External Shocks and Their Transmission Channels in Nigeria: A Dynamic Stochastic General Equilibrium Approach

Abstract

The study examines the sources of external shocks and investigates their transmission channels in Nigeria using the trade-weighted variables from the country’s five top trading partners. Based on the assumption of the small open economy model, the study adopts the New Keynesian Dynamic Stochastic General Equilibrium Model on quarterly data between 1981 and 2018 using the Bayesian estimation technique. Findings from the study reveal that external shocks have a temporary and short-lived effect on the Nigerian economy. In addition, the article shows that oil price, foreign output, and foreign inflation shock have positive impacts on output gap and inflation, while the impact of foreign interest rate shock on the output gap and inflation is negative and not significant. The study also reveals that external shocks collectively explain 86% and 39%of total fluctuations in the output gap and inflation, respectively. Lastly, the study finds that external shocks transmit to the Nigerian economy via different channels. The study, therefore, concludes that terms of trade and exchange rate channels are the dominant transmitters of external shocks in Nigeria. Based on the findings from the study, important policy implications are highlighted.

Keywords

External shocks output gap inflation dynamic general equilibrium model

Introduction

The role of external shocks in propagating economic fluctuations, especially, in Emerging Economies (EEs) has received major attention in recent times. This is partly because of the current wave of globalization that has reduced the entire world into a global village. Scholars have argued that external shocks account for large fluctuations in a typical small open economy (Chileshe et al., 2018; Doojav & Luvsannyam, 2019; Muhamad & Said, 2016; Oyelami & Olomola, 2016). Nigeria, as an EE, is not invulnerable to these global changes.

Meanwhile, empirical studies have argued that macroeconomic variables in EEs are highly volatile and vulnerable to unpredictable movements in the international variables. This susceptibility is more pronounced in Nigeria because of her reliance on crude oil as a major source of revenue. For instance, following the Oil Crisis of 2014, the price of crude oil in the world market fell drastically from $112 per barrel in 2014 to $48 early in 2015, with an attendant consequence on the Nigerian economy that depends majorly on crude oil export as its major source of export earnings (Hou et al., 2015). Consequently, economic growth fell from 6.31% in 2014 to 2.65% at the end of the crisis in 2015 (World Development Indicators [WDIs], 2018). Besides, the global financial crisis of 2008 has again generated interest in the vulnerability of emerging market economies to financial shocks. Apart from the adverse effect of positive financial shock on the balance of payment, it (increase in foreign interest rate) increases the cost of servicing foreign debt, with a major impact on the debt repayment ability of debtors’ nations. The financial shock is particularly relevant to Nigeria with its huge debt profile. In 2018, 23% of the budget was allocated for debt servicing in the country based on its outstanding external debt balance of $18.9 billion as of December 2017 (Debt Management Office, 2018). Therefore, any shock that affects the economy’s debt position would significantly affect the country’s macroeconomic performance. In addition, after the emergence of the Covid-19 pandemic in 2020, the price of crude oil at the international market plummeted significantly from $67.31 per barrel in December 2019 to $18.38 in April 2020, indicating a 266% drop in crude prices between the two periods (Energy Information Administration [EIA], 2021). This led to a major setback to the Nigerian economy, with economic growth declining from 2.55% in Q4 of 2019 to –6.1% in Q2 of 2021, thus, reflecting on the susceptibility of the economy to fluctuation in the global oil prices.

Another distinguishing feature of less developed countries (LDCs), including Nigeria, is that their trade is dominated by the export of primary commodities and the import of industrial goods. Historically, the prices of these primary commodities are volatile and are not determined in the domestic market, and this poses a serious concern for the LDCs’ trade position and their balance of payments (Chileshe et al., 2018; Thirwall, 2003). Besides, most of the manufacturing companies in Nigeria depend on intermediate and capital goods as inputs in the production process. However, these capital goods are often imported from abroad, thereby putting additional pressure on the country’s exchange rate. A critical analysis of the country’s exchange rate shows a consistent depreciation from 1986 to 2020. For example, the exchange rate of Naira to Dollar rose from N2.02 in 1986 to N21.89 in 1996, increased further to N128.65 in 2006, and again to N253.49 in 2016 and 306.95 in 2019 (Central Bank of Nigeria [CBN], 2019). Consequently, the continuous depreciation in the Naira generates inflationary pressure and as such distorts macroeconomics performance in the country (Chileshe et al. 2018; Monfared & Akin, 2017;). This suggests that external shocks are major drivers of macroeconomic fluctuations in Nigeria.

However, despite the significant effect of external shocks on small open economies, the issue has not been given adequate attention in developing economies and particularly in Nigeria. Meanwhile, the findings from empirical studies on the sources of external shocks and their transmission channel(s) have produced mixed results and inconclusive findings. A number of these studies fail to identify the specific channel(s) through which these shocks transmit to a small open economy. Besides, most of the studies in this area have focused more on the advanced and Asian economies. For instance, studies such as Allegret et al. (2012), Belhedi et al. (2015), Muhamad and Said (2016), Chileshe et al. (2018) and Raghavan and Athanasopoulos (2018) argue that external shocks, particularly from commodity price, foreign output, and oil price fluctuations, transmit to EEs through the trade and exchange rate channels. These studies claim that terms-of-trade shocks account for significant fluctuations in the output of EEs. The second strand of studies identifies the financial channel through foreign interest rate and foreign price as the dominant avenue by which external shocks affect domestic macroeconomic performance (Canova, 2005; Mackowiak, 2007; Nizamani et al., 2017; Senadheera, 2016). Lastly, some studies (Doojav & Luvsannyam, 2019; Onafowora & Owoeye, 2017; Rasaki, 2017; Rasaki & Malikane, 2015) argued that external debt is the leading channel through which global shocks influence macroeconomic performance. Based on these inconsistent findings, the present study attempts to provide answers to the following questions: What is or are the dominant external shocks in Nigeria? Through which channel(s) do these shocks transmit into the Nigerian economy? The need to identify the dominant channel(s) through which external shocks percolate to Nigeria is imperative to guide domestic policymakers to design the right and effective policies to insulate the domestic economy from these unanticipated external disturbances.

