This paper examines a nonparametric CUSUM-type test for common trends in large panel data sets with individual fixed effects. We consider, as in Zhang, Su, and Phillips (2012), a partial linear regression model with unknown functional form for the trend component, although our test does not involve local smoothings. This conveniently forgoes the need to choose a bandwidth parameter, which due to a lack of a clear and sensible information criteria is difficult for testing purposes. We are able to do so after making use that the number of individuals increases with no limit. After removing the parametric component of the model, when the errors are homoscedastic, our test statistic converges to a Gaussian process whose critical values are easily tabulated. We also examine the consequences of having heteroscedasticity as well as discussing the problem of how to compute valid critical values due to the very complicated covariance structure of the limiting process. Finally, we present a small Monte Carlo experiment to shed some light on the finite sample performance of the test.
We thank for the comments of a referee which have led to a clearer version of the manuscript. Of course, any remaining errors are our sole responsibility.
Hidalgo, J. and Lee, J. (2014), "A CUSUM Test for Common Trends in Large Heterogeneous Panels", Essays in Honor of Peter C. B. Phillips (Advances in Econometrics, Vol. 33), Emerald Group Publishing Limited, pp. 303-345. https://doi.org/10.1108/S0731-905320140000033010Download as .RIS
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