structural Macroeconomic Model (SMM) comprising of ten behavioural equations and four identities with quarterly data spanning from 1981 to 2015, the SMM simulations of the external sector found that oil price shocks do have significant impacts on the components of Nigeria’s external sector. Specifically, while oil price shocks elicited varying responses from all components of Nigeria’s external sector components, the simulated results showed very limited evidence of asymmetry in the responses to both positive and negative oil price shocks. For policy, the simulated responses of capital financial flows, foreign debt flows, imports, nominal exchange rates, reserves, remittances, and capital financial flows, reflect the over - dependence of the Nigerian economy on crude oil, and the justifiable need to diversify the Nigerian economy away from the oil sector.

Achieving macroeconomic objectives—economic growth, full employment, price stability and balance of payments stability—remain an integral commitment of the Nigerian government and policy-makers alike. The external sector which is a subset of the general macro economy, as captured by the overall balance of payments account, provides a fundamental connection between Nigeria’s dealings with the rest of the world and how actions—trade, capital flows and foreign policy—can influence Nigeria’s external sector outcomes. Indeed, to fully grasp the workings of Nigeria’s macroeconomic structure requires a detailed and thorough study of its external sector and the external sector’s role as a possible propagative channel through which exogenous shocks originating outside the economy can affect the domestic real economy. For example, developing open economies like Nigeria depend on trade, consumption of imported goods and foreign direct investments, which have direct consequences on the real, fiscal and financial sectors of its macro economy [

The literature on the effects of, especially, oil price shocks (demand side shocks) on the domestic economy for both developing and developed economies is vast [

The major problem that now exercises researchers, is not to determine if oil price shocks do matter, as that has been established from the literature. What is of utmost importance is to understand the process by which the effects of these shocks are transmitted to the domestic economy. Empirically this can be achieved by first tracing the effects of these shocks on all components of the shock transmission channel, and secondly, by eliciting the responses of domestic macroeconomic variables from the shock responses of the components of the transmission channel. The channel through which the effects of these exogenous shocks are propagated to the domestic economy is by external sector components. Addressing this problem is important because, as [

The literature on the effects of oil price shocks on the domestic economy for both developing and developed economies has been firmly established [

This study will provide insights into the responses of Nigeria’s external sector, which is an important transmission channel for oil price shocks. Especially in recent times, where global oil price shocks and exchange rate fluctuations are primary concerns for Nigeria, this study will provide detailed understanding on how oil price shocks affect Nigeria’s external sector and also provide policy tools for intervention. This will be beneficial to the Nigerian government, policy makers, and all economic agents that partake in the global market place. On this basis, the study sets out to 1) expand on the literature on the effects of oil price shocks. 2) Show the effects of oil price shocks on the Nigerian external sector. 3) Provide policy responses to oil price shocks as it relates to components of Nigeria’s external sector and 4) show the existing inter-relationships between components of Nigeria’s external sector. In Section 2, the study focuses on the literature review. In Sections 3 and 4, this study provides the methodology used and the results from the simulated model respectively. In Section 5, the paper presents the discussions and findings, and concludes in Section 6.

From economic literature, the empirical variations in both methods and variables used in macroeconomic modeling of the external sector depend on the peculiarities of the economy being modeled. Macroeconomic models of the external sector therefore reflect the structure and dynamics of individual economies. However, it has become conventional to include the major external sector variables—current account variables, capital account variables in such models as well as variables—such as, exchange rates, interest rates, capital mobility that acts as linkages to both the external sector and the real domestic economy.

The advantages of employing a macro econometric model of an economy or a sub sector cannot be over emphasized. In [

In determining the exogenous shocks relevant for this study, the literature on modelling external sector components, however few, has provided possible exogenous shocks that may influence the economy of an oil dependent country like Nigeria, [

In [

B P t = B t + K t

The foreign exchange curve used in the model relates domestic aggregate demand, Y, with the level of interest rate, r, that exists for a given exchange rate, η, and aggregate foreign demand, Y^{f}. Therefore the persisting foreign exchange, e, can be represented as;

r = α 0 + α 1 Y + α 2 Y f + α 3 e r

Import demand was modeled as a function of income, lags of imports, price of imported goods and services in foreign currency terms, and the real effective exchange rates. Exports on the other hand were modeled as a function of real GDP, lags of export and the prices of exports in terms of the US dollar. [

Given the structure of the Nigerian economy as an oil importing as well as an oil exporting country, it becomes paramount to understand the relationship between oil prices and Nigeria’s economic growth process as shocks to oil prices are likely to have both direct and indirect effects at the micro and macro levels of the Nigerian economy [

Also, the transmission channel through which oil price shocks will affect an economy will depend largely on the economy’s sectoral composition, institutional structures and level of economic development [

From existing literature, considerable amount of studies have focused on understanding the relationship between oil price shocks and aggregate economic performance for various countries [

The full effects of oil price shocks—both positive and negative—on general economic activity will depend to a large extent to what oil producers (government) do with the windfall revenue. [

[

For industrialised economies, it was established that shocks to oil prices have detrimental effects on the manufacturing and industrial sectors [

[

The external sector model to be constructed will consist of eleven behavioral equations and three identities. The behavioral equations will include oil export, non-oil export, total imports, reserves, exchange rate, foreign direct investment, foreign debt flow, foreign portfolio investment, remittances, and services, which are the components of the external sector to be estimated. The study employed SMM appropriate econometric technique based on the characteristics of the data for the period 1980-2015. The system modelling used in this study will estimate a group of equations containing unknown parameters (parameters attributed to exogenous variables). SMM allows for the examination of simulated results under assumptions that are peculiar to exogenous variables. Thus, various multivariate techniques can be used in estimating the unknown parameters as well as consider the interdependence that exists between the equations in the system. The general form of the system model is:

f i = ( y t , y t − 1 , y t − p , x t , α i ) = μ i t , i = 1 , ⋯ , n ; t = 1 , ⋯ , T

where y_{t} represents a vector of endogenous variables, x_{t} is a vector of exogenous variables and α i is a vector of parameters to be estimated in the system model. ϵ t is a vector of possible serially correlated disturbance terms in the system model and f is a vector of real-valued functions. Endogenous variables are determined within the model while exogenous variables are determined outside the model. All equations in the model have a unique endogenous variable attached to it such that, each equation in the model can be represented as:

Y i = f ( y i , x i , α i )

where y_{i} is the endogenous variable in equation i. Any variable that is not assigned as an endogenous variable becomes an exogenous variable. The equations in the system model are divided into stochastic and identities. Identities are equations that are expected to hold in reality while stochastic equations are those that can only hold with a random error^{1}. Stochastic equations will be gotten from the estimation of the equations in the system model, while identities are derived from accounting relationships amongst variables.

The behavioral equations in the SMM will be estimated using ordinary least square (OLS) with the inclusions of lags for both dependent and independent variables in each behavioral equation. [^{2}.

Thus, the SMM used in estimating the effects of exogenous shocks on Nigeria’s external sector components is adapted and improved upon from [

*Stochastic equations express the endogenous variables, identities are relationships that are expected to hold in real life and do not have any stochastic characteristics.

^{2}Assume μ i t = ρ i μ i t − 1 + ϵ i t ( t = 2 , ⋯ , T ) where ϵ i t does not have serial correlated properties. Taking lags of this equation and subtracting it from the original equation 3.2, we get f ( y i , x i , α i ) − ρ f ( y i − 1 , x i − 1 , α i ) = μ i t − ρ i μ i t − 1 = ϵ i t . This transformation removes the problem of serial correlation when using OLS to estimate the behavioural equations in an SMM.