Therefore, the present study contributes to knowledge in four major areas. First, unlike the previous studies in Nigeria that focus only on oil price shocks (Abdulkareem & Abdulhakeem, 2016; Akinleye & Ekpo, 2013; Iwayemi & Fowowe, 2011; Khan & Ahmed, 2011; Olomola & Adejumo, 2006; Yusuf, 2015), the present article identifies oil price, trade-weighted output, interest rate, and inflation as major external shocks and three potential channels (terms of trade, exchange rate, and external debt), based on the structure of the Nigerian economy, and it quantifies their impact on output and inflation in Nigeria. Second, previous studies (Adefabi & Rasaki, 2018; Oladunni, 2019; Rasaki & Malikane, 2015) utilize the United State data to proxy the external shocks in Nigeria. Nigeria engages in international trade with many countries; hence, any attempt to ignore the influence of these top trading partners will underestimate the impact of external shocks in the country. Besides, in the recent times, the volume of transactions between Nigeria and other countries (like China and India) have outweighed that of the United States (International Monetary Fund [IMF], 2018). Meanwhile, some studies (Ojeyinka & Yinusa, 2020; Zaidi et al., 2018) argue that the usage of one country to proxy the global economy might generate a single-country misspecification bias in research findings. Hence, it important to capture the impact of these other major trading partners to accurately and comprehensively assess the impact of external shocks on the Nigerian economy. Based on this argument, the study constructs trade-weighted external variables from Nigeria’s five top trading partners (China, India, Netherlands, the United Kingdom and the United States) by employing the data from IMF’s Direction of Trade, 2018, to capture the external shocks. Third, the study unravels the conduits through which external shocks infiltrate the Nigerian economy. To the best of our knowledge, our study appears to be the first to examine the transmission channels of external shocks in Nigeria. Lastly, on methodological grand, the study departs from the existing studies and employs the Dynamic Stochastic General Equilibrium (DSGE) model because of its advantage over other techniques of estimation. The DSGE model is theoretical and has been widely adopted and accepted in the literature for investigating the propagation mechanisms of economic shocks. One striking attribute of DSGE models is the role of “rational expectations” in explaining the behavior of economic agents over time. This assumption implies that economic agents rely on not only past information but also future information to optimize their objectives. Additionally, the DSGE model has the advantage of combining the micro-foundation of both households’ and firms’ optimization problems with a large collection of both real and nominal rigidities (Paccagnini, 2011).

The next section presents the literature review followed by the theoretical framework and methodology. Subsequently, data analysis and discussion of results are presented, and lastly, we present the conclusions of the study.

Literature Review

Extensive studies have been conducted on external shocks and their transmission channels in developed, Latin American and Asian countries. However, the issue has received less attention in the Sub-Saharan African countries and particularly in Nigeria. Besides, the major source of discrepancy in the existing literature is on the precise. For instance, Canova (2005) investigates the channels through which external shocks, majorly from US shocks channels through which external shocks transmit to the domestic economy, propagate to Latin America using the vector autoregressive (VAR) technique. The author concludes that external shocks transmit to the Latin American economies through the financial channels. of external shocks on the macroeconomic fluctuations of eight EEs. The study identifies the US Similarly, within the structural vector autoregressive (SVAR) framework, Mackowiak (2007) assesses the impact monetary policy and world commodity price as major external shocks driving the business cycle, and it finds that US monetary policy shock explains a large variation in the price level and domestic output in the countries studied. The studies by Canova (2005) and Mackowiak (2007) are consistent in their findings as they both identify financial shock via the US monetary policy shocks as a major driver of the business cycle in their respective studies on Latin American countries. In East Asia, Nguyen et al. (2014) assert that the region is progressively exposed to foreign interest rate shocks than to trade shocks. Following this line of argument, recent studies such as Senadheera (2016), Nizamani et al. (2017), and Chileshe et al. (2018) find that external shocks are transmitted to Sri Lanka, Pakistan, and Zambia respectively through the financial channel (foreign interest rate).

However, studies such as Kose and Riezman (1999) and Mendoza (1995) analyze the role of external shocks in propagating macroeconomic fluctuations in developing countries including those in Africa. The authors argue that trade shock accounts for more than half of the output fluctuations in developing countries. Similarly, Zhu (2010) challenges the submission of Canova (2005) and Mackowiak (2007) by arguing that terms of trade shocks and debt accumulation account for major fluctuations in output. His finding is in tandem with an earlier study by Kose and Riezman (1999) who confirm that trade shocks significantly propel macroeconomic fluctuations in African countries. Furthermore, Allegret et al. (2012) analyze the relative importance of external shocks on domestic macroeconomic fluctuations of East Asian countries. Using the SVAR approach on quarterly data covering Q1 of 1990 to Q1 of 2012, the authors identify real oil price shocks, trade shocks, monetary shocks, and financial shocks as major external shocks affecting the sampled countries. Contrary to the finding of Mackowiak’s study, the authors find that oil price shocks and trade shocks significantly affect domestic activities. Hence, the study concludes that the trade channel is the most important channel through which external shocks permeate to the East Asian countries. In addition, Murach and Wagner (2021) use factor-augmented VAR to estimate the effect of external shocks in driving the business cycle in China. The study reveals that the confidence channel works through trade and financial channels to generate the business cycle in the economy.

Apart from the two strands discussed earlier, a pocket of studies identified external debt and exchange rates as major transmission channels through which external shocks propagate fluctuations in small open economies. Employing the VAR method, Desroches (2004) investigates the sources of macroeconomic fluctuations in 22 emerging market economies, and he concludes that the exchange rate regime plays a critical role in the transmission of external shocks. In Nigeria, Rasaki (2017) investigates the impact of external shocks and finds that output and inflation in Nigeria are substantially driven by external debt and exchange rates. Extending the work of Rasaki (2017), Adefabi and Rasaki (2018) employ the SVAR technique to examine the nexus between external shock and economic growth in Nigeria. Findings from the study reveal that external debts explain significant fluctuations in output growth. In a recent study, Doojav and Luvsannyam (2019) use the Bayesian VAR approach and discover that external shocks affect the Mongolian economy via the exchange rate channel.

