This paper studied the effects of exchange rate volatility on economic growth. Our empirical analysis focuses on the Democratic Republic of Congo (DRC) from 1990 to 2021 and is based on the vector autoregression (VAR) model. The results show that economic growth is a function of its own innovations, the exchange rate and trade openness. Also, a depreciation of the domestic currency against the foreign currency hinders economic growth. These results suggest a strengthening of resilience through the diversification of economic activity in order to improve the international competitiveness of the Congolese economy.
In developing countries, the search for economic growth is one of the fundamental objectives that every state includes in its national development policy. However, the acceleration of growth and the accumulation of capital have an impact on the balance of payments and on the exchange rate. The determination of the exchange rate, therefore, appears to be one of the major issues in international macroeconomics (Ghosh, 2014).
In recent years, a significant amount of research has focused on the relationship between the exchange rate and economic growth (Hatmanu et al., 2020; Ioan et al., 2020; Vo & Zhang, 2019; Latief & Lefen, 2018; Alagidede & Ibrahim, 2017; Dal Bianco & Loan, 2017). The results of these studies are not unanimous. Indeed, the specificities of the countries or the methodologies used are at the root of these divergences. Also, some research leads to the finding that undervalued and competitive exchange rates are positively associated with higher economic growth. There are two reasons for this: on the one hand, an undervalued exchange rate favors the reallocation of resources to the trade sector, the locus of learning-by-doing externalities and technological spillovers (Rodrik, 2008; Eichengreen, 2008). On the other hand, the role of competitive exchange rates in loosening the exchange rate constraint influences growth (Porcile & Lima, 2010; Razmi et al., 2012).
As a small, open, dollarized and extroverted economy, the economy of the Democratic Republic of Congo (DRC) is dependent on international trade with the mining sector being the mainstay of the economy in terms of foreign exchange reserves. Since the beginning of the 1990s, the Congolese economy has been characterized by a deterioration of its fabric, resulting in a loss of value of the national currency and negative economic growth rates. In 2002, with the implementation of economic reforms instituted by the Congolese government and the resumption of cooperation with donors (IMF), the Congolese economy recovered from its slump. The international financial crisis that hit in 2008 was the cause of the scarcity of foreign currency on the foreign exchange market, with the national currency losing 41.2% of its value against the US dollar between 2008 and 2009. This situation led on the one hand to disruptions in the foreign exchange market and on the other hand to a decline in economic growth explained by the drop in exports (6.2% in 2008 against 2.8% in 2009) (Central Bank of Congo, 2010-2020).
Given the continuing divergence on the impacts of the exchange rate on economic growth in the literature, while the issue of continued depreciation of the national currency (Congolese Franc) is attracting the attention of both policymakers and researchers in the DRC, the discussions surrounding it focus primarily on inflation and are generally without reference to the real sphere. As a result, there is a virtual lack of attention to the role of exchange rate management in promoting economic growth and maintaining external competitiveness. It is in this context that this study aims to contribute effectively to macroeconomic policy recommendations by conducting an empirical investigation of the effects of exchange rate volatility on economic growth in the DRC.
To this end, the paper is structured as follows: Section 2 provides a brief review of the literature on the interrelationships between exchange rate volatility and economic growth. Section 3 presents the data and methodology, and results and discussions are discussed in Section 4 and Section 5 focuses on the conclusion of this study.
An extensive literature has evaluated the relationship between the exchange rate and economic growth.
Rodrik (2008) found that there is a positive relationship between real exchange rate undervaluation and growth, especially in developing countries. However, the instability of the real exchange rate relative to its equilibrium can have a positive or negative effect on economic growth. Also, the discussion on how real exchange rate appreciation (or depreciation) affects the economic growth of the host country (region) is essential, but no consistent conclusions have been drawn.
Rapetti et al. (2012) confirmed the promoting effect of real exchange rate depreciation on economic growth. Aizenman and Lee (2010) and Benigno et al. (2015) admit that there are learning effects through practice external to the individual industry in the traded goods sector; therefore a low real exchange rate is necessary to support the production of tradable goods. In these models, an undervalued exchange rate acts as a subsidy to the tradable goods sector. A low real exchange rate compensates for institutional weaknesses and market failures.
A different channel is proposed by Glüzmann et al. (2012) where he finds that a low exchange rate leads to higher savings and investment through lower labor costs and income redistribution. By shifting resources from consumers to financially constrained firms, real devaluation stimulates savings and investment. Zhao et al. (2014) found in their research that the total effect of real exchange rate appreciation is that it contributes to the transformation of the economic growth pattern at both the Chinese and regional levels. Habib et al. (2017) found the same results and confirmed that real exchange rate depreciation increases annual GDP growth in DCs and real exchange rate appreciation decreases GDP growth. Meanwhile, Ybrayev (2021) claimed that there is a positive relationship between real exchange rate undervaluation and the growth of manufactured exports and high-tech manufacturing industries, but that real exchange rate overvaluation increases the growth rate of primary product industries.
