INTRODUCTION
This chapter presents the empirical analysis for interpreting the results obtained from the methodology used and discussed in the earlier chapter. The emphasis of the empirical investigation is to analyze the processes shown on the estimated Panel Vector Autoregressive (P-VAR) model to determine the impact of changes in net foreign capital flow volatility on low-income economies.
The chapter is divided into two sections that separately analyze the impacts of net foreign remittance volatility and the impacts of net foreign portfolio investment volatility.
In total, 6 variables are ordered to determine the simultaneous relationships in the model. The Orthogonalization Cholesky analysis procedures take account of the chosen lag length criteria, impulse response functions, variance decompositions and economic interpretation of the econometric results. The results obtained from these procedures are derived from the levels specification of the P-VAR which is in line with various empirical studies (Smets and Wouters, 2002, Uhlig, 2005, Vonnák, 2005, Cheng, 2006, Seleteng and Motelle, 2016).
6.1 NON-STATIONARITY
In line with Fève and Guay (2010), Uhlig (2005) and Ibrahim and Amin (2005), this investigation applies a levels VAR. The advantage of using level VAR is to avoid the loss of important information about the data sets which might occur in the process of differencing. In addition, it has been posited that the addition of lagged lengths of the variables in the VAR will allow the residuals to be stationary even with non-stationary data (Berkelmans, 2005). Many recent empirical studies have also employed this technique, including Ngalawa and Viegi (2011), Elbourne (2008) and Mordi and Adebiyi (2010). In conformity with the aim and objectives of this study, this level VAR is further carried out in a panel form in order to cover all the SADC countries and ensure a large sample size with large degree of freedom. Theoretically, the larger the sample size, the better the estimates. Therefore, the PVAR will offer a better estimate for this study.
177 6.2 THE LAG LENGTH
The lag length provides a suggestion of the time between policy action responses to volatility in the economy in order to determine the impact of changes in net foreign remittance flow volatility on low-income economies. Since the data sets are quarterly, the study tests for different types of lag selection criteria to allow for variations in the model and the realization of well-behaved residuals. Therefore, the different types of lag length tested for this model follow the standard Akaike Information Criteria (AIC), Sequential Modified LR test statistic (LR), Final Prediction Error (FPE), Schwartz Bayesian Information Criteria (SBIC) and Hannan-Quinn Information Criteria (HQIC). They all suggested that an optimal lag length of four would suit for the model.
Table 6.1 presents the P-VAR lag order selection criteria using different lag lengths (lag order of 5). On the basis of the results obtained, the LR, FPE, AIC, SBIC and the HQIC selected 4-lags.
All of them give the minimum number among the lag lengths by choosing 4-lag length for the overall analysis of this study. These 4-lags tend to suggest values of k that are generally too small for unit root tests to have good sizes and prevent distortion (Ng and Perron, 2001). The selection of the optimum 4-lags is well supported by previous literature (Elbourne, 2008) and is an attempt to achieve a robust and dynamic result without necessarily shortening the analysis sample.
Table 6.1: The P-VAR Lag Order Selection Criteria
Endogenous variables: LOGFFR LOGRGDP LOGNFR LOGMS LOGCPI INT Exogenous variables: C
Sample: 2000Q1 2015Q4
Lag LogL LR FPE AIC SIC HQC
0 -2462.580 NA 136.4108 21.94293 22.03403 21.97970 1 998.0945 6706.018 8.21e-12 -8.498618 -7.860946 -8.241251 2 1976.534 1843.814 1.89e-15 -16.87585 -15.69161 -16.39789 3 3139.331 2129.211 8.46e-20 -26.89183 -25.16100 -26.19326 4 29659.38 47146.75* 4.9e-122* -262.3056* -260.0282* -261.3864*
5 35250.55 8814.008 2.77e-63 30.87651 31.00987 30.00048 * 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
Source: Author’s computation from output result from the regression analysis
178 6.3 DIAGNOSTIC TESTS ON THE PVAR MODEL
After the lag length tests revealed an optimum lag length of 4 to be the best fit for the model, the study further tests for the P-VAR normality, heteroskedasticity and serial correlation in order to show the appropriateness and robustness of the model. The benchmark criteria for the null hypotheses that are tested for the serial correlation, heteroscedasticity and normality tests are:
▪ 𝐻0: 𝛼 = 1, there is normality of the residuals, no heteroskedasticity and no serial correlation.
▪ 𝐻1: 𝛼 ≠ 1, there is non-normality of residuals, heteroskedasticity and serial correlation.