X 0 = f ( P 0 , O P E C , Y f ) , X n = f ( N E R , Y f , Y n ) ,_{ X s = f ( Y , X , R E R ) }, M = f ( Y d , R E R , M L R , R E S ) , R E S = f ( R E R , P 0 , E D S , X 0 ) , N E R = f ( R E S , R M T , I R D , X / M , C P I , T G E , P 0 ) _{, F D I = f ( F D I t − k , P C G D P , X n ) }, F P I = f ( Y , Y f , S M R , I N T f , N E ˙ R ) , F D F = f ( I R D , N E ˙ R , C R , M , Y f ) , R M T = f ( N E R , Y u s )

On the basis of these mathematical equations, the econometric behavioral equations to be estimated will be in the form of a double log equation:

Behavioral equations

L O G X 0 = V 0 , 1 + V 1 , 1 L O G P 0 ( t − 2 ) + V 2 , 1 L O G O P E C ( t − 2 ) + V 3 , 1 L O G Y ( t − 1 ) f + V 4 , 1 L O G X 0 ( t − 3 ) + μ 1

L O G X n = V 0 , 2 + V 1 , 2 R E R + V 2 , 2 L O G Y ( t − 2 ) f + V 3 , 2 L O G Y n ( t − 2 ) + V 4 , 2 L O G X n ( t − 3 ) + μ 2 _{ }

L O G X s = V 0 , 3 + V 1 , 3 L O G Y + V 2 , 3 L O G X ( t − 2 ) + V 3 , 3 R E R + V 4 , 3 L O G X s ( t − 3 ) + μ 3

L O G M = V 0 , 4 + V 1 , 4 L O G Y ( t − 2 ) d + V 2 , 4 R E R + V 3 , 4 M L R ( t − 1 ) + V 4 , 4 L O G R E S ( t − 3 ) + V 5 , 4 L O G M ( t − 2 ) + μ 4

L O G R E S = V 0 , 5 + V 1 , 5 R E R + V 2 , 5 P 0 + V 3 , 5 E D S t − 1 + V 4 , 5 L O G M ( t − 2 ) + V 5 , 5 L O G R E S ( t − 3 ) + V 6 , 5 L O G Y ( t − 2 ) + μ 5

N E R = V 0 , 6 + V 1 , 6 L O G R E S + V 2 , 6 L O G R M T ( t − 1 ) + V 3 , 6 I R D ( t − 1 ) + V 4 , 6 X M + V 5 , 6 C P I ( t − 1 ) + V 6 , 6 L O G T G E ( t − 1 ) + V 7 , 6 P 0 ( t − 2 ) + V 8 , 6 N E R ( t − 3 ) + μ 6

L O G F D I = V 0 , 7 + V 1 , 7 L O G P C G D P ( t − 2 ) + V 2 , 7 L O G X n ( t − 4 ) + V 3 , 7 L O G F D I ( t − 2 ) + μ 7

F P I = V 0 , 8 + V 1 , 8 Y ( t − 1 ) + V 2 , 8 Y ( t − 2 ) f + V 3 , 8 S M R + V 4 , 8 I N T ( t − 2 ) f + V 5 , 8 N E R ( t − 2 ) + V 6 , 8 F P I ( t − 3 ) + μ 8

L O G F D F = V 0 , 9 + V 1 , 9 I R D ( t − 1 ) + V 2 , 9 N E R ( t − 2 ) + V 3 , 9 C R ( t − 2 ) + V 4 , 9 L O G M ( t − 4 ) + V 5 , 9 L O G Y ( t − 2 ) f + V 6 , 9 L O G F D F ( t − 4 ) + μ 9

L O G R M T = V 0 , 10 + V 1 , 10 N E R + V 2 , 10 L O G Y u s + V 3 , 10 L O G R M T ( t − 3 ) + μ 10

Identities

R E R = N E R ∗ ( F C P I / C P I )

X = X 0 + X n

C A = X − M − I N V I − X s + C T

C F = F D I + F P I + O I

where X_{0} is oil exports, P_{0} is oil prices, OPEC represents OPEC quota and Y^{us} represents output from the United States of America, X_{n} is non-oil exports, NER is the nominal exchange rates, Y_{n} is non-oil GDP, X_{s} is service exports, X is the value of exports, and RER is the real exchange rate, M is imports,_{ }Y^{d} represents personal disposable income, MLR is domestic lending rate, RES is external reserves, EDS is external debt services, FDI represents foreign direct investment, PCGDP is per capita income, RES is foreign reserves, RMT is remittances, IRD is the interest rate differential,_{ }X/M is the terms of trade, CPI is the consumer price index, FCPI is foreign country CPI (we used the CPI of the United States given that the nominal exchange rate is expressed in dollar terms), TGE is total government expenditure comprising both recurrent and capital expenditure, FPI represents foreign portfolio investment, Y is domestic GDP, SMR is stock market returns proxied by the returns on the All Share Index (ASI), INT^{f} is interest rate for OECD countries, FDF represents foreign debt flow, Y^{F} is the income or foreign output proxy by OECD, CR represents the credit risk of the borrowing country proxied by the ratio of gross debt to GDP, RMT represents remittances, INVI represents investment income, CA is the current account balance, CF is capital financial flows, OI represents other investments in the capital account and CT represents the net current transfers as captured in the current account balance.

The external sector components that are used to make up the SMM in this study comprises of ten behavioural equations—oil export, non-oil exports, service exports, imports, external reserves, foreign debt flow, remittances, foreign direct investments, foreign portfolio investments, and nominal exchange rates—and four identities. The behavioural equations are estimated in the model using OLS, with the amount of lags included for each variable determined by the Akaike information Criterion (AIC). The appropriate optimal lag length chosen for each variable in each behavioural equation, was done in such a way that the estimated errors for each behavioural equation will not exhibit serial correlation properties. The estimates of the behavioural equation provide close estimates of the actual historical data, which will be used in simulating a baseline for the mode.

The estimated behavioural equation for oil exports in

The estimates on non-oil export equation in

Estimates of the service export equation in

LOG(X0) = 1.39999580363 * LOG(X0(−1)) − 0.324846615845 * LOG(X0(−2)) − 0.18190027768 * LOG(X0(−3)) + 0.0324272337436 * PO − 0.00569744857525 * PO(−1) − 0.0076016998432 * PO(−2) + 0.000218229060879 * OPEC + 0.000281155770049 * OPEC(−1) − 0.000541758175011 * OPEC(−2) + 0.116489919066 * LOG(YF) − 0.0647384706743 * LOG(YF(−1)) − 0.060915329265 @INNOV X0 0.569689062 |
---|

Source: Author’s computation from SMM.

LOG(XN) = 1.39999580363 * LOG(XN(−1)) − 0.324846615845 * LOG(XN(−2)) − 0.18190027768 * LOG(XN(−3)) + 0.0324272337436 * RER − 0.00569744857525 * LOG(YF) − 0.0076016998432 * LOG(YF(−1)) + 0.000218229060879 * LOG(YF(−2)) + 0.000281155770049 * LOG(YN) − 0.000541758175011 * LOG(YN(−1)) + 0.116489919066 * LOG(YN(−2)) − 0.0647384706743 @INNOV XN 0.8183643748 |
---|

Source: Author’s computation from SMM.

