Estimating the New Keynesian Phillips Curve (NKPC) with Fat-tailed Events

., Kaustubh and Gopalakrishnan, Pawan Gopalakrishnan and Ranjan, Abhishek Ranjan (2025): Estimating the New Keynesian Phillips Curve (NKPC) with Fat-tailed Events.

Abstract

This paper provides estimation of the New Keynesian Phillips curve accounting for the unexpected large shocks such as Covid-19. The recent pandemic distorted the estimates of the output gap derived using the regular trend cycle decomposition of GDP (HP Filter, BP Filter, Kalman Filter). We propose a modified unobserved components model (UCM) by introducing an additional Student-t distributed irregular component in the trend cycle decomposition of GDP, which successfully isolates transitory shocks like COVID-19 from trend and cycle estimates. We also construct a model-based measure of inflation expectations that captures adaptive learning from a long inflation history and real-time updating during the pandemic. For India, we find a stable linear NKPC. Our results demonstrate that accounting for fat-tailed events is crucial for obtaining reliable Phillips curve estimates in emerging markets.

• Item Type: MPRA Paper
• Language: English
• Keywords: Philips Curve, Potential Growth, Output Gap, Inflation Expectations, Unobserved Components Model, Kalman Filter, Almon Lag
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C60 - General; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E32 - Business Fluctuations ; Cycles

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