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Issues in Estimating New Keynesian Phillips Curves in the Presence of Unknown Structural Change

Author

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  • Mariano Kulish

    () (UNSW)

  • Adrian Pagan

    () (University of Sydney)

Abstract

Many papers which have estimated models with forward looking expectations have reported that the magnitude of the coefficients of the expectations term is very large when compared with the effects coming from past dynamics. This has sometimes been regarded as implausible and led to the feeling that the expectations coefficient is biased upwards. A relatively general argument that has been advanced is that the bias could be due to structural changes in the means of the variables entering the structural equation. An alternative explanation is that the bias comes from weak instruments. In this paper we investigate the issue of upward bias in the estimated coefficients of the expectations variable based on a model where we can see what causes the breaks and how to control for them. We conclude that weak instruments are the most likely cause of any bias and note that structural change can affect the quality of instruments. We also look at some empirical work in Castle et al. (2011) on the NK Phillips curve in the Euro Area and U.S, assessing whether the smaller coefficient on expectations that Castle et al. (2011) highlight is due to structural change. Our conclusion is that it is not. Instead it comes from their addition of variables to the NKPC. After allowing for the fact that there are weak instruments in the estimated re-specified model it would seem that the forward coefficient estimate is actually quite high rather than low.

Suggested Citation

  • Mariano Kulish & Adrian Pagan, 2013. "Issues in Estimating New Keynesian Phillips Curves in the Presence of Unknown Structural Change," NCER Working Paper Series 94, National Centre for Econometric Research.
  • Handle: RePEc:qut:auncer:2013_6
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    File URL: http://www.ncer.edu.au/papers/documents/WP94.pdf
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    References listed on IDEAS

    as
    1. Mariano Kulish & Adrian Pagan, 2017. "Estimation and Solution of Models with Expectations and Structural Changes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(2), pages 255-274, March.
    2. Bill Russell & Anindya Banerjee & Issam Malki & Natalia Ponomareva, 2010. "A Multiple Break Panel Approach to Estimating United States Phillips Curves," Discussion Papers 10-14, Department of Economics, University of Birmingham.
    3. David Hendry & Jennifer L. Castle & Jurgen A. Doornik, 2010. "Testing the Invariance of Expectations Models of Inflation," Economics Series Working Papers 510, University of Oxford, Department of Economics.
    4. Leandro M. Magnusson & Sophocles Mavroeidis, 2010. "Identification-Robust Minimum Distance Estimation of the New Keynesian Phillips Curve," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(2-3), pages 465-481, March.
    5. Sophocles Mavroeidis, 2004. "Weak Identification of Forward-looking Models in Monetary Economics," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(s1), pages 609-635, September.
    6. Binder,M. & Pesaran,H.M., 1995. "Multivariate Rational Expectations Models and Macroeconomic Modelling: A Review and Some New Results," Cambridge Working Papers in Economics 9415, Faculty of Economics, University of Cambridge.
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    Cited by:

    1. Garratt, Anthony & Lee, Kevin & Shields, Kalvinder, 2016. "Information rigidities and the news-adjusted output gap," Journal of Economic Dynamics and Control, Elsevier, vol. 70(C), pages 1-17.

    More about this item

    Keywords

    Phillips Curve; structural change;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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