LOG(XS) = 1.39999580363 * LOG(XS(−1)) − 0.324846615845 * LOG(XS(−2)) − 0.18190027768 * LOG(XS(−3)) + 0.0324272337436 * LOG(Y) − 0.00569744857525 * LOG(X) − 0.0076016998432 * LOG(X(−1)) + 0.000218229060879 * LOG(X(−2)) + 0.000281155770049 * RER − 0.000541758175011 @INNOV XS 0.4567353275 |
---|

Source: Author’s computation from SMM.

relationships between lags of service exports and current exports. Also, the estimates for real exchange rate and domestic output were found to have positive signs in explaining the variations in service exports, but total value of lags of exports was found to have a negative response in explaining the variations in service exports. There are negative feedback innovations for service exports equation.

From economic theory, we modelled Nigeria’s imports to depend on lags of imports, domestic income, real exchange rates, domestic lending rates and reserves. The estimated equation in

According to [

LOG(M) = 1.39999580363 * LOG(M(−1)) − 0.324846615845 * LOG(M(−2)) − 0.18190027768 * LOG(YD) + 0.0324272337436 * LOG(YD(−1)) − 0.00569744857525 * LOG(YD(−2)) − 0.0076016998432 * RER + 0.000218229060879 * MLR + 0.000281155770049 * MLR(−1) − 0.000541758175011 * LOG(RES) + 0.116489919066 * LOG(RES(−1)) − 0.0647384706743 * LOG(RES(−2)) − 0.060915329265 * LOG(RES(−3)) + 0.544094953689 @INNOV M 0.4610127424 |
---|

Source: Author’s computation from SMM.

prices, external debt servicing, imports and domestic output. From the estimates of the equation in

Exchange rate is a key factor in Nigeria’s external sector because it reflects changes in both the domestic and foreign country macroeconomic fundamentals. Based on this, we modelled Nigeria’s exchange rate with lags of nominal exchange rates [

The sustained rise in FDI and its consequent importance on the Nigerian economy has been traced to economic reforms that allowed for foreign investments in telecommunication, construction and the oil and Gas sectors. Thus, we modelled FDI as a function of per capita GDP, non-oil exports and lags of FDI to capture inertia effects where current FDI flows are affected by previous FDI flows. From the estimated equation in

Just like the equation of FDI, foreign portfolio investments remain an important component of Nigeria’s capital and financial accounts. Due to reforms, especially

LOG(RES) = 1.39999580363 * LOG(RES(−1)) − 0.324846615845 * LOG(RES(−2)) − 0.18190027768 * LOG(RES(−3)) + 0.0324272337436 * RER − 0.00569744857525 * PO − 0.0076016998432 * EDS + 0.000218229060879 * EDS(−1) + 0.000281155770049 * LOG(M) − 0.000541758175011 * LOG(M(−1)) + 0.116489919066 * LOG(M(−2)) − 0.0647384706743 * LOG(Y) − 0.060915329265 * LOG(Y(−1)) + 0.544094953689 * LOG(Y(−2)) − 0.326038734525 @INNOV RES 2.817776461 |
---|

Source: Author’s computation from SMM.

NER = 1.39999580363 * NER(−1) − 0.324846615845 * NER(−2) − 0.18190027768 * NER(−3) + 0.0324272337436 * LOG(RES) − 0.00569744857525 * LOG(RMT) − 0.0076016998432 * LOG(RMT(−1)) + 0.000218229060879 * IRD + 0.000281155770049 * IRD(−1) − 0.000541758175011 * X_M + 0.116489919066 * CPI − 0.0647384706743 * CPI(−1) − 0.060915329265 * LOG(TGE) + 0.544094953689 * LOG(TGE(−1)) − 0.326038734525 * PO + 0.089757803001 * PO(−1) + 0.312221320928 * PO(−2) − 0.626094868597 @INNOV NER 5.996407199 |
---|

Source: Author’s computation from SMM.

LOG(FDI) = 1.39999580363 * LOG(FDI(−1)) − 0.324846615845 * LOG(FDI(−2)) − 0.18190027768 * LOG(PCGDP) + 0.0324272337436 * LOG(PCGDP(−1)) − 0.00569744857525 * LOG(PCGDP(−2)) − 0.0076016998432 * LOG(XN) + 0.000218229060879 * LOG(XN(−1)) + 0.000281155770049 * LOG(XN(−2)) − 0.000541758175011 * LOG(XN(−3)) + 0.116489919066 * LOG(XN(−4)) − 0.0647384706743 @INNOV FDI 1.695581558 |
---|

Source: Author’s computation from SMM.

those reforms targeted at equity participation, there has been increasing flows of portfolio investment to the Nigerian Economy [

The equation for foreign debt is based on theoretical explanations on the expected determinants of FDF [

LOGFPI = 1.39999580363 * LOGFPI(−1) − 0.324846615845 * LOGFPI(−2) − 0.18190027768 * LOGFPI(−3) + 0.0324272337436 * LOG(Y) − 0.00569744857525 * LOG(Y(−1)) − 0.0076016998432 * LOG(YF) + 0.000218229060879 * LOG(YF(−1)) + 0.000281155770049 * LOG(YF(−2)) − 0.000541758175011 * SMR + 0.116489919066 * INTF − 0.0647384706743 * INTF(−1) − 0.060915329265 * INTF(−2) + 0.544094953689 * NER − 0.326038734525 * NER(−1) + 0.089757803001 * NER(−2) + 0.312221320928 @INNOV FPI 116.0960255 |
---|

Source: Author’s computation From SMM.

LOGFDF = 1.39999580363 *LOGFDF(−1) − 0.324846615845 * LOGFDF(−2) − 0.18190027768 * LOGFDF(−3) + 0.0324272337436 * LOGFDF(−4) − 0.00569744857525 * LOG(M) − 0.0076016998432 * LOG(M(−1)) + 0.000218229060879 * LOG(M(−2)) + 0.000281155770049 * LOG(M(−3)) − 0.000541758175011 * LOG(M(−4)) + 0.116489919066 * IRD − 0.0647384706743 * IRD(−1) − 0.060915329265 * NER + 0.544094953689 * NER(−1) − 0.326038734525 * NER(−2) + 0.089757803001 * CR + 0.312221320928 * CR(−1) − 0.626094868597 * CR(−2) + 11.4807096219 * LOG(YF) − 27.5395211629 * LOG(YF(−1)) + 14.4583810946 * LOG(YF(−2)) + 35.8257864344 @INNOV FDF 85.73134111 |
---|

Source: Author’s computation From SMM.

imports, nominal exchange rates, foreign country output and country risk. However, it is difficult to interpret the direction of the effects due to the changes in causal effects at different lags. Despite this drawback, the estimated results clearly show that imports, nominal exchange rates, foreign country output and lags of FDF demonstrate strong influences over the variations of FDF. There are positive feedback innovations in the foreign debt flow equation.

Remittances have become very important to the Nigerian Economy, given that it has become a viable source of foreign exchange and revenue for the government. Thus, we modelled remittances as a function of United States of America’s output, nominal exchange rates and lags of remittances. From

Using the estimated behavioural equations, we introduce all the variables—endogenous and exogenous—and identities into the SMM and solve the SMM block using the Gauss-Seidel method. The first step in using the Gauss-Seidel method to study the effects of exogenous shocks on the external sector components is to compare actual and simulated within-sample values for all the endogenous variables. This comparison will provide an assessment of the

LOG(RMT) = 1.39999580363 * LOG(RMT(−1)) − 0.324846615845 * LOG(RMT(−2)) − 0.18190027768 * LOG(RMT(−3)) + 0.0324272337436 * LOG(YUS) − 0.00569744857525 * NER − 0.0076016998432 @INNOV RMT 1.071626221 |
---|

Source: Author’s computation From SMM.