On the methodological ground, a large number of studies employ the VAR/SVAR model to analyze the impact of external shocks on macroeconomic performance (Cazacu, 2015; Kabanda, 2016; Muhamad & Said, 2016; Nizamani et al., 2017; Oyelami & Olomola, 2016; Sen & Kaya, 2015;). However, the VAR technique has been criticized for its lack of economic theory in the ordering of model variables (Luetkepohl, 2011). In addition, ordering in the conventional VAR model is often dictated by data and not by any economic intuition, and this affects the reliability of any outcome and finding from such analysis. Consequently, VAR models have been criticized for relying on ad-hoc assumptions in identifying restriction, which makes no economic sense (Kilian, 2011). Similarly, SVAR has been criticized for its inability to handle large datasets. This, in turn, leads to a loss of the degree of freedom when more variables are added to the model. Additionally, the results from SVAR are sensitive to identification restrictions imposed on the model. To address the problem of limited dataset plaguing the SVAR method, the DSGE model was developed and has been widely adopted in the literature for investigating the propagation mechanisms of economic shocks (Bazhenova & Bazhenova, 2016; Forni et al., 2015; Senadheera, 2016; Shioji et al., 2010). As documented by Peiris and Saxegaard (2007), DSGE models have many advantages that make them more attractive for the analysis of macroeconomic policy. First, they are stochastic, suggesting that they incorporate the random components that play a significant role in describing the cyclical behavior of the economy. Second, DSGE models have microeconomic foundations. This means that they are based on the decisions of economic agents such as households and firms in the economy. Third, they are structural, as each equation in the model has an economic interpretation. Lastly, DSGE models are forward-looking as agents based their decision on rational expectations about the future economic condition.

Methodology

Theoretical Framework

The New Keynesian (N-K, hereafter) theory is adopted as the theoretical framework for this study. The N-K theory combines the features of the traditional Keynesian theory and that of the new classical theory. The theory assumes that households and firms maximize utility and profit, respectively, but it rejects the assumptions of price flexibility, perfect market, and money neutrality that characterized the real business cycle (RBC) theory. Thus, the N-K theory is based on the hypotheses of nominal rigidity (in price and wages), imperfect competition, and non-neutrality of money. The assumption of imperfect competition entails that firms operate in a monopolistic competitive market in which each firm produces a differentiated product and as such can reset its price (Goodfriend, 2002). However, the theory argues that only a proportion of firms can adjust their prices because of the sluggish nature of price adjustment. Hence, the N-K theory concludes that a monopolistic competitive firm cannot adjust its price without cost and that price cannot be adjusted instantaneously. This study adopts the N-K theory as the theoretical framework based on its relevance to the economy of developing countries such as Nigeria. First, the Nigerian economy is not completely market-driven in its entirety. For commodities like petroleum, the pump price is regulated by the government. This imposes some constraints or rigidities on the market forces in an attempt to adjust the price. Hence, the model adopted in the study assumes costly adjustment for firms, especially, in the final goods sector. Second, in labor market, workers are not free to adjust wages because of the long-term contract that exists with their employers, especially in the public sector. In particular, wages are fixed based on negotiations between labor unions and the government. Consequently, it is very rare to experience a downward review of wages in Nigeria. This evidence of rigidity in prices and wages makes the N-K theory to be of particular relevance to the modelling of an EE like Nigeria. Hence its adoption as the theoretical foundation for this study.

Generally, there are three agents in the N-K theory: the households, the firms, and the government. To describe the evolution of each of these actors, the N-K model is derived from three major equations: the Investment–Savings (IS) curve (output equation) to characterize the behavior of households, which represents the demand side of the economy; the Phillips Curve (inflation rate), which seeks to explain the behavior of firms and captures the supply side; and the interest rate rule, derived from the monetary policy rule to describe the evolution of monetary authority in the economy.

Model Specification

The study follows the works of Gali and Monacelli (2005) and Gali (2008)¹ in modelling the evolution of the three equations as enshrined in the N-K Theory. However, we modify the baseline models from the N-K theory by incorporating the external shocks and their transmission channels into the estimated models to achieve the study’s objectives. Specifically, we augment the three-baseline models by incorporating the identified external shocks. Besides, the study also includes three additional equations to describe the evolution of the three transmission channels: terms of trade, exchange rate, and external debt channels into the analysis. The summary of models estimated to achieve the objectives are presented further. However, for brevity, the full derivation of equations (1)–(10) are reported in the supplementary material² to this article.

{\tilde{y}}_{t} = α_{1} E_{t} {\tilde{y}}_{t + 1} + α_{2} E_{t} π_{t} + α_{3} i_{t} + α_{4} s_{t} + α_{5} q_{t} + α_{6} y_{t}^{*} + α_{7} π_{t}^{*} + α_{8} i_{t}^{*} + c_{t}

(1)

π_{t} = β_{1} E_{t} {\tilde{y}}_{t + 1} + β_{2} E_{t} π_{t + 1} + β_{3} i_{t} + β_{5} q_{t} + β_{6} y_{t}^{*} + β_{7} π_{t}^{*} + β_{8} i_{t}^{*} + β_{9} o p_{t} + o_{t}

(2)

i_{t} = φ_{1} i_{t - 1} + φ_{2} {\tilde{y}}_{t} + φ_{3} π_{t} + φ_{4} q_{t} y_{t}^{*} + φ_{5} i_{t}^{*} + n_{t}

(3)

s_{t} = η_{1} s_{t - 1} + η_{2} π_{t}^{*} - η_{3} π_{t} + η_{4} o p_{t} + η_{5} q_{t} + h_{t}

(4)

q_{t} = λ_{1} q_{t - 1} + λ_{2} π_{t}^{*} - λ_{3} π_{t} + λ_{4} d e b_{t} + λ_{5} o p_{t} + x_{t}

(5)

d e b_{t} = \partial_{1} d e b_{t - 1} + \partial_{2} i_{t}^{*} + \partial_{3} {\tilde{y}}_{t} + \partial_{4} o p_{t} + \partial_{5} q_{t} + \partial_{6} y_{t}^{*} + m_{t}

(6)

o p_{t} = ρ_{o p} o p_{t - 1} + p_{t} 0 < ρ_{o p} < 1

(7)

y_{t}^{*} = ρ_{y^{*}} y_{t - 1}^{*} + v_{t} 0 < ρ_{y}^{*} < 1

(8)

π_{t}^{*} = ρ_{π} * π_{- t - 1}^{*} + w_{t} 0 < ρ_{π^{*}} < 1

(9)

i_{t}^{*} = ρ_{i^{*}} i_{t - 1}^{*} + z_{t} 0 < ρ_{i^{*}} < 1,

(10)

where $\tilde{y}$ represents the output gap; ύ is the inflation rate in the domestic economy; i represents the nominal interest rate; s is terms of trade; $π_{t}^{*}$ is the foreign inflation rate; deb is external debt; op and q are oil price and real effective exchange rate, respectively; $y_{t}^{*}, π_{t}^{*}$ and $i_{t}^{*}$ are foreign output, foreign inflation, and foreign interest rate, respectively;. and $c_{t}, o_{t}, n_{t}, h_{t} x_{t}, and m_{t}$ are residuals from output gap, inflation, terms of trade, exchange rate and external debt equations, respectively. Similarly, $p_{t}, v_{t}, w_{t} and z_{t}$ are shocks from the oil price, foreign output, foreign inflation, and foreign interest rate, respectively.

