What drives volatility in foreign exchange market in Pakistan? This paper undertakes an analysis of modelling exchange rate volatility in Pakistan by potential macroeconomic fundamentals well-known in the economic literature. For this monthly data on Pak Rupee exchange rates in the terms of major currencies (US Dollar, British Pound, Canadian Dollar and Japanese Yen) and macroeconomics fundamentals is taken from April, 1982 to November, 2011. The results show that the PKR-USD exchange rate volatility is influenced by real output volatility, foreign exchange reserves volatility, inflation volatility and productivity volatility. The PKR-GBP exchange rate volatility is influenced by foreign exchange reserves volatility and terms of trade volatility. The PKR-CAD exchange rate volatility is influenced by terms of trade volatility. The findings of this paper reveal that exchange rate volatility in Pakistan results from real shocks than nominal shocks.
In conventional Econometrics, the unit root and cointegration analysis are the only ways to circumvent the spurious regression which may arise from missing variable (lag values) rather than the nonstationarity process in time series data. We propose the Ghouse equation solution of autoregressive distributed lag mechanism which does not require additional work in unit root testing and bound testing. This advantage makes the proposed methodology more efficient compared to the existing cointegration procedures. The earlier tests weaken their position in comparison to it, as they had numerous linked testing procedures which further increase the size of the test and/or reduce the test power. The simplification of the Ghouse equation does not attain any such type of error, which makes it a more powerful test as compared to widely cited exiting testing methods in econometrics and statistics literature.
Financial development is essential for economic growth for all economies. In the current study, we examine the importance of financial development on economic growth for different countries using advanced econometric techniques of model selection. We use Auto metrics, Elastic Net,and Extreme Bound Analysis to retain the variables that affect the economic growth by using the data (1980-2019) on 32 countries from different regions. We used an advanced approach (retention frequency) to investigate the relationship between financial development and economic growth. The results show that financial development is a significant retention frequency in the case of Asian, European, and American countries directly and indirectly. For in-sample forecasting, we use root mean square error (RMSE) and mean square prediction error (MSPE). Extreme Bound Analysis presents a superior predictive performance interms of the lowest RMSE and MPSE that are 1.03 and 0.05,respectively.
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