SMM against the historical data on all endogenous variables and serve as a baseline for future simulations. To achieve this, the Gauss-Seidel method uses actual values for both exogenous and lagged endogenous variables in the SMM. This is necessary because it is the within-sample simulation of the external sector components that provides a test for the reliability of the SMM given that historical data already exist for these external sector components.

To initialise the SMM for estimating within samples, we simulate the data adjusting the dataset to allow for the inclusion of lagged variables. Since the data ranges from 1981Q1-2015Q4, we simulate for the baseline starting from 1982Q1-2015Q1. The simulation results of the SMM showed that the simulated results for the components of Nigeria’s external sector perform well (See

With satisfactory within-sample simulations for the SMM (See

To simulate the effects of oil price shocks on the external sector components, we

assume the following scenarios to simulate both positive and negative shocks of the exogenous variables within the period 2016Q1 to 2017Q4:

Benchmark Forecast:

1) Scenario 1: All variables in the model continue on their trend path, i.e. assume no changes in all variables.

Exogenous shocks

2) Scenario 2: We assume that oil prices will increase to 100 dollars per barrel (pre-2015) prices and remain at that level.

3) Scenario 3: We assume that oil prices will reduce to 35 dollars per barrel and remain at that level.

To effectively simulate these exogenous shock scenarios in the SMM, we will have to provide a benchmark forecast for the forecast and simulation range (2016Q1-2017Q4) and also for other exogenous variables in the SMM that do not comprise our shock variables (See

2016 | 2017 | |||||||
---|---|---|---|---|---|---|---|---|

Variable | Q1 | Q2 | Q3 | Q4 | Q1 | Q2 | Q3 | Q4 |

CA (N billion) | −3727.90 | −2641.70 | −1297.40 | 123.8 | 1485.20 | 2686.40 | 3659.10 | 4369.80 |

CF(N billion) | −2732.70 | −2735.80 | −2507.50 | −2143.60 | −1720.40 | −1301.70 | −931.7 | −636.7 |

FDF (N billion) | 908.9 | 856.3 | 797.6 | 753.6 | 732.8 | 737.1 | 764 | 808.9 |

FDI (N billion) | 299.1 | 323.4 | 350.4 | 374.3 | 398.6 | 422.9 | 447.6 | 472.4 |

FPI (N billion) | 589.7 | 793.4 | 979 | 1135.80 | 1257.30 | 1341.50 | 1390.20 | 1407.70 |

M (N billion) | 10,363.50 | 10,306.00 | 10,305.00 | 10,386.10 | 10,556.80 | 10,812.00 | 11,142.20 | 11,536.10 |

NER N | 227.5 | 242.5 | 257.4 | 272.1 | 286.8 | 301.5 | 316 | 330.4 |

RER N | 2.6 | 2.7 | 2.7 | 2.6 | 2.5 | 2.4 | 2.3 | 2.1 |

RES (N billion) | 6.45E+06 | 6.90E+06 | 7.39E+06 | 7.82E+06 | 8.22E+06 | 8.55E+06 | 8.83E+06 | 9.06E+06 |

RMT (N billion) | 1,318 | 484.07 | 203.16 | 90.223 | 42.601 | 21.318 | 11.238 | 6.2114 |

X (N billion) | 7411.08 | 8202.28 | 9401.36 | 10,835.70 | 12,381.30 | 13,927.00 | 15,389.00 | 16,713.90 |

X0 (N billion) | 6868.07 | 7637.47 | 8792.11 | 10,168.40 | 11,648.10 | 13,125.10 | 14,519.50 | 15,780.90 |

XN (N billion) | 543 | 564.8 | 609.2 | 667.2 | 733.1 | 801.9 | 869.5 | 933 |

XS (N billion) | 2796.90 | 2663.90 | 2583.10 | 2551.70 | 2583.80 | 2680.30 | 2839.10 | 3054.40 |

Source: Author’s computation. Forecast period from 2016Q1 to 2017Q4 assuming a no change in the trend of the endogenous variables. For reference, we call this Scenario 1. Deviations from this assumed path will detail the response to external sector components to exogenous shocks.

their projected paths for the periods 2016Q1-2017Q4. We first provide simulations for our benchmark for the external sector components for the period 2016Q1 to 2017Q4, on the assumption of no significant shifts in the trend of all the external sector components.

We present the SMM simulations for positive oil price shocks and then the results for negative oil price shocks. In simulating the effects of oil price shocks, we assumed that the price of oil will increase to its pre-2015 levels at 100 dollars per barrel and all the other exogenous variables will continue on their trend path and not deviate from it within the time of the simulated results.

The output of the simulation in

2016 | 2017 | ||||||||
---|---|---|---|---|---|---|---|---|---|

Q1 | Q2 | Q3 | Q4 | Q1 | Q2 | Q3 | Q4 | ||

CA | |||||||||

Scenario 2 (N billion) | −2345.21 | 960.6684 | 4177.91 | 7192.58 | Scenario 2 (N billion) | 9750.89 | 11,771.90 | 13,258.00 | 14,272.70 |

Scenario 1 (N billion) | −3727.90 | −2641.70 | −1297.40 | 123.8 | Scenario 1 (N billion) | 1485.20 | 2686.40 | 3659.10 | 4369.80 |

Deviation (N billion) | 1382.60 | 3602.40 | 5475.30 | 7068.80 | Deviation (N billion) | 8265.70 | 9085.50 | 9598.90 | 9902.90 |

% Deviation | −37.1 | −136.4 | −422 | 5711.30 | % Deviation | 556.5 | 338.2 | 262.3 | 226.6 |

CF | |||||||||

Scenario 2 (N billion) | −2748.70 | −2792.80 | −2583.60 | −2,238.20 | Scenario 2 (N billion) | −1823.60 | −1407.10 | −1033.00 | −729.5 |

Scenario 1 (N billion) | −2732.70 | −2735.80 | −2507.50 | −2143.60 | Scenario 1 (N billion) | −1720.40 | −1301.70 | −931.7 | −636.7 |

Deviation (N billion) | −16 | −57 | −76.1 | −94.6 | Deviation (N billion) | −103.1 | −105.4 | −101.3 | −92.8 |

% Deviation | 0.6 | 2.1 | 3 | 4.4 | % Deviation | 6 | 8.1 | 10.9 | 14.6 |

FDF | |||||||||

Scenario 2 (N billion) | 893.1 | 804.9 | 732.7 | 670.7 | Scenario 2 (N billion) | 635.4 | 623 | 632.7 | 659.5 |

Scenario 1 (N billion) | 908.9 | 856.3 | 797.6 | 753.6 | Scenario 1 (N billion) | 732.8 | 737.1 | 764 | 808.9 |

Deviation (N billion) | −15.8 | −51.4 | −64.9 | −82.9 | Deviation (N billion) | −97.4 | −114.1 | −131.3 | −149.4 |

% Deviation | −1.7 | −6 | −8.1 | −11 | % Deviation | −13.3 | −15.5 | −17.2 | −18.5 |

FDI | |||||||||

Scenario 2 (N billion) | 299.1 | 323.5 | 350.8 | 374.9 | Scenario 2 (N billion) | 399.5 | 424.1 | 449.1 | 474.3 |