The IS curve in equation (1) defines the dynamics of output gap ( ${\tilde{y}}_{t})$ which is derived from the decision of the household in an attempt to maximize the present and future utility. The output gap is determined by the future output gap, inflation and interest, and the external shock variables. The Phillips curve (equation (2)) explains the evolution of the inflation rate (ύ) and captures the supply side of the economy. The equation is derived from the firm’s behavior of maximizing profit. Equation (2) states that inflation depends on expected inflation, output gap, interest rate, and external variables from Nigerian top trading partners. The interest rate (equation (3)) is a modified version of the Taylor’s (1993) rule that incorporates the assumption of the open economy model assumption. Taylor’s rule sets the evolution of short-term nominal interest rate over time. It is derived from the decision of monetary authority in performing its traditional function of price stability in the economy.

Equation (4) describes the evolution of terms of trade channel, where the terms of trade are positively related to its lag and foreign inflation but negatively related to domestic inflation. However, we incorporate the oil price and real effective exchange rate to capture the influence of the oil sector on the Nigerian economy. The derivation of the real effective exchange channel (equation (5)) is based on the assumption of the law of one price (LOP) which implies that the LOP gap is unity since the law assumes that identical goods should command the same price when the market is efficient. However, we follow the work of Rasaki (2017) in describing the evolution of the real effective exchange by incorporating external debt and oil price into the channel to account for the significance of oil in the Nigerian economy. Lastly, equation (6) is the external debt channel which is adapted from the work of Rasaki (2017), as external debt is shown to depend on oil price and real effective exchange rate.

Equations (7)–(10) describe the behavior of oil price, foreign output, foreign inflation, and foreign interest rate shocks in the study. To describe the evolution of these external variables, the study follows existing studies on external shocks and thus assumes that foreign variables evolve through the AR (1) process as presented earlier.

Method of Analysis: The Bayesian Approach

In estimating the parameter of the model, the study employs the Bayesian estimation approach because of its advantages over other techniques of estimation. First, the Bayesian technique is better to deal with identification problems associated with other techniques. Second, the Bayesian technique allows one to incorporate the use of a priori information through the specification of prior distribution function to reduce the identification problem. Third, it explicitly addresses the issue of model specification by incorporating shocks to represent measurement error in a structural equation. The technique automatically produces probability distributions for the parameters. Lastly, the technique is good for model comparison (Almedia, 2009; Griffoli, 2007, 2013; Oye & Alege, 2018).

There are three elements in the Bayesian approach: the prior, the likelihood function, and the posterior. The combination of prior and likelihood function is expressed by the Bayes theorem to generate the posterior value of the parameters, given that the prior distribution for a parameter is given by $P (θ)$ and that the conditional probability, defined by the likelihood function for a variable, is expressed as $P (y^{*} / θ) .$ The posterior distribution connected with the prior and the likelihood function through the Bayes’ theorem is presented as follows:

P (θ / y^{*}) = \frac{P (y^{*} / θ) P (θ)}{P (y^{*})},

(11)

where $P (θ / y^{*})$ is the posterior distribution, $P (y^{*} / θ)$ is the likelihood function, $P (θ)$ is the prior density function, and $P (y^{*})$ represents the given data.

Equation (11) implies that the probability of the event $θ$ is conditional on the known value of the event $y^{*}$ . In addition, $P (y^{*})$ the equation does not depend on the parameter and, as such, can be treated as constant. Based on this, equation (11) can be re-expressed as

P (θ / y^{*}) \propto P (y^{*} / θ) P (θ) = K (θ / y^{*}) .

(12)

Here, $K (θ / y^{*})$ is the posterior kernel. The logarithm version of equation (12) is presented as follows:

\log (θ / y^{*}) = \log P (y^{*} / θ) + \log P (θ) .

(13)

Lastly, in this study, we simulate the posterior distribution, in equation (13), using the Markov Chain Monte Carlo (MCMC) Metropolis-Hasting (M-H) algorithm. The MCMC is the most frequently used simulation method in the Bayesian approach to the DSGE model.

Data, Calibration of Model Parameters, and Posterior Values

Data

Quarterly data on output gap, domestic inflation, nominal interest rate, real effective exchange rate, oil price, external debt to GDP, terms of trade, foreign output, foreign interest rate, and foreign inflation are sourced from the International Financial Statistics (IFS), World Development Indicator (WDI), and CBN Statistical Bulletin between Q1 of 1981 and Q4 of 2018. As noted under the introduction, we construct trade-weighted variables from Nigeria’s five top trading partners in 2018. Tanilable A1 in the Appendix³ contains the weights assigned to each country in the computation of trade-weighted variables. In line with the standard in the literature, the study applies a one-sided Hodrick Prescott (HP) filter on the logarithm of real gross domestic product to generate the output gap. Lastly, all the variables are transformed into their logarithm for before the estimation. Tanilable A2 in the Appendix contains the description and measurement of variables. Data for the study were stored in a .mod file before they were imported into the Matlab software. Lastly, the study used Matlab R2015a and dynare 4.4.3 to estimate the DSGE model.

Calibration and Choice of Prior Distribution and Posteriors

By definition, calibration is the process of choosing prior values for parameters in a DSGE model. Theoretically, prior values are selected based on facts and observations from previous empirical studies and economic theories. In addition, the priors reflect the researcher’s guess on the values of structural parameters before the actual estimation is performed. In Nigeria, it must be acknowledged that studies have been conducted on DSGE, among which are Olekah and Oyaromade (2007), Alege (2008), Olayeni (2009), Adebiyi and Mordi (2010), Mordi et al. (2013), Rasaki and Malikane (2015), Adegboye (2015), Adebiyi and Mordi (2016), Oye and Alege (2018), and Akinlo and Apanisile (2019). Hence, priors for this study are chosen from these existing studies and other studies on oil-exporting countries, while those priors that are not available are estimated based on Nigerian data from 1981 to 2018. For the persistence parameters and monetary policy (interest rate equation), priors are sourced from Mordi et al (2013) and Rasaki (2017), respectively. Also, the priors for output gap (dynamic IS curve) and inflation (N-K Philip curve) equations are obtained from the works of Adebiyi and Mordi (2016).