Other studies, however, have reached contradictory conclusions. Indeed, in a study on the effects of the exchange rate on economic growth in Morocco between 1988 and 2016, Haoudi and Rabhi (2020) found that the short-run impact of the exchange rate on economic growth is significant after one period, but does not exert the effect in the long-run, which negates the expected effect of price competitiveness in the long-run.
Fluctuations in macroeconomic factors and the dynamic nature of the business environment lead to exchange rate volatility (Anyanwu et al., 2017). The theories that explain this up and down movement of the exchange rate are real options theory, interest rate parity theory, purchasing power parity, traditional flow theory, etc.
Thus, the volatility of the exchange rate as an indicator of uncertainty explains the behavior of investors’ decisions. Stable exchange rates become more attractive for firms that decide to increase their investments. Jamil et al. (2012) examined the effect of volatility on growth over 2 periods for 11 European countries in the European monetary union and 4 countries that have not adopted the euro as their common currency. The results are mixed for the countries in the analysis, but the common currency reduces the adverse impact of exchange rate volatility on industrial output. Moreover, for Germany and Denmark, the impact of exchange rate volatility is negative for both periods, before and after the introduction of a common currency.
Rapetti (2020) estimated the effect of real exchange rate volatility on economic growth and found a positive relationship between the two, especially in DCs. He also mentioned that overvaluation is harmful to economic growth and that real exchange rate volatility has a negative effect on growth. Theoretical and empirical work on developed and developing countries shows mixed results on the relationship between exchange rate volatility and economic growth. Given these results, the study of this relationship in the DRC remains crucial.
To achieve the objective of this research, we have favoured an econometric approach, using Vector Autoregressive Modelling (VAR). This modelling makes it possible to determine the direction of causality between the variables studied and to capture the impacts of one on the other, through the impulse response functions.
Using Eviews 9, this study employs Vector Autoregressive modeling (VAR) for the period 1990 to 2021. The study variables are presented in
This is a VAR (vector autoregression) model that accounts for the dynamic relationship between the change in the exchange rate and inflation (referred to in this model as the consumer price index) and by taking into account other macroeconomic variables.
Sims (1980) criticisms of simultaneous equations (traditional macroeconomic models), in particular the main problem of identification, led to the development of the standard VAR model. This new model has a particular advantage, that of capturing the variation of the parameters (system of equations) over time, and thus allows for a better restitution of the dynamics of the system, which adjusts and adapts to the variations or shocks (innovations) experienced by the economic environment. This model justifies its choice in that it allows us to better grasp the interdependencies between the variables in their long-term dynamics, through impulse response functions.
Thus, a VAR model (1) with seven variables can be specified as follows:
y t = ϕ 0 + ϕ 1 y t − 1 + ϕ 2 y t − 2 + ⋯ + ϕ t − p + u t (1)
With y t = [ y t ⋮ y N t ] , ϕ 0 = [ a t o ⋮ a N o ] , ϕ 0 = [ a 1 1 a 1 2 ⋯ a 1 P N ⋮ ⋮ ⋱ ⋮ a N P 1 a N P 2 ⋯ a N P N ]
| Variables | Description of variable | Characteristic | Source |
|---|---|---|---|
| GDP/capita | It is the gross domestic product per capita that represents the economic growth, it represents our dependent variable. | Continue | WDI, 2021 |
| Exc.r | This variable determines the nature of the relationship between the exchange rate and economic growth. Thus, if the coefficient of the exchange rate is positive, this indicates that a depreciation of the currency improves economic growth and vice versa. | Continue | WDI, 2021 |
| Gfcf | The ratio of gross fixed capital formation to GDP measures physical investment in a given year. | Continue | WDI, 2021 |
| Trade | This indicator is obtained by the rate of trade in relation to GDP to measure the degree of openness of the economy. It includes: exports and imports of goods and services relative to GDP. | Continue | WDI, 2021 |
| Infl | This variable is taken into account to highlight the effect of inflation. High inflation is a structural factor that negatively affects economic growth by reducing investor incentives. | Continue | WDI, 2021 |
| Govt | It is the ratio of total government expenditure to GDP. This variable captures capital accumulation or public investment formation as a source of growth. | Continue | WDI, 2021 |
| Ms | This variable measures the degree of monetization of the economy or the depth of the financial system. | Continue | WDI, 2021 |
Thus Equation (1) can be rewritten:
( I − ϕ 1 L − ϕ 2 L 2 − ⋯ + ϕ p L P ) y t = ϕ 0 + u t (2)
Which can be rewritten as follows:
E ( L ) y t = ϕ 0 + u t (3)
With the identity matrix, the delay operator, ϕ ( L ) = 1 − ∑ ϕ 0 L i and where u t satisfies the properties of white noise.