Scenario 1 (N billion) | 299.1 | 323.4 | 350.4 | 374.3 | Scenario 1 (N billion) | 398.6 | 422.9 | 447.6 | 472.4 |

Deviation (N billion) | 0.03 | 0.16 | 0.36 | 0.62 | Deviation (N billion) | 0.92 | 1.23 | 1.54 | 1.84 |

% Deviation | 0.01 | 0.05 | 0.1 | 0.17 | % Deviation | 0.23 | 0.29 | 0.34 | 0.39 |

FPI | |||||||||

Scenario 2 (N billion) | 573.7 | 736.3 | 902.5 | 1040.50 | Scenario 2 (N billion) | 1153.20 | 1235.00 | 1287.40 | 1313.00 |

Scenario 1 (N billion) | 589.7 | 793.4 | 979 | 1135.80 | Scenario 1 (N billion) | 1257.30 | 1341.50 | 1390.20 | 1407.70 |

Deviation (N billion) | −16 | −57.1 | −76.5 | −95.3 | Deviation (N billion) | −104 | −106.6 | −102.9 | −94.7 |

% Deviation | −2.72 | −7.2 | −7.81 | −8.39 | % Deviation | −8.28 | −7.95 | −7.4 | −6.73 |

M | |||||||||

Scenario 2 (N billion) | 10,142.60 | 9770.82 | 9375.14 | 9018.20 | Scenario 2 (N billion) | 8720.15 | 8486.29 | 8312.27 | 8191.05 |

Scenario 1 (N billion) | 10,363.50 | 10,306.00 | 10,305.00 | 10,386.10 | Scenario 1 (N billion) | 10,556.80 | 10,812.00 | 11,142.20 | 11,536.10 |

Deviation (N billion) | −220.9 | −535.2 | −929.9 | −1367.90 | Deviation (N billion) | −1836.60 | −2325.70 | −2829.90 | −3345.10 |

% Deviation | −2.1 | −5.2 | −9 | −13.2 | % Deviation | −17.4 | −21.5 | −25.4 | −29 |

NER | |||||||||

Scenario 2 (N) | 224.2 | 230.5 | 241.2 | 252.6 | Scenario 2 (N) | 264.9 | 277.7 | 290.7 | 304 |

Scenario 1 (N) | 227.5 | 242.5 | 257.4 | 272.1 | Scenario 1 (N) | 286.8 | 301.5 | 316 | 330.4 |

Deviation | −3.3 | −12 | −16.1 | −19.6 | Deviation | −21.9 | −23.8 | −25.2 | −26.4 |

% Deviation | −1.45 | −4.93 | −6.27 | −7.19 | % Deviation | −7.65 | −7.89 | −7.99 | −8 |

RER | |||||||||
---|---|---|---|---|---|---|---|---|---|

Scenario 2 (N) | 2.5 | 2.5 | 2.5 | 2.4 | Scenario 2 (N) | 2.4 | 2.2 | 2.1 | 1.9 |

Scenario 1 (N) | 2.6 | 2.7 | 2.7 | 2.6 | Scenario 1 (N) | 2.5 | 2.4 | 2.3 | 2.1 |

Deviation | −0.04 | −0.13 | −0.17 | −0.19 | Deviation | −0.19 | −0.19 | −0.18 | −0.17 |

% Deviation | −1.45 | −4.93 | −6.27 | −7.19 | % Deviation | −7.65 | −7.89 | −7.99 | −8 |

RES | |||||||||

Scenario 2 (N billion) | 5.33E+06 | 4.87E+06 | 4.45E+06 | 4.12E+06 | Scenario 2 (N billion) | 3.85E+06 | 3.63E+06 | 3.44E+06 | 3.27E+06 |

Scenario 1 (N billion) | 6.45E+06 | 6.90E+06 | 7.39E+06 | 7.82E+06 | Scenario 1 (N billion) | 8.22E+06 | 8.55E+06 | 8.83E+06 | 9.06E+06 |

Deviation (N billion) | −1.12E+06 | −2.03E+06 | −2.94E+06 | −3.70E+06 | Deviation (N billion) | −4.37E+06 | −4.93E+06 | −5.40E+06 | −5.79E+06 |

% Deviation | −17.4 | −29.5 | −39.8 | −47.3 | % Deviation | −53.2 | −57.6 | −61.1 | −63.9 |

RMT | |||||||||

Scenario 2 (N billion) | 1,322 | 491.4 | 209.11 | 94.302 | Scenario 2 (N billion) | 45.243 | 23.007 | 12.321 | 6.9155 |

Scenario 1 (N billion) | 1,318 | 484.07 | 203.16 | 90.223 | Scenario 1 (N billion) | 42.601 | 21.318 | 11.238 | 6.2114 |

Deviation (N billion) | 4.47 | 7.33 | 5.95 | 4.08 | Deviation (N billion) | 2.64 | 1.69 | 1.08 | 0.7 |

% Deviation | 0.3 | 1.5 | 2.9 | 4.5 | % Deviation | 6.2 | 7.9 | 9.6 | 11.3 |

X | |||||||||

Scenario 2 (N billion) | 8829.95 | 11,745.70 | 14,545.10 | 17,284.10 | Scenario 2 (N billion) | 19,714.40 | 21,766.10 | 23,422.10 | 24,720.90 |

Scenario 1 (N billion) | 7411.08 | 8202.28 | 9401.36 | 10,835.70 | Scenario 1 (N billion) | 12,381.30 | 13,927.00 | 15,389.00 | 16,713.90 |

Deviation (N billion) | 1418.90 | 3543.40 | 5143.80 | 6448.40 | Deviation (N billion) | 7333.10 | 7839.10 | 8033.10 | 8007.00 |

% Deviation | 19.1 | 43.2 | 54.7 | 59.5 | % Deviation | 59.2 | 56.3 | 52.2 | 47.9 |

X0 | |||||||||

Scenario 2 (N billion) | 8286.86 | 11,180.50 | 13,935.00 | 16,615.30 | Scenario 2 (N billion) | 18,978.80 | 20,960.90 | 22,548.40 | 23,782.90 |

Scenario 1 (N billion) | 6868.07 | 7637.47 | 8792.11 | 10,168.40 | Scenario 1 (N billion) | 11,648.10 | 13,125.10 | 14,519.50 | 15,780.90 |

Deviation (N billion) | 1418.80 | 3543.00 | 5142.90 | 6446.80 | Deviation (N billion) | 7330.70 | 7835.80 | 8028.90 | 8002.00 |

% Deviation | 20.7 | 46.4 | 58.5 | 63.4 | % Deviation | 62.9 | 59.7 | 55.3 | 50.7 |

XN | |||||||||

Scenario 2 (N billion) | 543.1 | 565.2 | 610.1 | 668.8 | Scenario 2 (N billion) | 735.6 | 805.2 | 873.7 | 938 |

Scenario 1 (N billion) | 543 | 564.8 | 609.2 | 667.2 | Scenario 1 (N billion) | 733.1 | 801.9 | 869.5 | 933 |

Deviation (N billion) | 0.08 | 0.38 | 0.89 | 1.6 | Deviation (N billion) | 2.44 | 3.34 | 4.22 | 4.98 |

% Deviation | 0.01 | 0.07 | 0.15 | 0.24 | % Deviation | 0.33 | 0.42 | 0.49 | 0.53 |

XS | |||||||||

Scenario 2 (N billion) | 3054.00 | 3140.20 | 3181.50 | 3299.20 | Scenario 2 (N billion) | 3487.90 | 3759.60 | 4103.20 | 4503.60 |

Scenario 1 (N billion) | 2796.90 | 2663.90 | 2583.10 | 2551.70 | Scenario 1 (N billion) | 2583.80 | 2680.30 | 2839.10 | 3054.40 |

Deviation (N billion) | 257.1 | 476.2 | 598.4 | 747.5 | Deviation (N billion) | 904.1 | 1079.30 | 1264.10 | 1449.20 |

% Deviation | 9.2 | 17.9 | 23.2 | 29.3 | % Deviation | 35 | 40.3 | 44.5 | 47.4 |

Source: Author’s computation. Scenario 2 represents responses of external sector components to an oil price increase, while Scenario 1 represents the benchmark forecast. Underneath each variable’s response is the deviation and percentage deviation of the responses to an oil price increase from the benchmark forecast.