Similar to the choice of the priors in the Bayesian technique is the probability distribution of parameter estimates in the DSGE model. In line with Olayeni (2012), Allegret and Benkhoja (2015), and Rasaki and Malikane (2015), the study assumes a beta distribution for those parameters whose values must lie between (0, 1) intervals and normal distribution for parameters whose values may be bounded between negative and positive domains. This applies to all the persistence parameters of the exogenous stochastic (AR (1)). Also, for those parameters that must be positive, the study assumes gamma distribution. Priors for the standard deviation of exogenous shocks are assumed to follow the inverted gamma distribution. Tanilable A3 (in Appendix) displays the prior values used in estimating the structural parameters of the study with their probability distributions.

After the identification of the prior values for the structural parameters and shocks, the next step in the Bayesian technique is the simulation of the model to confirm the existence of the steady state for the endogenous variables. This is important before actual estimation is performed to check whether the steady-state or stable equilibrium exists for the variables in the model. The diagnostic tests performed on the prior values reveal the existence of a steady state for the endogenous variables in the model as all the variables in the model return to equilibrium level after a temporary shock to all the external shocks variables. In line with this, the results for the response of endogenous variables to external or exogenous shocks in our model are generated and presented in Faniligures A1 to A4 in the supplementary material. It can be deduced from the figures that after a temporary shock to the system, all the variables return to their steady-state values (zero). This further confirms that the models estimated are stable and thus satisfy the Blanchard Kahn condition for stable equilibrium.

Having established the existence of stable equilibrium for the model, the study proceeds to estimate the posterior values for the structural parameters and shocks in the model. The posterior values are derived by combining the prior values with the likelihood function. To generate the posterior, the random walk MH algorithm is utilized to produce 10,000 draws from the posteriors. The study employed 103 out of the available 152 data points, covering the period between Q1 of 1981 and Q4 of 2018. The Log data density was 79.778161. The results of posterior values for estimated parameters and shocks are reported in Tanilable A4 in the appendix. The outcome from Tanilable A4 shows the probability distribution assumed, the prior mean, the posterior mean, the prior standard deviation, and the confidence interval for each parameter. Overall, evidence from Tanilable A4 affirms that, on average, volatility values for all the parameters are lower than the prior values calibrated. Lastly, estimated posterior values for all the identified shocks are significantly different from zero, indicating that all innovations to these external variables have a sizable impact on the economy.

The visual comparison of the prior mean assumed and posterior values generated for parameters and shocks in the study are presented in Figures B1 to B4 in the supplementary materials attached to this study. Each graph consists of the prior (grey line) posterior value (black line). The vertical green line in each diagram gives information on the posterior mode from the optimization process. The outcomes from these figures suggest that the data and the priors provide sufficient information on the parameter estimated, which in turn confirms the probability of the posterior values. Furthermore, the distribution of the prior and posterior are similar for most of the parameters while shapes of posterior estimates do not diverge from normality.

Validity of the Estimation

In DSGE, the statistical integrity of the Bayesian estimation method and results are investigated using some sets of visual diagnostic tests. Some of these diagnostic tests include Historical and Smoothed variables tests, smoothed shocks test, Monte Carlo Markov Chain (MCMC) univariate convergence diagnostic test, and MCMC multivariate convergence diagnostic test. The results from these diagnostic tests are presented in Figures C1 to C3 in the supplementary material.

Figures C1 and C2 present the historical and smoothed shocks for each model. In interpreting the diagram, the horizontal axis in each plot denotes the length of the sample period. The overriding assumption here is that unobserved exogenous shocks in the model have zero mean (Ahmed et al., 2013). Visual inspection from the diagnostic test confirms all the smooth shocks clustered around zero, suggesting the consistency of parameter estimates.

Similarly, MCMC multivariate diagnostic test in Figure C3 summarizes the overall convergence of all the parameter estimates in the model. The multivariate test is presented in three graphs. Each graph indicates a specific convergence measure. The three graphs present the mean (interval), variance (m2), and third moment (m3) , respectively. Also, the two lines capture the result of within and between chains. The overall criterion is for the two lines to become constant and to converge to each other in the long run (Almeida, 2009). The outcomes from Figure C3 show that the models estimated are stable as the two lines converge at the end of the time horizon. Lastly, the visual inspection of the two tests suggests that the optimization procedure produces a robust maximum for the posterior kernel (Ahmed et al., 2013). This supports the fact that prior values provide adequate information on the true value (posteriors) of the estimated parameters.

Sensitivity of the Results

Apart from various diagnostic tests conducted on the parameters in the model, the study also carries out sensitivity analysis on prior values. To evaluate the sensitivity of the results to priors, we increase the mean and the standard deviation of priors of structural parameters, persistence parameters, and structural shocks by 5%. To conserve space, the outcomes from the sensitivity analysis are presented in Tanilable A5 in the supplementary material. The outcomes from the sensitivity analysis show that changes in the value of the estimates do not significantly affect the estimated parameters, except for standard deviation of oil price ( $σ_{p}$ ) and persistence parameter of oil price ( $ρ_{o p}$ ) whose posterior values increase substantially. In Tanilable A5, the posterior value for the standard deviation of oil price is estimated to be 0.8771, and this implies that oil price shock is the most volatile shock affecting the Nigerian economy. This might be attributed to frequent and unpredictable changes in the oil price in the world oil market. Also, after the sensitivity analysis, the posterior of the persistence parameter for oil price shock in Tanilable A5 is 0.77 against the sensitized prior value of 0.472 assumed. This suggests that the estimate of oil price persistence is highly data-driven, and that oil price shocks in Nigeria will linger for a considerable length of time. The overall conclusion from the sensitivity analysis is that for reasonable changes in the value of prior mean and standard deviation, the overall quantitative results are quite robust and thus reinforce the Blanchard Kahn condition that posterior values obtained are reliable and stable.

Discussion of Results

Bayesian Impulse Response Analysis

Oil Price Shocks

As expected, innovation to oil prices has a positive effect on the output gap for the entire period. Evidence from Figure 1 shows a steady decline in the output gap from 0.017% in the 1st quarter to 0.011% in the 40th quarter. The result confirms the findings of Allegret et al. (2012) and Bazhenova and Bazhenova (2016) for East Asian countries and Ukraine. The result shows that the oil price shock has a significant and long-lived impact on the output gap in Nigeria. Conversely, with a positive innovation to the oil price, inflation experiences a sharp decline from the 1st quarter to the 10th quarter indicating a decline in the inflation rate from 0.015% in 1st quarter to 0.0003% in 12th quarter. However, after the 12th quarter, the impact of oil price shocks on inflation fizzles out and this is persistent to the last quarter. This further confirms the outcomes of Olomola and Adejumo (2006) for Nigeria and Bhardwaj (2016) for India that oil price has a negligible effect on inflation. One plausible explanation for this finding might not be unconnected with the tight monetary policy implemented by the monetary authority to curb the inflationary pressure following an increase in oil prices. This is evident by the increase in interest rate between the first and fifth quarters.

