A Univariate based NAIRU Estimation in the Context of Data Constrained Developing Countries

This study addresses the challenge of estimating the NAIRU in developing countries, focusing on Pakistan from 1972 to 2022. Due to data constraints in such contexts, it introduces robust methodologies, including Hodrick-Prescott and Kalman filters within a univariate framework, to derive NAIRU estimates and their precision. The NAIRU estimates average around five percent, fluctuating over time, indicating a time-varying NAIRU in Pakistan. An analysis of unemployment decomposition reveals that cyclical and non-structural factors are more influential than structural ones in the labor market, emphasizing the need for counter-cyclical policies alongside necessary structural reforms. The unemployment gap and inflation analysis provide mixed evidence on NAIRU's theoretical foundation, recommending simultaneous supply shocks' control and demand management in inflation-targeting. The study indicates Kalman filter estimates the NAIRU more effectively than the HP filter, though both lack precision. Therefore, future research in developing countries with limited data should focus on developing structural approaches suitable for lower degrees of freedom alongside univariate methods.