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Stability Tests for Heterogeneous Panel Data



This paper proposes a new test for structural instability in heterogeneous panels. The test builds on the seminal work of Andrews (2003) originally developed for time series. It is robust to non-normal, heteroskedastic and serially correlated errors, and allows for the number of post break observations to be small. Importantly, the test considers the alternative of a break affecting only some - and not all - individuals of the panel. Under mild assumptions the test statistic is shown to be asymptotically normal, thanks to the additional cross sectional dimension of panel data. This greatly facilitates the calculation of critical values. Monte Carlo experiments show that the test has good size and power under a wide range of circumstances. The test is then applied to investigate the effect of the Euro on trade.

Suggested Citation

  • Felix Chan Tommaso Mancini-Griffoli Laurent L. Pauwels, 2006. "Stability Tests for Heterogeneous Panel Data," IHEID Working Papers 24-2006, Economics Section, The Graduate Institute of International Studies, revised Dec 2006.
  • Handle: RePEc:gii:giihei:heiwp24-2006

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    References listed on IDEAS

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    Cited by:

    1. Chiu, Yi-Bin & Lee, Chien-Chiang & Sun, Chia-Hung, 2010. "The U.S. trade imbalance and real exchange rate: An application of the heterogeneous panel cointegration method," Economic Modelling, Elsevier, vol. 27(3), pages 705-716, May.
    2. Qian, Junhui & Su, Liangjun, 2016. "Shrinkage estimation of common breaks in panel data models via adaptive group fused Lasso," Journal of Econometrics, Elsevier, vol. 191(1), pages 86-109.

    More about this item


    Structural change; end-of-sample instability tests; heterogeneous panels; Monte Carlo; Euro effect on trade.;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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