Figure 1.

Source: The authors.

Trade Weighted Foreign Output Shocks

The impact of a standard deviation shock to foreign output on output and inflation is presented in Figure 2. An unanticipated shock that leads to an upsurge in the output of Nigerian trading partners is expected to raise the demand for Nigeria’s export and stimulates output. The response of the domestic output gap to foreign output shock is positive throughout the periods. This is not surprising because an increase in income of Nigeria’s trading associates is expected to boost domestic output because of increasing demand for Nigerian crude oil, whereas the effect of foreign output shock on the output gap, though positive, declines from 0.018% in the first quarter to 0.010% in the fourth quarter. From the fifth quarter, its impacts on the output gap become constant, with an average impact of 0.005% for the remaining period. The results reinforce the findings of Abere and Akinbobola (2020) for Nigeria, Nizamani et al. (2017) for Pakistan, and Chileshe et al. (2018) for Zambia. In the first two quarters, inflation negatively reacted to foreign output shocks with –0.0076% and –0.0004% in the first and second quarters, respectively. However, from the 4th quarter, the impact of foreign output on inflation turns positive, and this persists for the entire sample period. Furthermore, the effect of foreign output on domestic inflation becomes insignificant, especially from the 12th quarter to the 40th quarter. The finding is in tandem with the study of Nizamani et al. (2017) for Pakistan.

Figure 2.

Source: The authors.

Trade Weighted Foreign Inflation Shocks

A positive innovation to foreign inflation implies a hike in inflation rate from Nigeria’s trading partners. When there is an increase in foreign prices, monetary authorities in these economies will adjust their policy rate to stem inflationary pressure. Based on Nigeria’s tie with these countries, foreign monetary policy action to curb rising prices will indirectly affect domestic inflation in the country. Figure 3 presents the responses of output gap and inflation to foreign inflation. The results suggests that the output gap and inflation respond positively to a positive shock in foreign inflation. On impact, a 1% rise in foreign price increases the output gap by 0.28% and 0.12% in the first and second quarters, respectively. However, the response of the output gap wanes continuously from the third quarter to the last quarter. Similarly, the impact of foreign inflation shock on domestic inflation is significant in the first eight quarters. Specifically, the inflation rate in Nigeria increases immensely from 0.0008% in the first quarter to 0.0017% in the eighth quarter, suggesting a strong connection between Nigeria’s inflation rate and foreign inflation. The result attests to the finding of Oyelami and Olomola (2016). Also, from the 12th quarter onward, foreign price shock had little or no impact on domestic inflation as the inflation rate oscillates by 0.0005% between the 12th and 40th quarters. Again, evidence from Figure 3 shows that the impact of foreign prices on domestic inflation is short-lived.

Figure 3.

Source: The authors.

Foreign Interest Rate Shocks

Figure 4 presents the reaction of the endogenous variables to a 1% shock to the foreign interest rate. The results from Figure 4 show that output gap and domestic inflation respond negatively to foreign interest rate shock throughout the periods examined. In addition, the impact of financial shock from Nigeria’s trading partners is significant and hurts the output gap in the first four quarters. On impact, one standard deviation shock in foreign interest rate reduces the domestic output gap by 0.023% and 0.009% in the first and fourth quarter, respectively. Nevertheless, the impact of the foreign interest rate on the output gap substantially reduces in the longer horizon. The result validates the findings of Allegret et al. (2012) and Yamamoto (2014) for Asian economies, Nizamani et al. (2017) for Pakistan, and Rasaki and Malikane (2015) for Nigeria. The finding aligns with the a priori expectation that an increase in foreign interest rate will slow down the economic activity by reducing consumption and investment, hence dampening domestic output.

Figure 4.

Source: The authors.

In the same vein, domestic inflation responds negatively to a positive shock to the foreign interest rate. However, the effect of interest rate shock on domestic inflation is marginal and fizzles out at the higher horizons. It can be deduced from Figure 4 that the inflation rate adjusts to its steady state from the 10th quarter after a positive shock to the foreign interest rate. For instance, the average response of inflation to financial shock was –0.0006% for the entire period. Hence, the study concludes that financial shock had a limited impact on inflation in the long run. The finding confirms the assertion of Nizamani et al. (2017) for East Asia but negates the studies by Chileshe et al. (2018) for Zambia. Contrary to the expectation, the impact of the foreign interest rate on the domestic interest rate is negative throughout the time horizons. However, a similar finding is observed by Oyelami and Olomola (2016) and Abere and Akinbobola (2020) suggesting that the monetary authority in Nigeria follows a different path in fixing the monetary policy rate in the country. In other words, the Central Bank responds to shock in foreign interest rate with loose monetary policy. One major explanation for this is linked with the intention of the Apex bank to stimulate domestic economic activity following huge capital outflow associated with a foreign financial shock.

One major outcome from the impulse response analysis discussed earlier is that the impact of external shocks on inflation is temporary and short-lived. With time, the impact of these shocks on the inflation rate dissipates, suggesting that external shocks do not have permanent effects on the economy, as the domestic inflation return to its steady state after a shock to each of the four external shock variables.

Forecast Error Variance Decomposition

Table 1 reports the contribution of external shocks on variation in the output gap. Collectively, external shocks account for approximately 92% of the total variation in the output gap in the second quarter. The contribution of external shocks on output gap fluctuations is persistent throughout the period investigated. This suggests that output gap in Nigeria is highly prone to external shocks from the country’s to trading partners. The result confirms the findings of previous studies on the Nigerian economy that external shocks are major drivers of output in Nigeria (Adefabi & Rasaki, 2018; Oyelami & Olomola, 2016). In sum, external shocks substantially contribute to output fluctuations in Nigeria. Similar results are obtained by Rahman (2015) for Bangladesh, Krznar and Kunovac (2010), Dumicic et al. (2015) for Croatia, and Rasaki and Malikane (2015) for Africa. On the dominant shock among the external shock variables, evidence from Table 1 shows that foreign inflation explains a larger percentage of the total variation in the output gap. In terms of contribution, foreign prices contribute 89.8%, 83.5%, and 79.4% in the second, fourth and twelfth quarters, respectively, of the total variation in the output gap. This suggests that foreign inflation shock is a key driver of the output fluctuations in Nigeria.

