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A New Time‐Varying Parameter Autoregressive Model for U.S. Inflation Expectations

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  • MARKKU LANNE
  • JANI LUOTO

Abstract

We study the evolution of U.S. inflation by means of a new noncausal autoregressive model with time‐varying parameters that outperforms the corresponding causal and constant‐parameter noncausal models in terms of fit and forecast accuracy. Our model also beats the unobserved component stochastic volatility (UCSV) model, one of the best‐performing univariate inflation forecasting models, in terms of both point and density forecasts. We also show how the new Keynesian Phillips curve can be estimated based on our noncausal model. Both expected and lagged inflation turn out important, but the former dominates in determining the current inflation.

Suggested Citation

  • Markku Lanne & Jani Luoto, 2017. "A New Time‐Varying Parameter Autoregressive Model for U.S. Inflation Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(5), pages 969-995, August.
    • Handle: RePEc:wly:jmoncb:v:49:y:2017:i:5:p:969-995
    DOI: 10.1111/jmcb.12402
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    3. Hecq Alain & Sun Li, 2021. "Selecting between causal and noncausal models with quantile autoregressions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(5), pages 393-416, December.

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