1) Current account balance continuously increases during the forecast period by 37% and above 200% in 2016Q1 and 2017Q4 respectively.

2) Capital financial flows increase by 0.6% to 14.6% in 2016Q1 and 2017Q4 respectively.

3) Foreign debt flow decrease by 1.7% to 18.5% in 2016Q1 and 2017Q4 respectively.

4) Foreign direct investment increase by 0.01% to 0.39% in 2016Q1 and 2017Q4 respectively.

5) Foreign portfolio investments decrease by 2.72% to 6.73% in 2016Q1 and 2017Q4 respectively.

6) Imports decrease by 2.1% to 29% in 2016Q1 and 2017Q4 respectively.

7) Nominal exchange rates appreciate from $1:N227 to $1:N224.2 by 2016Q1 and from $1:N330.4 to $1:N304 by 2017Q4.

8) Real exchange rates also appreciates by 1.45% to 8% in 2016Q1 and 2017Q4 respectively.

9) Reserves decrease by 17.4% to 63.9% in 2016Q1 and 2017Q4 respectively.

10) Remittances increase by 0.3% to 11.3% in 2016Q1 and 2017Q4 respectively.

11) Total exports increase by 19.1% to 47.9% in 2016Q1 and 2017Q4 respectively. Oil exports also increase by 20.7% to 50.7% in 2016Q1 and 2017Q4 respectively, while service exports also increase by 9.2% to 47.4% in 2016Q1 and 2017Q4 respectively. However, non-oil exports increased marginally by 0.01% to 0.53% in 2016Q1 and 2017Q4 respectively.

With the increase in oil prices—as captured by the positive oil price shock, Nigeria gains more in foreign exchange earnings which ultimately significantly increases her reserves and inflow of remittances. With the increases in reserves, Nigeria will be able to cover more of her imports putting less pressure on the Naira and therefore the currency appreciates in value. The simulation results show a reduction in nominal exchange rates showing that positive oil price shocks will lead to the appreciation of the Naira but imports did not increase as expected.

As expected, there was a significant increase in reserves and remittances, which seemed to be a permanent change from the benchmark. Similarly, total exports, service exports and oil exports behaved similarly with respect to their responses. Relative to total exports, service exports and oil exports, non-oil exports responded with just a marginal increase (See

In simulating responses to a negative oil price shock, we assumed the world oil price will drop to 35 dollars per barrel and all the other exogenous variables will continue on their trend path and not deviate from it within the time of the simulated results. The output of the simulation in

rates, reserves and remittances. A closer look at the simulation results and the percentage deviations show with a decrease in oil prices to $35, the following responses of external sector components were elicited;

1) Current account balance continuously decreases during the forecast period by 5% and above 6% in 2016Q1 and 2017Q4 respectively.

2) Capital financial flows increase by 0.09% to 4.48% in 2016Q1 and 2017Q4 respectively.

3) Foreign debt flow increase by 0.27% to 4.93% in 2016Q1 and 2017Q4 respectively.

4) Foreign direct investment decrease by 0.002% to 0.089% in 2016Q1 and 2017Q4 respectively.

5) Foreign portfolio investments increase by 0.43% to 2.22% in 2016Q1 and 2017Q4 respectively.

6) Imports increase by 0.34% to 8.84% in 2016Q1 and 2017Q4 respectively.

7) Nominal exchange rates depreciate from $1:N227 to $1:N228.1 by 2016Q1 and from $1:N330.4 to $1:N338.1 by 2017Q4.

2016 | 2017 | ||||||||
---|---|---|---|---|---|---|---|---|---|

Q1 | Q2 | Q3 | Q4 | Q1 | Q2 | Q3 | Q4 | ||

CA | |||||||||

Scenario 3 (N billion) | −3923.70 | −3144.80 | −2126.90 | −1072.60 | Scenario 3 (N billion) | −109.4 | 671.5 | 1209.70 | 1476.00 |

Scenario 1 (N billion) | −3727.90 | −2641.70 | −1297.40 | 123.8 | Scenario 1 (N billion) | 1485.20 | 2686.40 | 3659.10 | 4369.80 |

Deviation (N billion) | −195.9 | −503.1 | −829.5 | −1196.40 | Deviation (N billion) | −1594.60 | −2014.90 | −2449.40 | −2893.70 |

% Deviation | 5.3 | 19 | 63.9 | −966.6 | % Deviation | −107.4 | −75 | −66.9 | −66.2 |

CF | |||||||||

Scenario 3 (N billion) | −2730.20 | −2726.40 | −2493.40 | −2124.20 | Scenario 3 (N billion) | −1696.90 | −1274.80 | −902.4 | −605.9 |

Scenario 1 (N billion) | −2732.70 | −2735.80 | −2,507.50 | −2143.60 | Scenario 1 (N billion) | −1720.40 | −1301.70 | −931.7 | −636.7 |

Deviation (N billion) | 2.5 | 9.4 | 14.1 | 19.3 | Deviation (N billion) | 23.5 | 26.9 | 29.3 | 30.8 |

% Deviation | −0.09 | −0.34 | −0.56 | −0.9 | % Deviation | −1.37 | −2.07 | −3.14 | −4.84 |

FDF | |||||||||

Scenario 3 (N billion) | 911.3 | 864.8 | 809.8 | 770.6 | Scenario 3 (N billion) | 754.5 | 764.3 | 797.2 | 848.8 |

Scenario 1 (N billion) | 908.9 | 856.3 | 797.6 | 753.6 | Scenario 1 (N billion) | 732.8 | 737.1 | 764 | 808.9 |

Deviation (N billion) | 2.5 | 8.5 | 12.2 | 17 | Deviation (N billion) | 21.7 | 27.2 | 33.3 | 39.9 |

% Deviation | 0.27 | 1 | 1.53 | 2.25 | % Deviation | 2.97 | 3.69 | 4.35 | 4.93 |

FDI | |||||||||

Scenario 3 (N billion) | 299.1 | 323.3 | 350.3 | 374.1 | Scenario 3 (N billion) | 398.4 | 422.6 | 447.2 | 472 |

Scenario 1 (N billion) | 299.1 | 323.4 | 350.4 | 374.3 | Scenario 1 (N billion) | 398.6 | 422.9 | 447.6 | 472.4 |

Deviation (N billion) | −0.01 | −0.03 | −0.06 | −0.11 | Deviation (N billion) | −0.18 | −0.25 | −0.34 | −0.42 |

% Deviation | −0.002 | −0.008 | −0.018 | −0.031 | % Deviation | −0.045 | −0.06 | −0.075 | −0.089 |

