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Estimating New-Keynesian Phillips curves: A full information maximum likelihood approach

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  • Linde, Jesper

Abstract

The New-Keynesian Phillips curve has recently become an important ingredient in monetary policy models. However, using limited information methods, the empirical support for the New-Keynesian Phillips curve appear to be mixed. This paper argues, by means of Monte Carlo simulations with a simple New-Keynesian sticky price model, that single equations methods, e.g. GMM, are likely to produce imprecise and biased estimates. Then, it is argued that estimating the model with full information maximum likelihood (FIML) is a useful way of obtaining better estimates. Finally, a version of the model used in the Monte Carlo simulations is estimated on U.S. data with FIML and although the pure forward-looking New-Keynesian Phillips curve is rejected, a version with both forward- and backward-looking components provides a reasonable approximation of U.S. inflation dynamics.
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  • Linde, Jesper, 2005. "Estimating New-Keynesian Phillips curves: A full information maximum likelihood approach," Journal of Monetary Economics, Elsevier, vol. 52(6), pages 1135-1149, September.
  • Handle: RePEc:eee:moneco:v:52:y:2005:i:6:p:1135-1149
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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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