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

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  • ., Kaustubh
  • Gopalakrishnan, Pawan Gopalakrishnan
  • Ranjan, Abhishek Ranjan

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.

Suggested Citation

  • ., Kaustubh & Gopalakrishnan, Pawan Gopalakrishnan & Ranjan, Abhishek Ranjan, 2025. "Estimating the New Keynesian Phillips Curve (NKPC) with Fat-tailed Events," MPRA Paper 126329, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:126329
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    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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