Table 6.2: The P-VAR Normality test
Com Skewness Kurtosis Jarque-Bera
Skew Chi-sq Df Prob Kurt Chi-sq df Prob Jarque df Prob
1 0.034674 0.064925 1 0.7989 3.951912 12.23285 1 0.0005 12.29778 2 0.0021
2 -
0.333575 6.008719 1 0.0142 3.688285 6.395437 1 0.0114 12.40416 2 0.0020
3 -
0.131801 0.938067 1 0.3328 2.822436 0.425643 1 0.5141 1.363710 2 0.5057 4 0.925790 46.28274 1 0.0000 3.574224 4.451404 1 0.0349 50.73414 2 0.0000 5 0.326719 5.764248 1 0.0164 2.629609 1.852055 1 0.1735 7.616302 2 0.0222 6 0.725884 28.45299 1 0.0000 6.439233 159.6823 1 0.0000 188.1353 2 0.0000 Joint Joint 87.51168 6 0.0000 185.0397 6 0 272.5514 18 0.0000
Source: Author’s computation from output result from the regression analysis
“***”, “**” and “*” represent statistical significance at 1%, 5%, and 10%, respectively
In Table 6.2, the normality test is shown on the basis of skewness, kurtosis and Jarque-Bera. The findings show that 95% of the variables in the model are normally distributed and passed the normality test individually and jointly. It is further revealed that the residuals are normally distributed and the data sets are well modelled. This is shown by the probability values at 1% and 5% level of significance. The effect is that the data distribution and the residuals of the model for the SADC countries are normally distributed. Overall, the null hypothesis of normality of the residuals, no heteroscedasticity and no serial correlation cannot be rejected. These results show that our model is reliable and advantageous in determining the impact of changes in net foreign capital flow volatility on low-income SADC countries.
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Table 6.3 presents the heteroscedasticity test results for the model in determining the impact of changes in net foreign remittance flow volatility on low-income economies. The probability value confirms that there is no heteroscedasticity in the model.
Table 6.3: Heteroscedasticity Test
Heteroscedasticity Test: joint test Null Hypothesis: no Heteroscedasticity
Chi-sq Df Prob.
5424.000 446 0.2110
Source: Author’s computation from output result from the regression analysis
“***”, “**” and “*” represent statistical significance at 1%, 5%, and 10%, respectively Table 6.4: Serial Correlation LM Test
Null Hypothesis: no serial correlation at lag order h
Lags LM-Stat Prob
1 101.5999 0.5207
2 142.8431 0.0703
3 141.5943 0.2276
4 556.4239 0.1729
5 93.39794 0.0938
6 97.93459 0.2641
Source: Author’s computation from output result from the regression analysis
“***”, “**” and “*” represent statistical significance at 1%, 5%, and 10%, respectively
Furthermore, Table 6.4 above presents the test for autocorrelation for the equation in determining the impact of changes in net foreign capital flow volatility on low-income economies. The probability value confirms that the model is free from serial correlation. This means that there is no serial correlation found in repeating patterns, where the current level of a variable affects its future level.
6.4 THE IMPULSE RESPONSE FUNCTIONS
An impulse response indicates how any dynamic system reacts in response to external changes in the economy. The P-VAR estimation system applied in this study helps to determine the effect of changes in net foreign remittance flow volatility on low-income SADC economies. The impulse response functions will further help to analyze the dynamic characteristics of the model in achieving the study’s objective.
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Therefore, the impulse response is developed from shocks or volatility to all the variables in the P-VAR model as it provides an exogenous source of volatility that enables us to detect the economy’s response to policy shocks in net foreign remittance flow volatility. In addition, it provides an appropriate summary and properties of the relationships of the larger number of estimated coefficients in the model. The impulse response functions described in the graphs are generally in line with the modern empirical evidence for African countries and many other regions (Tsangarides, 2010, Oni, 2013). The graphs show the impulse impact of Cholesky one-standard innovation or deviation of policy shock defined as an exogenous, unanticipated and temporary rise in all the variables in the P-VAR model. Impulse response functions show the economy’s response to volatility. The dynamic impacts of various macro-economic and financial shocks on policy and non-policy related variables in the model that shape the effect of changes in net foreign remittance flow volatility in low-income SADC economies are shown below.
6.4.1 THE IMPULSE RESPONSE OF FEDERAL FUND RATES (FFR)
Since the study employed quarterly series, the periodical bases covering a 12-quarter horizon are covered. However, in order to achieve a simple and suitable analysis of the impulse response functions, they are further divided into 4-quarter to cover the 12-quarter horizon (as shown on the horizontal axis in Figures 6.1 to 6.6) to highlight the economy’s response to the volatility and identify the process through which it occurs and is transmitted to other variables in the model.
Figure 6.1 shows the impulse response function of the external shocks from the global market and the impact of global shocks and volatility on the low-income economies. Following Kutu and Ngalawa (2016b), the study utilizes the Federal Funds Rate (FFR) as a proxy for global interest rates. It is believed that shock/volatility is transmitted from the global market to the domestic market and not the other way around and that the transmission of international shock/volatility can be very rapid (Berkelmans, 2005). Thus, global interest rates serve as a channel through which shock/volatility is transmitted from the global market to the domestic market. Based on the results in Figure 6.1, domestic interest rates do not significantly impact the global interest rate. This is because, when it comes to monetary policy formulation, the US acts as a leader (being the most industrialized economy in the world). In addition, the Dollar influences SADC economies as most trading and transactions in the region are done in foreign currency. Hence, the domestic interest rate does not affect the global interest rate. However, shocks to GDP, NFR, MS and CPI