Abstract:
We employ time series data to empirically determine the causal relationship between economic policy uncertainty and the GDP growth rates of seven emerging market economies while controlling for the effect of oil price, interest rates, and the CPI. Due to differences in sampling frequencies between the GDP series and other variables, a multi-horizon mixed frequency VAR model is specified. This model fully exploits the recently developed mixed frequency Granger causality test in order to circumvent the distorting effects of temporal aggregation. The empirical results show a strong statistical evidence for causality flowing from EPU to GDP in Brazil, Chile, and India in the mixed frequency case while weak statistical evidence is found for Colombia, Mexico, and Russia. For comparative analysis, the low-frequency Granger causality test is also employed and strong statistical evidence of causality flowing from EPU to GDP in Brazil, Chile, India, Mexico is uncovered. Analyzing the causal patterns uncovered in both specifications show that the low-frequency Granger causality results are less intuitively appealing than those that are obtained from the mixed frequency Granger causality test specifications. The results have empirical as well as policy implications which are discussed
