Chapter 4 Predictors Of Foreign Capital Volatility
4.15 Inferences And Comparisons Between The Two P-Ardl Models In Sadc Countries
This chapter investigated the main predictors of net foreign remittance volatility and net foreign portfolio investment volatility in low-income SADC countries using two P-ARDL models. In pooling the data together, the findings from both models do not show the presence of cross- sectional dependence or common factors affecting the cross-sectional units in the SADC countries.
This is similar to Pesaran (2004; 2007) who corrected for cross-sectional dependence in his study on general diagnostic tests for cross-section dependence in panels and a simple panel unit root test in the presence of cross‐section dependence. The results in the two P-ARDL models showed that the t-statistic value of 21.79118 is greater than the Pesaran table value and since the p-value of 0.0000 is statistically significant at 5%, the study failed to reject the null hypothesis of no correlation of the residuals and rejected the alternative hypothesis that correlation of the residuals exists in the model. The finding is also similar to Gow et al. (2010) that corrected for cross- sectional and time-series dependence in accounting research due to its negative effect on panel analysis.
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Furthermore, the unit root test results are consistent with previous empirical studies that employed P-ARDL such as Kutu and Ngalawa (2016a). In addition, the unlimited likelihood ratio test for the lag lengths conducted for various lag selection criteria for the purpose of determining the optimum lag for the two P-ARDL models chose lag 3 as the optimum lag. These various orders of lags were conducted using the sequential modified LR test statistic (LR), Final Prediction Error (FPE), Akaike Information Criterion (AIC), Schwarz Bayesian Information Criterion (SBIC) and Hannan-Quinn Information Criterion (HQIC). Lag 3 was found to give the minimum criteria for the value of LR, FPE, AIC and HQ which are now the optimal lag length for the variables in the two models. This is consistent with Nowak-Lehmann et al. (2011), Mijinyawa (2015) and Olarewaju et al. (2017). The criteria graphs also exhibit similar trends; it was found that the AIC gives the most minimum number based on the benchmark hypothesis that the smaller the number, the better the model selection for the lag length. The ARDL (4, 4, 4, 4, 4, 4, 4) model was found to be strongly chosen over the other models.
However, there are some disparities in the two models. For example, in the first model, world GDP does not significantly impact net foreign remittance volatility in both the short and long run. This means that world GDP does not serve as a main predictor for net foreign remittance flow volatility in low-income SADC countries. Regardless of the world output growth rate, foreign migrant workers might still transfer money to their family or other individuals at home. However, in the second model, all the factors in the equation were statistically sound in the short run and in the long run; and hence determine net foreign portfolio investment volatility in SADC countries. These results are consistent with theory and empirical studies such as Glytsos (2005), Ramirez (2006), and Akinlo (2004), among others.
Furthermore, the two P-ARDL models provide evidence of cointegration analyses among the variables employed in the models during the period under investigation. In both findings, the study does not have sufficient evidence to accept the null hypothesis of no cointegration in the model and has therefore accepted the alternative hypothesis that there is a cointegration relationship among the variables in the model. This therefore revealed the evidence of cointegration among the variables employed. Given the values of F-statistics that are significant and positive for the two models, this is an indication of a long-run cointegration association between net foreign remittance volatility and other variables in the first model as well as between net foreign portfolio investment
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volatility and other variables in the second model. These findings are in line with various empirical studies such as Giuliano and Ruiz-Arranz (2009), Yang (2008), Li and Liu (2005) and Jahjah et al. (2003), among others. It can therefore be concluded that both net foreign remittance flows and net foreign portfolio investment flows are sources of development for SADC countries.
Finally, employing the ECT for the two models after the evidence of cointegration was revealed, the study found that there is a comparatively high speed of adjustment from which disequilibrium in the short run can be restored back to equilibrium in the long run for the two models. Specifically, the first result showed that about 71% deviation of the net foreign remittance flow volatility in the short run is restored back to equilibrium in the long run in low-income SADC countries while the second result revealed that about 73% deviation of the net foreign portfolio investment volatility in the short run is restored back to equilibrium in the long run in these economies. The values of the ECT for the two models were significant at 5% which is an indication that long-run equilibrium is attainable and that the system converges in the long run. These findings are supportive of those of Waliullah and Rabbi (2011), Kutu and Ngalawa (2016a) and Banerjee, Dolado, and Mestre (1998) who contended that a highly significant ECT is further evidence of the existence of a steady long-run relationship among the variables employed in any model.
The following chapter analyses the causal relationships or financial linkages between capital flow volatility, financial deepening and the performance of capital markets using the panel vector error correction model (P-VECM) in the context of SADC low-income countries.
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