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A simple test for nonstationarity in mixed panels with incidental trends

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  • Westerlund, Joakim

Abstract

Ng (2008) shows how the cross-sectional variance of the observed panel data can be used to construct a simple test for the proportion of non-stationary units. However, in the case with incidental trends the test is distorted. The present note shows how the distortions can be substantially reduced by the use of bias-adjustment. It also investigates the local power of the bias-adjusted test, which is shown to suffer from the same incidental trends problem previously only documented for conventional t-tests.

Suggested Citation

  • Westerlund, Joakim, 2014. "A simple test for nonstationarity in mixed panels with incidental trends," Economics Letters, Elsevier, vol. 125(2), pages 160-163.
    • Handle: RePEc:eee:ecolet:v:125:y:2014:i:2:p:160-163
    DOI: 10.1016/j.econlet.2014.09.003
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    References listed on IDEAS

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    1. Moon, Hyungsik Roger & Perron, Benoit & Phillips, Peter C.B., 2007. "Incidental trends and the power of panel unit root tests," Journal of Econometrics, Elsevier, vol. 141(2), pages 416-459, December.
    2. Joakim Westerlund, 2012. "Testing For Unit Roots In Panel Time-Series Models With Multiple Level Breaks," Manchester School, University of Manchester, vol. 80(6), pages 671-699, December.
    3. Ng, Serena, 2008. "A Simple Test for Nonstationarity in Mixed Panels," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 113-127, January.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Unit root test; Panel data; Incidental trends; Bias correction; Local asymptotic power;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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