This model as specified in our study is written as follows:
[ GDP capita Infl gfcf Govt Exc .r Trade Ms ] = [ a 1 o a 2 o a 3 o a 4 o ] + [ a 11 1 a 11 3 a 11 5 a 11 7 a 21 1 a 21 3 a 21 5 a 21 7 a 31 1 a 31 3 a 31 5 a 31 7 a 41 1 a 41 3 a 41 5 a 41 7 ] [ GDP capita t − 1 Inf t − 1 Govt t − 1 Exc .r t − 1 Govt t − 1 Trade t − 1 Ms t − 1 ] + [ a 12 2 a 12 4 a 12 6 a 12 8 a 22 2 a 22 4 a 22 6 a 22 8 a 32 2 a 32 4 a 32 6 a 32 8 a 42 2 a 42 4 a 42 6 a 42 8 ] [ GDP capita t − 2 Inf t − 2 Govt t − 2 Exc .r t − 2 Govt t − 2 Trade t − 2 Ms t − 2 ] + [ u t u t u t u t ]
In order to know the description of the variables (
| Statistiques | GDP/capita | Exc.r | Gfcf | Trade | Infl | Govt | Ms |
|---|---|---|---|---|---|---|---|
| Moyenne | 15.14553 | 765.3805 | −0.949593 | 962.5939 | 797.9621 | 50.77500 | 1.313333 |
| Médiane | 0.000000 | 21.50000 | 0.000000 | 45.50000 | 22.54929 | 23.25000 | 3.150000 |
| Maximum | 451.6122 | 9796.900 | 924.2500 | 13729.00 | 9796.900 | 238.0000 | 9.500000 |
| Minimum | −6.701200 | 0.850000 | −736.2200 | −377.6000 | 0.820000 | 2.000000 | −13.50000 |
| Ecart type | 58.36250 | 2006.623 | 136.1157 | 2560.809 | 2050.658 | 56.93790 | 6.250834 |
| Jarque Bera | 4906.679 | 876.1582 | 3251.099 | 1917.641 | 201.8516 | 17.72310 | 3.122503 |
| Probabilité | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1523.250 | 0.209873 |
| Somme | 1862.900 | 94141.80 | −116.8000 | 118399.1 | 23938.86 | 94015.79 | 39.40000 |
| Nbre d'Obs | 32 | 32 | 32 | 32 | 32 | 32 | 32 |
Note: Author’s calculations.
than the critical significance level of 5%.
It is important to note from the characteristics of the variables under study that not all variables are normally distributed.
Economic statistics makes it possible to discover and measure the various phenomena observed. The strength of linkage or the degree of association between variables is studied with the help of correlation. In other words, it is to know the degree of interdependence between the variables under examination (
Using
- A negative correlation between the exchange rate and economic growth;
- A positive correlation between gross fixed capital formation and economic growth;
- A negative correlation between trade openness and economic growth;
- A negative correlation between public expenditure and economic growth;
- A negative correlation between money supply and economic growth.
Taken as an absolute value, at the 5% threshold, we notice that the value of the ADF statistic for each series is higher than the VCM statistic. With the exception of the series GDP/capita and Gov are stationary at first difference; the other series are level with Dickey-Fuller-Augmented values higher than the VCM statistic in absolute value at the 5% threshold.
It is necessary to verify the properties of the selected series in terms of stationarity (TableA1). In the context of our study, we opt for a significance threshold α = 5%. We apply the Dickey-Fuller-Augmented (DFA) test to determine the individual order of integration of the series as shown in Table4.
| Correlation Coefficient | GDP/capita | Exc.r | Gfcf | Trade | Infl | Govt | Ms |
|---|---|---|---|---|---|---|---|
| GDP/capita | 1.000000 | −0.093999 | 0.010310 | −0.050440 | −0.165086 | 0.827317 | 0.311295 |
| Exc.r | −0.093999 | 1.000000 | −0.074056 | 0.003454 | −0.541912 | 0.311295 | 0.408713 |
| Gfcf | 0.010310 | −0.074056 | 1.000000 | −0.012726 | −0.516831 | 0.459812 | 0.781239 |
| Trade | −0.050440 | 0.003454 | −0.012726 | 1.000000 | 0.982436 | 0.123897 | 0.421398 |
| Infl | −0.165086 | −0.541912 | −0.516831 | 0.982436 | 1.000000 | 0.598723 | 0.123565 |
| Govt | −0.827317 | 0.311295 | 0.459812 | 0.123897 | 0.598723 | 1.000000 | 0.895623 |
| Ms | −0.311295 | 0.408713 | 0.781239 | 0.421398 | 0.123565 | 0.895623 | 1.000000 |
Note: Author’s calculations.