One crucial inference from Table 1 is that oil price shock has a negligible contribution to variation in the output gap. The shock accounts for less than 1% of overall fluctuations in domestic output. The result is surprising despite the country’s reliance on the oil sector. However, a similar result has been established in the literature for Nigeria. For instance, Adefabi and Rasaki (2016), Olomola and Adejumo (2006), and Iwayemi and Fowowe (2011) find that oil price shock accounts for minor variation in economic growth in Nigeria. However, our finding contradicts the findings of Oyelami and Olomola (2016) who establish that oil price significantly drives output variation in Nigeria. Considering the contributions of other external shocks, foreign output and foreign interest rate shocks explain 0.9% and 1%, respectively, of output variations in the second quarter. In addition, the two contribute below 2% of the fluctuations in domestic output throughout the 12 quarters investigated. Overall, the output gap in Nigeria is significantly driven by external shocks especially from trade-weighted foreign prices, implying that output in Nigeria is highly vulnerable to foreign inflation from its trading partners.

Table 1.

Variance Error Decomposition of Output.

Periods	op	fgdp	finf	fint	inter	outp	inf
2	0.0026	0.0091	0.8981	0.0109	0.0081	0.0513	0.0003
4	0.0046	0.0127	0.8629	0.0138	0.0171	0.0509	0.0003
6	0.0063	0.0147	0.8353	0.0159	0.0256	0.0494	0.0003
8	0.0077	0.016	0.8128	0.0174	0.0335	0.0481	0.0003
10	0.0089	0.0169	0.7936	0.0185	0.0407	0.0469	0.0003
12	0.0001	0.0176	0.7765	0.0194	0.0473	0.0458	0.0003

Source: The authors.

Note: op = oil price; fgdp = trade-weighted foreign output; finf = trade-weighted foreign inflation; fint = trade-weighted foreign interest rate; inter = interest rate; outp = domestic output gap; and inf = domestic inflation.

Similarly, Table 2 presents the drivers of inflation in Nigeria using variance decomposition generated from the DSGE model estimated. Overall, the outcomes from Table 2 reveal that external shocks contribute 35% of total variations in inflation in the second quarter. Unlike the output fluctuations, the finding suggests that domestic inflation is less vulnerable to external shocks. Also, it is observed from the results that the contribution of external shocks to variations in inflation increases steadily in the longer horizons. For instance, cumulatively, the contribution of external shocks increases from 35% in the 2nd quarter to 40% in the 12th quarter. Again, among the external shocks, foreign inflation shocks account for major fluctuations in domestic inflation. Specifically, trade-weighted foreign inflation explains 15% of fluctuations in inflation from the second quarter and this increases significantly to 21% between the fourth and twelfth quarters. This suggests a significant spillover effect of foreign prices from the country’s top trading partners to domestic inflation. The result is in line with the finding of Oyelami and Olomola (2016), and of Rahman (2015) for Bangladesh, that foreign inflation has a substantial effect on domestic inflation in Nigeria. Similarly, the contribution of other external shocks stemming from the oil price, trade-foreign output, and foreign interest rate considerably explains the variability in inflation in Nigeria. The three shocks jointly contribute about 19% of the fluctuations in inflation over the 12 quarters examined. However, the foreign interest rate seems to be the least contributor to variability in inflation. The finding implies that the inflation rate in Nigeria depends strongly on foreign prices from the country’s major trading partners.

Table 2.

Variance Error Decomposition of Inflation.

Periods	op	fgdp	finf	fint	inter	outp	inf
2	0.0857	0.0934	0.1593	0.0149	0.0642	0.0153	0.2313
4	0.0692	0.0813	0.2114	0.0262	0.067	0.015	0.1619
6	0.0646	0.0869	0.2142	0.0315	0.0672	0.0144	0.1433
8	0.0633	0.0893	0.2121	0.0339	0.0678	0.0142	0.1422
10	0.0629	0.0904	0.2109	0.035	0.0684	0.0141	0.1409
12	0.0628	0.0906	0.2103	0.0354	0.0689	0.014	0.1403

Source: The authors.

Transmission Channels of External Shocks in Nigeria: Evidence from Variance Decomposition

Another major objective of this study is to explore the conduits through which the external shocks transmit into the Nigerian economy. To gain insight into the transmitters of shock, the proportion of shocks transmitted via each identified channel is quantified and compared to identify the dominant channels of external shocks transmission in Nigeria. The analysis of shock transmission is presented in Table 3. The first panel displays the proportion of forecast error variance of different external shocks that were transmitted to the economy through the terms of trade channel, while the second and third panels present the percentage of shocks transmitted via external debt and real effective exchange rate channels, respectively. To achieve this objective, we isolate and analyze only those shocks emanating from the four identified external shocks. In other words, shocks propagated by domestic variables are ignored in the analysis.

Starting with the oil price shock, the outcomes from Table 3 reveal that 2% of shock to the oil price is transmitted through the exchange rate channel in the second quarter. The response of the channel to oil price shock is constant at 2% throughout the entire period. This suggests that the magnitude of oil price shock transmitted via the channel is marginal. On the other hand, terms of trade channel convey 17% of oil price shock in the second quarter. Its significance as a transmitter of shock persists throughout the time horizons. Similarly, between the second and fourth quarters, external debt transmits 7% of oil price shock in the economy. Based on this finding, terms of trade channel constitute the dominant channel in the propagation of oil price shock in Nigeria while external debt channel is the second. The significance of terms of trade channel is not surprising because an increase in oil price is expected to raise Nigerian export, which will, in turn, improve the country’s trade position. The result confirms the findings of Kose and Riezman (1999), Zhu (2010), Allegret et al. (2012), and Muhamad and Said (2016) who argue that external shocks transmit to the LDCs via the trade channel. The results reflect the significant contribution of total trade to the GDP of Nigeria.

