Abstract/Description
In this study, Bayesian maximum likelihood estimation of quarterly projections model for Pakistan as presented, as documented in Ahmad & Pasha (2015). Estimation results based on quarterly data from 2001 to 2023 show substantial differences in values of estimated versus calibrated parameters related to aggregate demand, aggregate supply, monetary policy rule and exogenous shock processes. The aim of this study is to compare forecasting performance for key macro variables. It shows that the estimated model provides more precise forecasts in case of headline inflation, real GDP growth, interest rate and exchange rate over 8-quarters forecast horizon. An estimated model for gap analysis and scenario analysis was used. Gap analysis, based on March 2023 data, shows that Pakistan is passing through a recession with overshot exchange rate. In scenario analysis, implications of political instability, climate risks, commodity prices and global financial conditions for next three years’ forecasts of domestic variables under baseline and alternate scenarios were incorporated. The scenario analysis shows that simultaneous realization of assumed risk factors may lead to substantial deterioration of macroeconomic outlook and under current circumstances, using an expansionary monetary policy may lead to substantial rise in inflation and macroeconomic volatility without offering sustainable gains in GDP growth.
Keywords
Bayesian Analysis, Applied General Equilibrium Models, Forecasting and Simulation
JEL Codes
C11; D58; E37
Location
S1 room, Adamjee building
Session Theme
Evolving Dynamics in Inflation, Monetary and Fiscal Policy
Session Type
Parallel Technical Session
Session Chair
Kalim Hyder, State Bank of Pakistan
Session Discussant
Ilfan Oh, Institute of Business Administration ; Karim Khan, Pakistan Institute of Development Economics
Start Date
10-12-2024 3:15 PM
End Date
10-12-2024 5:15 PM
Recommended Citation
Ahmad, S., & ., W. (2024). Bayesian Estimation of a Pragmatic Model for Monetary Policy Analysis: The Case of Pakistan. CBER Conference. Retrieved from https://ir.iba.edu.pk/esdcber/2024/program/20
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Bayesian Estimation of a Pragmatic Model for Monetary Policy Analysis: The Case of Pakistan
S1 room, Adamjee building
In this study, Bayesian maximum likelihood estimation of quarterly projections model for Pakistan as presented, as documented in Ahmad & Pasha (2015). Estimation results based on quarterly data from 2001 to 2023 show substantial differences in values of estimated versus calibrated parameters related to aggregate demand, aggregate supply, monetary policy rule and exogenous shock processes. The aim of this study is to compare forecasting performance for key macro variables. It shows that the estimated model provides more precise forecasts in case of headline inflation, real GDP growth, interest rate and exchange rate over 8-quarters forecast horizon. An estimated model for gap analysis and scenario analysis was used. Gap analysis, based on March 2023 data, shows that Pakistan is passing through a recession with overshot exchange rate. In scenario analysis, implications of political instability, climate risks, commodity prices and global financial conditions for next three years’ forecasts of domestic variables under baseline and alternate scenarios were incorporated. The scenario analysis shows that simultaneous realization of assumed risk factors may lead to substantial deterioration of macroeconomic outlook and under current circumstances, using an expansionary monetary policy may lead to substantial rise in inflation and macroeconomic volatility without offering sustainable gains in GDP growth.