| Series | Statistics | Models | Decision | Degree of integration | ||
|---|---|---|---|---|---|---|
| DFA | MCV 5% | MCV 10% | ||||
| GDP/capita | −4.809160 | −2.971853 | −2.625121 | With intercept | Stationary to the first difference | I(1) |
| Infl | −17.77835 | −2.991878 | −2.635542 | With intercept | Stationary in level | I(0) |
| gfcf | −4.684143 | −2.967767 | −2.622989 | With intercept | Stationary in level | I(0) |
| Govt | −5.529374 | −2.971853 | −2.625121 | With intercept | Stationary to the first difference | I(1) |
| Exc.r | −3.184414 | −2.938987 | −2.607932 | With constant and trend | Stationary in level | I(0) |
| Trade | −5.085750 | −3.612199 | −3.243079 | With constant and trend | Stationary in level | I(0) |
| Ms | −10.04050 | −2.998064 | −2.638752 | With intercept | Stationary in level | I(0) |
Author’s calculations.
To determine the number of lags p of a VAR model, we use the criteria of Akaike and Schwartz. We will use the criteria of Akaike (AIC) and Schwarz (SC) for lags p ranging from 0 to 8.
Taking into account all the different criteria mentioned above, we retain the first-order lag. This means that our model will be estimated with the first-order lag. Before estimating the VAR itself, it is recommended that we carry out a causality test in order to know which equations are the most relevant to analyse (see TableA2 in the appendix).
The notion of causality plays a very important role in economics in that it allows us to better understand the relationships between variables. However, one of the specificities of the VAR model is that it allows the study of impacts and causalities between related variables. TableA3 of the Granger causality test shows that the exchange rate, the inflation rate and trade openness cause economic growth at the 1%, 5% and 10% threshold respectively.
The results of the estimation obtained from the VAR model with a lag number of 1 are reported in TableA4.
This is the crucial part of the model; it is the very purpose of the model. The VAR model is often analysed through its dynamics, via the simulation of random shocks (impulse responses) (
1) Impulse response analysis
The aim is to demonstrate the extent to which economic growth reacts (responses) to shocks or innovations (impulses) on the inflation rate, public spending, the exchange rate, the growth rate of the money supply, investment and trade openness.
| Response of GDP/Capita | |||||||
|---|---|---|---|---|---|---|---|
| Period | GDP/Capita | Infl | Gfcf | Govt | Exc.r | Trade | Ms |
| 1 | 58.10235 | −373.4973 | 31.45795 | 108.45772 | 0.000000 | 24.39200 | 456.99452 |
| 2 | 89.57898 | −105.9236 | 31.39814 | 95.60982 | −20.12919 | 24.37100 | 372.31226 |
| 3 | 109.34227 | −54.56273 | 31.51232 | 53.93090 | −6.50353 | 136.1028 | 214.38290 |
| 4 | 123.16666 | −17.46282 | 31.26835 | 32.5222 | 5.956485 | 177.9470 | 159.43603 |
| 5 | 132.03898 | 25.68258 | 48.520955 | 29.68694 | 28.94066 | 189.3474 | 159.47463 |
| 6 | 153.94566 | 43.974686 | 5918.51032 | 14.04185 | 43.11671 | −19.214493 | 126.50289 |
| 7 | 176.87727 | −35.20243 | 81.30138 | −22.31557 | 58.19079 | −30.83441 | 119.52361 |
| 8 | 188.82704 | 76.30034 | 111.61686 | −44.58501 | 72.66344 | −26.45725 | 98.53883 |
| 9 | 198.79007 | 153.78368 | 136.96581 | −68.74068 | 143.0976 | −6.406550 | 32.55004 |
| 10 | 201.76280 | 225.72965 | 147.46074 | −109.07700 | 181.7087 | 8.731103 | −46.55831 |
Note: Author’s calculations.
| Variance Decomposition of GDP/Capita | ||||||||
|---|---|---|---|---|---|---|---|---|
| Period | S.E. | GDP/Capita | Infl | Gfcf | Govt | Exc.r | Trade | Ms |
| 1 | 58.99452 | 77.44043 | 1.672598 | 0.000000 | 0.00000 | 21.102368 | 5.429770 | 0.000000 |
| 2 | 59.31226 | 61.85642 | 2.330381 | 1.725141 | 0.94923 | 23.41844 | 9.59119 | 0.129198 |
| 3 | 59.38290 | 38.31935 | 6.675885 | 1.614805 | 1.491997 | 35.63869 | 15.76742 | 0.491858 |
| 4 | 59.43603 | 35.32981 | 6.619731 | 1.559979 | 2.421324 | 36.110215 | 17.15059 | 0.808351 |
| 5 | 59.47463 | 30.34473 | 6.718546 | 1.738083 | 2.904645 | 37.91719 | 18.40578 | 1.971083 |
| 6 | 59.50289 | 27.08764 | 6.694582 | 1.987773 | 2.935486 | 39.92458 | 20.21537 | 2.020102 |
| 7 | 59.52361 | 22.02076 | 5.689815 | 2.020278 | 2.918333 | 40.95896 | 24.077291 | 2.314563 |
| 8 | 59.53883 | 18.02656 | 6.694864 | 2.021794 | 2.967543 | 41.95165 | 26.22852 | 2.410235 |
| 9 | 59.55004 | 15.98881 | 4.693327 | 2.026854 | 2.992674 | 42.98434 | 29.25250 | 2.521034 |
| 10 | 59.55831 | 12.97772 | 6.694146 | 2.026406 | 3.026406 | 42.99587 | 31.25969 | 2.003258 |
Note: Author’s calculations.