FPI | |||||||||

Scenario 3 (N billion) | 592.2 | 802.9 | 993.2 | 1155.20 | Scenario 3 (N billion) | 1281.00 | 1368.70 | 1419.90 | 1439.00 |

Scenario 1 (N billion) | 589.7 | 793.4 | 979 | 1135.80 | Scenario 1 (N billion) | 1257.30 | 1341.50 | 1390.20 | 1407.70 |

Deviation (N billion) | 2.5 | 9.4 | 14.2 | 19.5 | Deviation (N billion) | 23.7 | 27.1 | 29.6 | 31.3 |

% Deviation | 0.43 | 1.19 | 1.45 | 1.71 | % Deviation | 1.88 | 2.02 | 2.13 | 2.22 |

M | |||||||||

Scenario 3 (N billion) | 10,398.50 | 10,399.20 | 10,482.70 | 10,673.20 | Scenario 3 (N billion) | 10,980.50 | 11,401.60 | 11,929.50 | 12,555.90 |

Scenario 1 (N billion) | 10,363.50 | 10,306.00 | 10,305.00 | 10,386.10 | Scenario 1 (N billion) | 10,556.80 | 10,812.00 | 11,142.20 | 11,536.10 |

Deviation (N billion) | 35 | 93.2 | 177.6 | 287.1 | Deviation (N billion) | 423.7 | 589.5 | 787.3 | 1,019.70 |

% Deviation | 0.34 | 0.9 | 1.72 | 2.76 | % Deviation | 4.01 | 5.45 | 7.07 | 8.84 |

NER | |||||||||

Scenario 3 (N) | 228.1 | 244.4 | 260.3 | 276.2 | Scenario 3 (N) | 291.8 | 307.4 | 322.8 | 338.1 |

Scenario 1 (N) | 227.5 | 242.5 | 257.4 | 272.1 | Scenario 1 (N) | 286.8 | 301.5 | 316 | 330.4 |

Deviation | 0.52 | 1.97 | 2.99 | 4.02 | Deviation | 4.97 | 5.91 | 6.83 | 7.73 |

% Deviation | 0.23 | 0.81 | 1.16 | 1.48 | % Deviation | 1.73 | 1.96 | 2.16 | 2.34 |

RER | |||||||||
---|---|---|---|---|---|---|---|---|---|

Scenario 3 (N) | 2.6 | 2.7 | 2.7 | 2.7 | Scenario 3 (N) | 2.6 | 2.5 | 2.3 | 2.2 |

Scenario 1 (N) | 2.6 | 2.7 | 2.7 | 2.6 | Scenario 1 (N) | 2.5 | 2.4 | 2.3 | 2.1 |

Deviation | 0.006 | 0.022 | 0.031 | 0.039 | Deviation | 0.044 | 0.048 | 0.049 | 0.049 |

% Deviation | 0.23 | 0.81 | 1.16 | 1.48 | % Deviation | 1.73 | 1.96 | 2.16 | 2.34 |

RES | |||||||||

Scenario 3 (N billion) | 7.00E+06 | 7.00E+06 | 8.00E+06 | 9.00E+06 | Scenario 3 (N billion) | 1.00E+07 | 1.00E+07 | 1.00E+07 | 1.00E+07 |

Scenario 1 (N billion) | 6.45E+06 | 6.90E+06 | 7.39E+06 | 7.82E+06 | Scenario 1 (N billion) | 8.22E+06 | 8.55E+06 | 8.83E+06 | 9.06E+06 |

Deviation (N billion) | 195,758 | 430,681 | 743,810 | 1.11E+06 | Deviation (N billion) | 1.52E+06 | 1.97E+06 | 2.46E+06 | 2.98E+06 |

% Deviation | 3 | 6.2 | 10.1 | 14.1 | % Deviation | 18.5 | 23.1 | 27.9 | 32.9 |

RMT | |||||||||

Scenario 3 (N billion) | 1317 | 482.88 | 202.13 | 89.472 | Scenario 3 (N billion) | 42.085 | 20.969 | 11.002 | 6.05 |

Scenario 1 (N billion) | 1318 | 484.07 | 203.16 | 90.223 | Scenario 1 (N billion) | 42.601 | 21.318 | 11.238 | 6.2114 |

Deviation (N billion) | −0.7 | −1.19 | −1.03 | −0.75 | Deviation (N billion) | −0.52 | −0.35 | −0.24 | −0.16 |

% Deviation | −0.05 | −0.25 | −0.51 | −0.83 | % Deviation | −1.21 | −1.64 | −2.1 | −2.6 |

X | |||||||||

Scenario 3 (N billion) | 7212.28 | 7718.69 | 8648.75 | 9793.58 | Scenario 3 (N billion) | 11,041.40 | 12,289.90 | 13,465.50 | 14,521.40 |

Scenario 1 (N billion) | 7411.08 | 8202.28 | 9401.36 | 10835.70 | Scenario 1 (N billion) | 12,381.30 | 13,927.00 | 15,389.00 | 16,713.90 |

Deviation (N billion) | −198.8 | −483.6 | −752.6 | −1042.10 | Deviation (N billion) | −1339.90 | −1637.10 | −1923.50 | −2192.50 |

% Deviation | −2.7 | −5.9 | −8 | −9.6 | % Deviation | −10.8 | −11.8 | −12.5 | −13.1 |

X0 | |||||||||

Scenario 3 (N billion) | 6669.28 | 7153.95 | 8039.65 | 9126.63 | Scenario 3 (N billion) | 10,308.70 | 11,488.70 | 12,596.90 | 13,589.60 |

Scenario 1 (N billion) | 6868.07 | 7637.47 | 8792.11 | 10,168.40 | Scenario 1 (N billion) | 11,648.10 | 13,125.10 | 14,519.50 | 15,780.90 |

Deviation (N billion) | −198.8 | −483.5 | −752.5 | −1041.80 | Deviation (N billion) | −1339.40 | −1636.40 | −1922.60 | −2191.30 |

% Deviation | −2.9 | −6.3 | −8.6 | −10.2 | % Deviation | −11.5 | −12.5 | −13.2 | −13.9 |

XN | |||||||||

Scenario 3 (N billion) | 543 | 564.7 | 609.1 | 666.9 | Scenario 3 (N billion) | 732.7 | 801.2 | 868.6 | 931.8 |

Scenario 1 (N billion) | 543 | 564.8 | 609.2 | 667.2 | Scenario 1 (N billion) | 733.1 | 801.9 | 869.5 | 933 |

Deviation (N billion) | −0.01 | −0.06 | −0.15 | −0.29 | Deviation (N billion) | −0.47 | −0.69 | −0.92 | −1.15 |

% Deviation | 0 | −0.01 | −0.03 | −0.04 | % Deviation | −0.06 | −0.09 | −0.11 | −0.12 |

XS | |||||||||

Scenario 3 (N billion) | 2758.90 | 2590.20 | 2482.40 | 2418.80 | Scenario 3 (N billion) | 2414.80 | 2468.50 | 2577.60 | 2736.00 |

Scenario 1 (N billion) | 2796.90 | 2663.90 | 2583.10 | 2551.70 | Scenario 1 (N billion) | 2583.80 | 2680.30 | 2839.10 | 3054.40 |

Deviation (N billion) | −37.9 | −73.7 | −100.7 | −132.8 | Deviation (N billion) | −169.1 | −211.8 | −261.5 | −318.4 |

% Deviation | −1.4 | −2.8 | −3.9 | −5.2 | % Deviation | −6.5 | −7.9 | −9.2 | −10.4 |

Source: Author’s computation. Scenario 3 represents responses of external sector components to an oil price decrease, while Scenario 1 represents the benchmark forecast. Underneath each variable’s response is the deviation and percentage deviation of the responses to an oil price increase from the benchmark forecast.

