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Estimating a common deterministic time trend break in large panels with cross sectional dependence

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  • Kim, Dukpa

Abstract

This paper develops an estimation procedure for a common deterministic time trend break in large panels. The dependent variable in each equation consists of a deterministic trend and an error term. The deterministic trend is subject to a change in the intercept, slope or both, and the break date is common for all equations. The estimation method is simply minimizing the sum of squared residuals for all possible break dates. Both serial and cross sectional correlations are important factors that decide the rate of convergence and the limiting distribution of the break date estimate. The rate of convergence is faster when the errors are stationary than when they have a unit root. When there is no cross sectional dependence among the errors, the rate of convergence depends on the number of equations and thus is faster than the univariate case. When the errors have a common factor structure with factor loadings correlated with the intercept and slope change parameters, the rate of convergence does not depend on the number of equations and thus reduces to the univariate case. The limiting distribution of the break date estimate is also provided. Some Monte Carlo experiments are performed to assess the adequacy of the asymptotic results. A brief empirical example using the US GDP price index is offered.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 164 (2011)
Issue (Month): 2 (October)
Pages: 310-330

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Handle: RePEc:eee:econom:v:164:y:2011:i:2:p:310-330

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Web page: http://www.elsevier.com/locate/jeconom

Related research

Keywords: Structural break Deterministic trend Panel data;

References

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  1. Peter C.B. Phillips & Hyungsik R. Moon, 1999. "Linear Regression Limit Theory for Nonstationary Panel Data," Cowles Foundation Discussion Papers 1222, Cowles Foundation for Research in Economics, Yale University.
  2. De Wachter, Stefan & Tzavalis, Elias, 2005. "Monte Carlo comparison of model and moment selection and classical inference approaches to break detection in panel data models," Economics Letters, Elsevier, vol. 88(1), pages 91-96, July.
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