However, for foreign output shock, the impact of terms of trade is not significant as less than 1% of the shock is transmitted via the channel for the entire time horizons. On the other hand, exchange rate and external debt channels disseminate 7% and 4%, respectively, of trade-weighted foreign output shock in the second quarter. However, between the 4th and 12th quarters, external debts and exchange rate channels individually propagate an average of 5% of foreign output shocks in the country. Hence, it can be concluded that the exchange rate and external debts channels dominate in the transmission of foreign output shocks to the Nigerian economy. On the transmission of foreign inflation shock, evidence from Table 3 shows that terms of trade channel transmit 25% of the innovation in the second quarter. The response of terms of trade to foreign inflation shock remains constant throughout the periods. Conversely, the magnitude of the shock transmitted through the exchange rate channel is 21.8% in the second period. The response of the channel increases significantly to 34% and 32% in the 8th and 12th periods, respectively, indicating a strong influence of the exchange rate channel in propagating foreign inflation shock to the economy. Also, the potency of the external debt channel in propagating foreign inflation shock is weak compared with other channels. On average, the magnitude of foreign inflation shock transmitted by external debt channel is 6% in the second period and marginally rises to approximately 9% in the last quarter. Hence, the exchange rate and terms of trade channels appear to be the dominant channels through which foreign inflation shock diffuses into the economy. The result conforms with the study of Sennoga and Matovu (2015) who identify terms of trade and exchange channels as major transmitters of macroeconomic shocks in Uganda. A similar finding is obtained by the recent study by Gupta et al. (2018) for the advanced and emerging market economies.

Lastly, on foreign interest rate shock, the response of terms of trade channel is not significant throughout the periods. For the 12 quarters examined, the contribution of terms of trade channel to the transmission of financial shock is less than 1%. Similarly, the significance of the exchange rate channel in transmitting foreign interest rate shock is sterile as the channel transmits 3% of foreign interest rate shock over the entire period. However, the magnitude of foreign interest rate shock transmitted through the external debt channel is strong and substantial in both the short- and long-run periods. Between the second and fourth quarters, 13% of foreign interest rate shock is propagated in the economy via the external debt channel. However, the response of the channel reduces marginally from 13% in the sixth quarter to 12% in the twelfth period. Based on the aforementioned result, it is clear that foreign interest rate shock hit the economy predominantly through the external debt channel. The result is not surprising because a positive shock to the foreign interest rate is expected to increase the value of foreign-denominated debt, and by extension on debt repayment and debt service cost in Nigeria.

Overall, one notable outcome from Table 3 is that oil price shock transmits to the local economy principally through the terms of trade channel, while external debt and exchange rate channels dominate in the propagation of foreign output. On the impact of foreign inflation shocks, terms of trade and exchange rate channels were crucial in propagating the effect of the shock in Nigeria while the external debt channel dominates in the propagation of foreign interest rate shock in Nigeria.

Table 3.

Forecast Error Variance Decomposition: Transmission Channels of External Shocks.

Channels	Periods	op	fgdp	finf	fint
Terms of trade	2	0.1708	0.0005	0.2547	0.0002
	4	0.1672	0.0005	0.2524	0.0003
	6	0.1657	0.0007	0.2523	0.0006
	8	0.1650	0.0008	0.2523	0.0019
	10	0.1647	0.0008	0.2523	0.0005
	12	0.1646	0.0009	0.2523	0.0005
External debt	2	0.0723	0.0497	0.0631	0.1348
	4	0.0723	0.0522	0.0843	0.1310
	6	0.0680	0.0537	0.0918	0.1276
	8	0.0665	0.0547	0.0938	0.1260
	10	0.0658	0.0552	0.0942	0.1252
	12	0.0654	0.0553	0.0943	0.1247
Exchange rate	2	0.0192	0.0717	0.2178	0.0360
	4	0.0233	0.0445	0.3321	0.0256
	6	0.0229	0.0496	0.3485	0.0253
	8	0.0231	0.0586	0.3414	0.0274
	10	0.0233	0.0635	0.3335	0.0293
	12	0.0235	0.0653	0.3282	0.0305

Source. The authors.

Concluding Remarks

The study unravels the sources of external shocks and examines the dominant channels by which external shocks transmit into the Nigerian economy. The article employs better measures of external shocks by constructing trade-weighted variables from Nigeria’s five top trading partners- the United States, the United Kingdom, India, China, and Netherlands. To achieve the study’s objectives, the article identified four major external variables that are relevant to the Nigerian economy, namely, the oil price, trade-weighted foreign interest rate, foreign output, and foreign inflation shocks from five of Nigeria’s top trading partners, and it analyzes their influence on output and inflation in Nigeria. Furthermore, the study identified three potential channels through which these shocks transmit into the domestic economy to identify the dominant one(s) among them. The identified channels include the exchange rate, terms of trade, and external debt channels. The study estimated a DSGE model based on the New Keynesian theory on quarterly data between 1981 and 2018, and it uses the Bayesian estimation approach to estimate the structural parameters of the model.

Empirical findings from the study show that oil price, foreign output, and foreign inflation shocks enhance output gap and inflation, while the impact of foreign interest rate shock on domestic output and inflation is detrimental and not significant. The results from conditional variance decomposition reveal that, on average, 86% and 39% of total variations in output and inflation, respectively, are explained by external shocks. Among the external shock variables, foreign inflation shock has a substantial contribution to variations in the output gap and inflation in Nigeria. For the transmission channels, it is revealed that oil price, foreign output, foreign price, and foreign interest rate shocks transmit to the Nigerian economy through different channels. The study finds that terms of trade and external debt channels dominate the transmission of oil price shocks, while exchange rate and external debts propagate a substantial portion of foreign output shocks. Conversely, the study documents that foreign inflation shock permeates into the economy through the exchange rate and terms of trade channels while the external debt channel dominates in the transmission of foreign interest rate shock. Overall, the study concludes that terms of trade and exchange rate channels are the dominant conduits through which external shocks transmit into the country.

Having established the susceptibility of the Nigerian economy to external shocks, the findings from the study have specific policy implications for the economy. First, the study finds that foreign inflation shock accounts for significant fluctuations in output and inflation in the economy. Hence, the CBN should closely monitor the price movement in the country’s main trading partners to design policy responses to counteract the effect of foreign inflation in Nigeria. Second, since terms of trade and exchange rate channels are the dominant transmitters of external debt, there is a need for the policymakers to continue to pursue measures to improve import substitution and export promotion in Nigeria to lessen the country’s exposure to external disturbances. In addition, appropriate strategies should be put in place by the Apex Bank to stabilize the country’s exchange rate. Lastly, a conducive environment should be created by the monetary authority and Federal government to encourage local investors and entrepreneurs to venture into the production of intermediate and capital goods to lessen the economy’s dependence on imported capital goods. The study has extended the frontier of knowledge on the transmission of external shocks to the Nigerian economy. However, further works in this direction might examine the impact of external shocks on sectoral performance to unveil the most vulnerable among them. Besides, further studies may analyze the macroeconomic effects of external shocks on other macroeconomic variables such as the balance of payment, poverty, and unemployment in Nigeria.

Footnotes

Declaration of Conflicting Interests

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Notes

ORCID iD

Titus Ayobami Ojeyinka

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