Analyzing and Forecasting Output Gap and Inflation Using Bayesian Vector Auto Regression (BVAR) Method: A Case of Pakistan

We attempt to forecast inflation and output gap of Pakistan using Bayesian VARs. We implement three different priors for this purpose. Analysis in this paper is conducted using Monetary Aggregates and Credit macro variables in order to forecast Output Gap and CPI Index based measure of Inflation. Output Gap used in our analysis is estimated in a State–Space framework using Kalman filter. Literature suggests that Bayesian shrinkage is an appropriate tool for forecasting using large number of Macro Economic variables. In addition, appropriate Prior selection is fundamental to robust forecasting in Bayesian VARs; in this backdrop, the 3 types of Priors implemented in our analysis are; 1: Minnesota Priors, 2: Independent Normal–Wishart Priors and 3: Independent Minnesota–Wishart Priors. Estimation and forecasting is conducted in conformity with Koop and Korobilis (2009). Diagnostics of Bayesian VAR models and robustness of forecast estimates show that Bayesian VARs provide robust forecasts and have suitable structural interpretation. This conclusion is especially relevant considering that Bayesian methods provide inherent solution to circumvent the problem of multicollinearity and over parameterization