A shock to fiscal and monetary policy in terms of increased government spending and money supply growth respectively results in a general decrease in economic growth throughout the period. The economic growth rate per capita is positively related to its past in all periods. A 1% shock to the exchange rate in terms of growth on the per capita economic growth rate results in a zero effect in the 1st period and an increase for the other 9 periods. A shock to GFCF results in a zero effect in period 1 to period 4 and an increase from period 5 to period 10. A shock of 1% to the inflation rate on the economic growth rate per capita results in a zero effect in the first period and a decrease in the other nine periods. A shock to trade policies in terms of trade openness results in a zero effect, an increase and a decrease in the economic growth rate per capita throughout the period.
2) Decomposition of variance
Based on the results of the variance decomposition, it appears that the variance of the GDP/capita forecast error is mainly influenced on average by its own innovations (33.95%) and by the shock to the exchange rate (36.31%) but also by the shock due to trade openness (19.75%). GDP/capita reacts less significantly to variations in the GFCF, public expenditure and the growth rate of the money supply.
The remarkable contribution of the exchange rate and trade openness on economic growth is justified by the extraversion of the Congolese economy (small open economy) characterised mainly by the export of raw materials and the import of value added products. Theoretically, a depreciation of the national currency (high exchange rate volatility) should make exports relatively cheaper, leading to an increase in demand for exports and, by extension, economic performance and vice versa.
However, in the context of the DRC, the depreciation of the national currency is a brake on economic growth. These results corroborate the work of Rapetti (2020); Ziadi and Abdallah (2007) who argue that exchange rate volatility has a negative effect on economic growth in developing countries because of the high external dependence of the economy.
The economic performance of a country depends on its competitiveness in international trade. The effects of exchange rate volatility on economic growth have always been a controversial issue in the economic literature. With an extroverted, dollarized and commodity-dependent economy, the exchange rate is an important determinant of the Congolese economy. Indeed, since the early 1990s, the Congolese economy has suffered from a continuous depreciation of its national currency due to its dependence on the outside world, which has made economic activity unstable.
Using the VAR model, the empirical results showed a significant impact of exchange rate volatility on economic growth. These results suggest that the resilience of the Congolese economy should be strengthened by diversifying economic activity to boost its international competitiveness. Nevertheless, taking into account the determinants of the exchange rate in the relationship between the exchange rate and economic growth will help refine the results of future work.
The authors declare no conflicts of interest regarding the publication of this paper.
Kabamba Mbuyi, A., Kato-Kale Kakasi, C., Muya Ntumba, C., & Imbaleva Mpebale, E. (2022). Exchange Rate Volatility and Economic Growth in the Democratic Republic of Congo (DRC). Modern Economy, 13, 729-746. https://doi.org/10.4236/me.2022.135039
| Economic Growth Series | ||||
|---|---|---|---|---|
| Null Hypothesis: D(GDP/capita) has a unit root | ||||
| Exogenous: Constant | ||||
| Lag Length: 0 (Automatic—based on SIC, maxlag = 7) | ||||
| t-Statistic | Prob.* | |||
| Augmented Dickey-Fuller test statistic | −5.508260 | 0.0003 | ||
| Test critical values: | 1% level | −4.603564 | ||
| 5% level | −2.902357 | |||
| 10% level | −2.862354 | |||
| Inflation Series | ||||
|---|---|---|---|---|
| Null Hypothesis: Infl has a unit root | ||||
| Exogenous: Constant | ||||
| Lag Length: 5 (Automatic—based on SIC, maxlag = 7) | ||||
