Publication Type

Working Paper

Publication Date

9-2009

Abstract

We propose tests for structural change in conditional distributions via quantile regressions. To avoid misspecification on the conditioning relationship, we construct the tests based on the residuals from local polynomial quantile regressions. In particular, the tests are based upon the cumulative sums of generalized residuals from quantile regressions and have power against local alternatives at rate n−1/2. We derive the limiting distributions for our tests under the null hypothesis of no structural change and a sequence of local alternatives. The proposed tests apply to a wide range of dynamic models, including time series regressions with m.d.s. errors, as well as models with serially correlated errors. To deal with possible correlations in the error process, we also propose a simulation method to obtain the p-values for our tests. Finally, Monte Carlo simulations suggest that our tests behave well in finite samples.

Keywords

Conditional distribution, Structural change, Local polynomial regression, Quantile regression, Block bootstrap

Discipline

Econometrics

Research Areas

Econometrics

First Page

1

Last Page

41

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://www.mysmu.edu/faculty/ljsu/Publications/distribution_break092209.pdf

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Econometrics Commons

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