Testing Parameter Stability in Quantile Models: An Application to the U.S. Inflation Process
AbstractThis 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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Bibliographic InfoPaper provided by University of Connecticut, Department of Economics in its series Working papers with number 2009-26.
Length: 33 pages
Date of creation: Feb 2009
Date of revision:
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Quantile Model; optimal test; parameter instability; Phillips curve; inflation;
Find related papers by JEL classification:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
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