The Power Performance of Fixed-T Panel Unit Root Tests allowing for Structural Breaks
AbstractThe asymptotic local power of least squares based fixed-T panel unit root tests allowing for a structural break in their individual effects and/or incidental trends of the AR(1) panel data model is studied. These tests correct the least squares estimator of the autoregressive coefficient of this panel data model for its inconsistency due to the individual effects and/or incidental trends of the panel. The limiting distributions of the tests are analytically derived under a sequence of local alternatives, assuming that the cross-sectional dimension of the tests (N) grows large. It is shown that the considered fixed-T tests have local power which tends to unity fast only if the panel data model includes individual effects. For panel data models with incidental trends, the power of the tests becomes trivial. However, this problem does not always appear if the tests allow for serial correlation of the error term.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 46012.
Date of creation: 09 Apr 2013
Date of revision:
Panel data; unit root tests; structural breaks; local power; serial correlation; incidental trends;
Other versions of this item:
- Yiannis Karavias & Elias Tzavalis, . "The power performance of fixed-T panel unit root tests allowing for structural breaks," Discussion Papers 13/01, University of Nottingham, Granger Centre for Time Series Econometrics.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-04-13 (All new papers)
- NEP-ECM-2013-04-13 (Econometrics)
- NEP-ETS-2013-04-13 (Econometric Time Series)
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