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Testing for Stationarity in Heterogeneous Panels with Serially Correlated Errors

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Abstract

This paper proposes panel-based tests for the null stationary against the alternative of unit roots in the presence of both heterogeneity across cross-section units and serial correlation across time periods. It is shown by the joint asymptotic approach (rather than the sequential asymptotic approach) that the proposed test statistic is distributed as standard normal under the null for large N (number of groups) and finite T (number of time periods) with the additional condition N/T -> 0. It is also shown that the test is consistent under the alternative hypothesis that the fraction of the individual processes that have unit root is non-zero. Monte Carlo results confirm that validity of the proposed test, and clearly indicate that the power of the panel-based test is substantially higher than that of the single time series-based test.

Suggested Citation

  • Yongcheol Shin & Andy Snell, 1999. "Testing for Stationarity in Heterogeneous Panels with Serially Correlated Errors," ESE Discussion Papers 70, Edinburgh School of Economics, University of Edinburgh.
  • Handle: RePEc:edn:esedps:70
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    File URL: http://www.econ.ed.ac.uk/papers/id70_esedps.pdf
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    Keywords

    stationary; unit roots; heterogeneous panels; serial correlation; joint asymptotic theory; Monte Carlo simulation; finite sample adjustment;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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