CHOICE OF FUNCTIONAL FORM IN THE NONLINEAR TAYLOR RULE: The Case of Pakistan

Linear Taylor rule prescribes symmetric response to inflation rate and output gap in good and bad times. Central banks in the world, however, are more concerned about inflation when economy is in high inflationary regime. Similarly they are more reactionary to output fluctuations when economy is experiencing slowdown in the economic activity. Thus, most of the researchers in the area of monetary policy construct a nonlinear monetary policy reaction function. In the literature related to monetary policy of Pakistan, this reaction function has been modeled as threshold regression (TR), Markov regime switching regression, and Logistic smooth transition regression (LSTR). This study compares these three choices for the case of Pakistan and tries to find out which functional form of nonlinear Taylor rule fits the Pakistani data well. Using quarterly data for the period 1993:1-2011:4, we find strong evidence that the monetary policy followed by the State Bank of Pakistan (SBP) exhibits nonlinearity. The results of this study show that threshold level of inflation rate is 6.37% and that of output gap is 2.5%. Moreover, threshold regression, with inflation rate as threshold variable, is found the best among the three specifications as it satisfies maximum number of criteria for comparison. However, LSTR model performs well if forecasting performance of the models is compared.