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Testing Parameter Stability in Quantile Models: An Application to the U.S. Inflation Process

  • Dong Jin Lee

    (University of Connecticut)

This paper considers parameter instability tests in conditional quantile models. I suggest tests for quantile parameter instability based on the asymptotically optimal tests of Lee (2008) both in parametric and semiparametric set-up. In parametric models, Komunjer (2005)'s tick-exponential family of distributions is used as the underlying distribution, in which the test has asymptotically correct sizes even when the error distribution is misspecified. I apply our test statistic to various quantile models of the U.S. inflation process such as Phillips curve, P-star model, and autoregressive models. The test result shows an evidence of parameter instability in most quantile levels of all models. The semiparametric test rejects the stability even in more recent period with moderate economic volatility. Phillips curve model and autoregressive model have asymmetric test results across quantile levels, implying the asymmetric response of inflation to economic shocks.

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Paper provided by University of Connecticut, Department of Economics in its series Working papers with number 2009-26.

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Length: 33 pages
Date of creation: Feb 2009
Date of revision:
Handle: RePEc:uct:uconnp:2009-26
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Web page: http://www.econ.uconn.edu/

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  1. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
  2. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
  3. Cogley, Timothy W. & Morozov, Sergei & Sargent, Thomas J., 2003. "Bayesian fan charts for UK inflation: Forecasting and sources of uncertainty in an evolving monetary system," CFS Working Paper Series 2003/44, Center for Financial Studies (CFS).
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  5. Richard Clarida & Jordi Galí & Mark Gertler, 1997. "Monetary policy rules and macroeconomic stability: Evidence and some theory," Economics Working Papers 350, Department of Economics and Business, Universitat Pompeu Fabra, revised May 1999.
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  7. Todd E. Clark & Michael W. McCracken, 2003. "The predictive content of the output gap for inflation : resolving in-sample and out-of-sample evidence," Research Working Paper RWP 03-06, Federal Reserve Bank of Kansas City.
  8. Godfrey, Leslie G, 1978. "Testing for Higher Order Serial Correlation in Regression Equations When the Regressors Include Lagged Dependent Variables," Econometrica, Econometric Society, vol. 46(6), pages 1303-10, November.
  9. James H. Stock & Mark W. Watson, 1999. "Forecasting Inflation," NBER Working Papers 7023, National Bureau of Economic Research, Inc.
  10. Ippei Fujiwara & Maiko Koga, 2002. "A Statistical Forecasting Method for Inflation Forecasting," Bank of Japan Working Paper Series Research and Statistics D, Bank of Japan.
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  12. repec:sae:niesru:v:167:y::i:1:p:106-112 is not listed on IDEAS
  13. James W. Taylor & Derek W. Bunn, 1999. "A Quantile Regression Approach to Generating Prediction Intervals," Management Science, INFORMS, vol. 45(2), pages 225-237, February.
  14. Komunjer, Ivana, 2002. "Quasi-Maximum Likelihood Estimation for Conditional Quantiles," Working Papers 1139, California Institute of Technology, Division of the Humanities and Social Sciences.
  15. J. Jouini & M. Boutahar, 2003. "Structural breaks in the U.S. inflation process: a further investigation," Applied Economics Letters, Taylor & Francis Journals, vol. 10(15), pages 985-988.
  16. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society.
  17. Koenker, Roger & Park, Beum J., 1996. "An interior point algorithm for nonlinear quantile regression," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 265-283.
  18. Marco Vega, 2004. "Policy Makers Priors and Inflation Density Forecasts," Econometrics 0403005, EconWPA.
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