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Inference in models with adaptive learning

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Author Info

  • Chevillon, Guillaume
  • Massmann, Michael
  • Mavroeidis, Sophocles

Abstract

Identification of structural parameters in models with adaptive learning can be weak, causing standard inference procedures to become unreliable. Learning also induces persistent dynamics, and this makes the distribution of estimators and test statistics non-standard. Valid inference can be conducted using the Anderson-Rubin statistic with appropriate choice of instruments. Application of this method to a typical new Keynesian sticky-price model with perpetual learning demonstrates its usefulness in practice.

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File URL: http://www.sciencedirect.com/science/article/B6VBW-4YDYSNH-2/2/0bb5ae34993475758339a2726c673b28
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Bibliographic Info

Article provided by Elsevier in its journal Journal of Monetary Economics.

Volume (Year): 57 (2010)
Issue (Month): 3 (April)
Pages: 341-351

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Handle: RePEc:eee:moneco:v:57:y:2010:i:3:p:341-351

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Web page: http://www.elsevier.com/locate/inca/505566

Related research

Keywords: Weak identification Persistence Anderson-Rubin statistic DSGE models;

References

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Citations

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Cited by:
  1. William Branch & Bruce McGough, 2011. "Business cycle amplification with heterogeneous expectations," Economic Theory, Springer, vol. 47(2), pages 395-421, June.
  2. Norbert Christopeit & Michael Massmann, 2010. "Consistent Estimation of Structural Parameters in Regression Models with Adaptive Learning," Tinbergen Institute Discussion Papers 10-077/4, Tinbergen Institute.
  3. Guillaume Chevillon & Sophocles Mavroeidis, 2013. "Learning generates Long Memory," Post-Print hal-00661012, HAL.
  4. Dufour, Jean-Marie & Khalaf, Lynda & Kichian, Maral, 2013. "Identification-robust analysis of DSGE and structural macroeconomic models," Journal of Monetary Economics, Elsevier, vol. 60(3), pages 340-350.
  5. Norbert Christopeit & Michael Massmann, 2013. "A Note on an Estimation Problem in Models with Adaptive Learning," Tinbergen Institute Discussion Papers 13-151/III, Tinbergen Institute.
  6. Stefano Eusepi & Bruce Preston, 2011. "Expectations, Learning, and Business Cycle Fluctuations," American Economic Review, American Economic Association, vol. 101(6), pages 2844-72, October.
  7. James Mitchell & George Kapetanios & Yongcheol Shin, 2012. "A Nonlinear Panel Data Model of Cross-Sectional Dependence," Discussion Papers in Economics 12/01, Department of Economics, University of Leicester.
  8. Norbert Christopeit & Michael Massmann, 2012. "Strong Consistency of the Least-Squares Estimator in Simple Regression Models with Stochastic Regressors," Tinbergen Institute Discussion Papers 12-109/III, Tinbergen Institute.
  9. Eric Gaus & Srikanth Ramamurthy, 2012. "Estimation of Constant Gain Learning Models," Working Papers 12-01, Ursinus College, Department of Economics, revised 01 Apr 2014.
  10. Eva A. Arnold, 2013. "The Role of Data Revisions and Disagreement in Professional Forecasts," Macroeconomics and Finance Series 201303, Hamburg University, Department Wirtschaft und Politik.
  11. Brissimis, Sophocles & Migiakis, Petros, 2010. "Inflation persistence and the rationality of inflation expectations," MPRA Paper 29052, University Library of Munich, Germany.
  12. Norbert Christopeit & Michael Massmann, 2012. "Strong Consistency of the Least-Squares Estimator in Simple Regression Models with Stochastic Regressors," Tinbergen Institute Discussion Papers 12-109/III, Tinbergen Institute.

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