| t-Statistic | Prob.* | |||
| Augmented Dickey-Fuller test statistic | −13.20135 | 0.0006 | ||
| Test critical values: | 1% level | −3.023452 | ||
| 5% level | −2.452103 | |||
| 10% level | −2.412034 | |||
| Gross fixed capital formation series | ||||
|---|---|---|---|---|
| Null Hypothesis: Gfcf has a unit root | ||||
| Exogenous: Constant | ||||
| Lag Length: 0 (Automatic—based on SIC, maxlag = 7) | ||||
| t-Statistic | Prob.* | |||
| Augmented Dickey-Fuller test statistic | −5.403587 | 0.0006 | ||
| Test critical values: | 1% level | −3.120358 | ||
| 5% level | −2.542089 | |||
| 10% level | −2.622989 | |||
| Public expenditure series | ||||
|---|---|---|---|---|
| Null Hypothesis: D(Govt) has a unit root | ||||
| Exogenous: Constant | ||||
| Lag Length: 0 (Automatic—based on SIC, maxlag = 7) | ||||
| t-Statistic | Prob.* | |||
| Augmented Dickey-Fuller test statistic | −5.4120387 | 0.0003 | ||
| Test critical values: | 1% level | −2.645201 | ||
| 5% level | −2.421302 | |||
| 10% level | −2.621489 | |||
| Exchange rate series | ||||
|---|---|---|---|---|
| Null Hypothesis: D(Exc.r) has a unit root | ||||
| Exogenous: Constant, Linear Trend | ||||
| Lag Length: 1 (Automatic—based on SIC, maxlag = 9) | ||||
| t-Statistic | Prob.* | |||
| Augmented Dickey-Fuller test statistic | −4.110235 | 0.0465 | ||
| Test critical values: | 1% level | −2.459876 | ||
| 5% level | −2.421688 | |||
| 10% level | −2.456897 | |||
| Commercial Opening Series | ||||
|---|---|---|---|---|
| Null Hypothesis: Trade has a unit root | ||||
| Exogenous: Constant, Linear Trend | ||||
| Lag Length: 5 (Automatic—based on SIC, maxlag = 7) | ||||
| t-Statistic | Prob.* | |||
| Augmented Dickey-Fuller test statistic | −5.452100 | 0.0002 | ||
| Test critical values: | 1% level | −3.469863 | ||
| 5% level | −3.986329 | |||
| 10% level | −3.853146 | |||
| Money supply growth rate series | ||||
|---|---|---|---|---|
| Null Hypothesis: Ms has a unit root | ||||
| Exogenous: Constant | ||||
| Lag Length: 6 (Automatic—based on SIC, maxlag = 7) | ||||
| t-Statistic | Prob.* | |||
| Augmented Dickey-Fuller test statistic | −10.42013 | 0.0001 | ||
| Test critical values: | 1% level | −3.489536 | ||
| 5% level | −2.963542 | |||
| 10% level | −2.875423 | |||
* MacKinnon (1996) one-sided p-values. Note: Author’s calculations.
* MacKinnon (1996) one-sided p-values. Note: Author’s calculations.
* MacKinnon (1996) one-sided p-values. Note: Author’s calculations.
* MacKinnon (1996) one-sided p-values. Note: Author’s calculations.
* MacKinnon (1996) one-sided p-values. Note: Author’s calculations.
* MacKinnon (1996) one-sided p-values. Note: Author’s calculations.
* MacKinnon (1996) one-sided p-values. Note: Author’s calculations.
| VAR Lag Order Selection Criteria | ||||||
|---|---|---|---|---|---|---|
| Endogenous variables: GDP/Capita Infl gfcf Govt Exc.r Trade Ms | ||||||
| Exogenous variables : C | ||||||
| Date: 28/02/22 Time: 10:25 | ||||||
| Sample: 1990 2021 | ||||||
| Included observations: 32 | ||||||
| Lag | LogL | LR | FPE | AIC | SC | HQ |
| 0 | −3451.419 | NA | 1.47e+21 | 60.09424 | 60.18972 | 60.13299 |
| 1 | −3309.190 | 272.0905 | 1.64e+20* | 57.89895* | 58.37633* | 58.09272* |
| 2 | −3307.569 | 2.988290 | 2.11e+20 | 58.14902 | 59.00830 | 58.49780 |
| 3 | −3305.524 | 3.627581 | 2.70e+20 | 58.39172 | 59.63290 | 58.89551 |
| 4 | −3281.723 | 40.56430* | 2.37e+20 | 58.25606 | 59.87915 | 58.91486 |
| 5 | −3268.613 | 21.43321 | 2.51e+20 | 58.30630 | 60.31130 | 59.12012 |
| 6 | −3267.786 | 1.294335 | 3.30e+20 | 58.57018 | 60.95708 | 59.53901 |
| 7 | −3266.813 | 1.453973 | 4.36e+20 | 58.83154 | 61.60034 | 59.95538 |
| 8 | −3264.079 | 3.898910 | 5.61e+20 | 59.06225 | 62.21296 | 60.34111 |
| *indicates lag order selected by the criterion | ||||||
| LR: sequential modified LR test statistic (each test at 5% level) | ||||||
| FPE: Final prediction error | ||||||
| AIC: Akaike information criterion | ||||||
| SC: Schwarz information criterion | ||||||
| HQ: Hannan-Quinn information criterion | ||||||
Note: Author’s calculations.