8) Real exchange rates also depreciate by 0.23% to 2.34% in 2016Q1 and 2017Q4 respectively.

9) Reserves increase by 3% to 32.9% in 2016Q1 and 2017Q4 respectively.

10) Remittances decrease by 0.05% to 2.6% in 2016Q1 and 2017Q4 respectively.

11) Total exports decrease by 2.7% to 13.1% in 2016Q1 and 2017Q4 respectively. Oil exports also decrease by 2.9% to 13.9% in 2016Q1 and 2017Q4 respectively, while service exports also decrease by 1.4% to 10.4% in 2016Q1 and 2017Q4 respectively. However, non-oil exports decrease marginally by 0.0%, 0.01% to 0.12% in 2016Q1 and 2017Q4 respectively.

With the decrease in oil prices—as captured by the negative oil price shock, Nigeria does not gains more in foreign exchange earnings which ultimately significantly decreases her reserves and inflow of remittances. With the decrease in reserves, Nigeria will be able to cover less of her imports putting more pressure on the Naira and therefore the currency depreciates in value. The simulation results show an increase in nominal exchange rates, implying that negative oil price shocks will lead to the depreciation of the Naira (see

The simulation results from this study point to important policy implications for the Nigerian external sector. First, world oil price shocks as captured by the negative and positive shocks to oil prices significantly affects the variables in the external sector. Increases in world oil price lead to increases in capital financial flows, a reduction in foreign debt flows, while oil price reductions significantly lead to increases in imports, depreciation in nominal exchange rates, increases in reserves, reductions in remittances, and increases in capital financial flows. This reflects the over dependence of the Nigerian economy on crude oil, and the justifiable need to diversify the Nigerian economy away from the oil sector.

Also, positive oil price shocks were shown to bring about decreasing responses in capital financial flows, foreign debt flow, foreign portfolio investments, imports, nominal exchange rates, real exchange rates and reserves. However, positive oil price shocks caused substantial increases in total exports, oil exports, non-oil exports, and service exports. The simulations also show that negative oil price brought about decreasing responses in the current account balance, total exports, oil exports, and service exports. However, negative oil price shocks brought about increases in capital financial flows, foreign debt flow, foreign portfolio investments, imports, nominal exchange rates, real exchange rates, reserves and remittances.

Increases in oil prices decrease Nigeria’s reserves while increasing the flow of remittances. A negative oil price shock is supposed to have a direct opposite response from the external sector components as compared to the response to a positive oil price shock. With decreases in oil prices, Nigeria loses foreign exchange earnings, which significantly reduces her reserves and remittances

inflows. Since Nigeria can now cover less of her imports, we expect imports to reduce and with the loss in foreign exchange we expect the Naira to depreciate in value. The simulated results show that the Naira actually depreciates in value as the nominal exchange rate increases, but imports did not reduce as expected.

Due to the nature of the methodology used—simulations, based on assumptions—making inferential statements on the findings of this study is not sacrosanct. From the stated objectives of this study and the dearth in literature on studies that specifically focus on simulating external sector component’s responses to exogenous shocks, making comparative assertions on external sector responses may be challenging. However, the simulated responses are discussed based on a priori expected responses that are hinged on economic intuition and economic theory. Likewise, the findings in [

The output of the simulation showed that a positive oil price shock brought about significant decreases in capital financial flows, foreign debt flow, foreign portfolio investments, imports, nominal exchange rates, real exchange rates and reserves. However, positive oil price shocks caused substantial increases in total exports, oil exports, non-oil exports, and service exports. It must be stated clearly that the responses of non-oil exports and remittances are very small relative to other responses from other external sector components.

With the increase in oil prices—as captured by the positive oil price shock, Nigeria gains more in foreign exchange earnings which ultimately significantly increases her reserves and inflow of remittances. With the increases in reserves, Nigeria will be able to cover more of her imports putting less pressure on the Naira and therefore the currency appreciates in value. The simulation results showed a reduction in nominal exchange rates showing that positive oil price shocks will lead to the appreciation of the Naira but imports did not increase as expected. These findings are in line with those of [

As expected, there was a significant increase in reserves and remittances, which seemed to be a permanent change from the benchmark. This increase in reserves and remittances as argued by [

For negative oil price shock, the output of the simulation showed that a negative oil price shock brought about significant decreases in current account balance, total exports, oil exports, and service exports. However, negative oil price shocks also brought about increases in capital financial flows, foreign debt flow, foreign portfolio investments, imports, nominal exchange rates, real exchange rates, reserves and remittances. The simulated results clearly show some form of symmetry in responses to increases and decreases in oil prices. Therefore, it is intuitive to assume that, explanations on the nature of elicited responses for negative oil price shocks would be the direct opposite of those of a positive oil price shock. This conclusion is hinged on the findings of [

With the decrease in oil prices—as captured by the negative oil price shock, Nigeria does not gain more in foreign exchange earnings which ultimately significantly decreases her reserves and inflow of remittances. With the decrease in reserves, Nigeria will be able to cover less of her imports putting more pressure on the Naira and therefore the currency depreciates in value. The simulation results show an increase in nominal exchange rates, implying that negative oil price shocks will lead to the depreciation of the Naira. These postulates follow those as reported in the findings of [

There is a need for government to proactively diversify the economy in order to restructure the components of exports. The unexpected response of imports to both positive and negative shocks to oil prices reflects the import dependence of the Nigerian economy. The composition and nature of Nigerian imports are mainly influenced by domestic factors not external factors. This is a reflection of Nigeria’s reliance on oil sector and a need for diversification. Also, there is a need for further research to incorporate domestic factors that determine Nigeria’s demand for imports in order to determine the inter relationships between external shocks, imports and domestic macroeconomic variables. The simulation results show that nominal exchange rates policy in response to shocks to oil prices can be used to encourage inflow of remittances and building up Nigeria’s reserves.

The reliance of the Nigerian economy on the proceeds from oil exploration has redirected resources and policy reference from other sectors to principally, the oil sector. This has made the oil sector the main source of foreign exchange, and also an important component of the Nigerian economy as it has both indirect and direct links to Nigeria’s reserves, export composition, the capacity to cover her imports, and exchange rates. Despite most studies focusing on the impact of oil price shock, there is detailed evidence in the literature that oil production shocks and output shocks of trade partners do matter in determining both external sector objectives and real domestic macroeconomic objectives.

In trying to elicit responses of external sector components to oil price shocks, this study employed a Structural Macroeconomic Model (SMM) which consisted of ten behavioural equations—which described the relationship between endogenous variables and exogenous variables in the SMM—and four identities which we expect will hold true in reality. Using the Gauss-Seidel technique, we simulated an eight period ahead forecast response of all the endogenous variables in the SMM and compared the responses to a baseline response. The baseline response was simulated under the assumption that all the variables will continue on their trend path without any significant change. The simulated responses for this study found overwhelming evidence that oil price shocks do have substantial effects on Nigeria’s external sector components.

The author declares no conflicts of interest regarding the publication of this paper.

Onyimadu, C. (2019) External Sector Responses to Oil Price Shocks: A Structural System Model for Nigeria. Theoretical Economics Letters, 9, 2885-2913. https://doi.org/10.4236/tel.2019.98180