| Pairwise Granger Causality Tests | |||
|---|---|---|---|
| Date: 28/02/22 Time: 13:57 | |||
| Sample: 1990 2021 | |||
| Lags: 2 | |||
| Null Hypothesis: | Obs | F-Statistic | Prob. |
| GDP/Capita does not Granger Cause Infl | 32 | 0.09021 | 0.9138 |
| Infl does not Granger Cause GDP/Capita | 0.71253 | 0.0425 | |
| GDP/Capita does not Granger Cause gfcf | 32 | 0.03267 | 0.9679 |
| gfcf does not Granger Cause GDP/Capita | 0.20492 | 0.8150 | |
| GDP/Capita does not Granger Cause Govt | 32 | 0.37524 | 0.8995 |
| Govt does not Granger Cause GDP/Capita | 0.00624 | 0.9938 | |
| GDP/Capita does not Granger Cause Exc.r | 32 | 0.02997 | 0.9705 |
| Exc.r does not Granger Cause GDP/Capita | 0.00045 | 0.0019 | |
| GDP/Capita does not Granger Cause Trade | 32 | 0.37516 | 0.6880 |
| Trade does not Granger Cause GDP/Capita | 0.02505 | 0.0973 | |
| GDP/Capita does not Granger Cause Ms | 32 | 0.00557 | 0.9944 |
| Ms does not Granger Cause GDP/Capita | 0.00226 | 0.9977 | |
| Infl does not Granger Cause gfcf | 32 | 0.51873 | 0.5349 |
| gfcf does not Granger Cause Infl | 0.03218 | 0.2458 | |
| Infl does not Granger Cause Govt | 32 | 0.03201 | 0.1485 |
| Govt does not Granger Cause Infl | 0.01285 | 0.9995 | |
| Infl does not Granger Cause Exc.r | 32 | 0.37516 | 0.6880 |
| Exc.r does not Granger Cause Infl | 0.02505 | 0.9753 | |
| Infl does not Granger Cause Exc.r | 32 | 0.07524 | 0.1257 |
| Exc.r does not Granger Cause Infl | 0.00205 | 0.7412 | |
| Infl does not Granger Cause Trade | 32 | 0.04102 | 0.9938 |
| Trade does not Granger Cause Infl | 0.02038 | 0.7705 | |
| Infl does not Granger Cause Ms | 32 | 0.34106 | 0.9995 |
| Ms does not Granger Cause Infl | 0.04879 | 0.1280 | |
| gfcf does not Granger Cause Govt | 32 | 0.00126 | 0.8752 |
| Govt does not Granger Cause gfcf | 0.03685 | 0.6521 | |
| gfcf does not Granger Cause Exc.r | 32 | 0.03527 | 0.7125 |
| Exc.r does not Granger Cause gfcf | 0.05011 | 0.8541 | |
| gfcf does not Granger Cause Trade | 32 | 0.63250 | 0.2413 |
| Trade does not Granger Cause gfcf | 0.10232 | 0.3541 | |
| gfcf does not Granger Cause Ms | 32 | 0.63251 | 0.7432 |
| Ms does not Granger Cause gfcf | 0.03210 | 0.3258 | |
| Govt does not Granger Cause Exc.r | 32 | 0.00350 | 0.4123 |
| Exc.r does not Granger Cause Govt | 0.02320 | 0.3896 | |
| Govt does not Granger Cause Trade | 32 | 0.03205 | 0.7474 |
| Trade does not Granger Cause Govt | 0.03652 | 0.3592 | |
| Govt does not Granger Cause Ms | 32 | 0.06320 | 0.2987 |
| Ms does not Granger Cause Govt | 0.69832 | 0.1875 | |
| Exc.r does not Granger Trade | 32 | 0.41035 | 0.7410 |
| Trade does not Granger Cause Exc.r | 0.06320 | 0.5369 | |
| Exc.r does not Granger Ms | 32 | 0.02045 | 0.7459 |
| Ms does not Granger Cause Exc.r | 0.03210 | 0.2589 | |
| Trade does not Granger Ms | 32 | 0.00158 | 0.6523 |
| Ms does not Granger Cause Trade | 0.15892 | 0.1963 | |
| Vector Autoregression Estimates | |||||||
|---|---|---|---|---|---|---|---|
| Date: 02/03/22 Time: 12:20 | |||||||
| Sample (adjusted): 1990 2021 | |||||||
| Included observations: 31 after adjustments | |||||||
| Standard errors in ( ) & t-statistics in [ ] | |||||||
| GDP/Capita | Infl | Gfcf | Govt | Exc.r | Trade | Ms | |
| GDP/Capita(−1) | 1.418854 | −3.786012 | −111.1354 | −3.786012 | −111.1354 | 3.103907 | 117.9032 |
| (0.20764) | (2.48259) | (39.5720) | (2.48259) | (39.5720) | (3.47090) | (55.3256) | |
| [6.83331] | [−1.52503] | [−2.80843] | [−1.52503] | [−2.80843] | [0.89427] | [2.13108] | |
| Infl(−1) | −0.960195 | −0.075746 | −0.428607 | 0.003982 | −0.488403 | −0.488403 | −0.488403 |
| (0.29030) | (0.09357) | (1.61868) | (0.22045) | (2.49056) | (2.49056) | (2.49056) | |
| [−3.30762] | [−0.00850] | [−0.26479] | [0.11806] | [−0.19610] | [−0.19610] | [−0.19610] | |
| Gfcf(−1) | 0.255994 | −0.002815 | 0.865075 | 0.000201 | −0.000929 | −0.000929 | −0.000929 |
| (0.17501) | (0.00268) | (0.04636) | (0.00631) | (0.07133) | (0.07133) | (0.07133) | |
| [1.46275] | [−1.05036] | [18.6605] | [0.03187] | [−0.01303] | [−0.01303] | [−0.01303] | |
| Govt(−1) | 0.011458 | 0.004104 | −0.118666 | −0.000262 | −0.010208 | −0.010208 | −0.010208 |
| (0.01431) | (0.03999) | (0.69181) | (0.09422) | (1.06444) | (1.06444) | (1.06444) | |
| [0.80078] | [0.10263] | [−0.17153] | [−0.00278] | [−0.00959] | [−0.00959] | [−0.00959] | |
| Exc.r(−1) | −0.001356 | 0.001674 | −0.021657 | −0.000490 | 0.798297 | 0.798297 | 0.798297 |
| (0.00471) | (0.00209) | (0.03613) | (0.00492) | (0.05559) | (0.05559) | (0.05559) | |
| [−0.28808] | [−0.00161] | [−0.59945] | [−0.09965] | [14.3609] | [14.3609] | [14.3609] | |
| Trade(−1) | −0.000226 | 0.001674 | −0.021657 | −0.000490 | 0.798297 | 0.798297 | 0.798297 |
| (0.00098) | (0.00209) | (0.03613) | (0.00492) | (0.05559) | (0.05559) | (0.05559) | |
| [−0.23067] | [0.80161] | [−0.59945] | [−0.09965] | [14.3609] | [14.3609] | [14.3609] | |
| Ms(−1) | −0.001024 | 0.001674 | −0.021657 | −0.000490 | 0.798297 | 0.798297 | 0.798297 |
| (0.00100) | (0.00209) | (0.03613) | (0.00492) | (0.05559) | (0.05559) | (0.05559) | |
| [−1.01980] | [0.80161] | [−0.59945] | [−0.09965] | [14.3609] | [14.3609] | [14.3609] | |
| C | −0.000312 | 16.99410 | 129.5185 | −0.702794 | 211.9346 | 211.9346 | 211.9346 |
| (0.00095) | (6.29739) | (108.938) | (14.8362) | (167.617) | (167.617) | (167.617) | |
| [−0.32760] | [2.69859] | [1.18892] | [−0.04737] | [1.26440] | [1.26440] | [1.26440] | |
| R-squared | 0.794468 | 0.019555 | 0.751795 | 0.000097 | 0.638993 | 0.638993 | 0.638993 |
| Adj. R-squared | 0.717393 | −0.013965 | 0.743310 | −0.034088 | 0.626651 | 0.626651 | 0.626651 |
| Sum sq. resids | 25.38916 | 407201.4 | 1.22E+08 | 2260131. | 2.88E+08 | 2.88E+08 | 2.88E+08 |
| S.E. equation | 1.028534 | 58.99452 | 1020.544 | 138.9869 | 1570.248 | 1570.248 | 1570.248 |
| F-statistic | 10.30778 | 0.583382 | 88.59632 | 0.002842 | 51.77344 | 51.77344 | 51.77344 |
| Log likelihood | −43.27926 | −668.0061 | −1015.785 | −772.5522 | −1068.354 | −1068.354 | −1068.354 |
| Akaike AIC | 3.134074 | 11.03289 | 16.73418 | 12.74676 | 17.59597 | 17.59597 | 17.59597 |
| Schwarz SC | 3.583004 | 11.14781 | 16.84910 | 12.86168 | 17.71089 | 17.71089 | 17.71089 |
| Mean dependent | 5.958824 | 15.26967 | 769.7426 | −0.957377 | 970.4840 | 970.4840 | 970.4840 |
| S.D. dependent | 1.934761 | 58.58686 | 2014.313 | 136.6769 | 2569.868 | 2569.868 | 2569.868 |
| Determinant resid covariance (dof adj.) | 1.56E+20 | ||||||
| Determinant resid covariance | 1.32E+20 | ||||||
| Log likelihood | −3518.442 | ||||||
| Akaike information criterion | 58.00725 | ||||||
| Schwarz criterion | 58.46692 | ||||||