Testing for Common Trends in Semiparametric Panel Data Models with Fixed Effects, forthcoming

By Yikui Zhang, Liangjun Su, Peter C. B. Phillips. 2012

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This paper proposes a nonparametric test for common trends in semiparametric panel data models with fixed effects based on a measure of nonparametric goodness-of-fit (R^2). We first estimate the model under the null hypothesis of common trends by the method of profile least squares, and obtain the augmented residual which consistently estimates the sum of the fixed effect and the disturbance under the null. Then we run a local linear regression of the augmented residuals on a time trend and calculate the nonparametric R^2 for each cross section unit. The proposed test statistic is obtained by averaging all cross sectional nonparametric R^2's, which is close to zero under the null and deviates from zero under the alternative. We show that after appropriate standardization the test statistic is asymptotically normally distributed under both the null hypothesis and a sequence of Pitman local alternatives. We prove test consistency and propose a bootstrap procedure to obtain p-values. Monte Carlo simulations indicate that the test performs well in finite samples. Empirical applications are conducted exploring the commonality of spatial trends in UK climate change data and idiosyncratic trends in OECD real GDP growth data. Both applications reveal the fragility of the widely adopted common trends assumption.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/1d8d4bf1ea71fdc935fd62caf0f83bb72be33138

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Citations in the corpus

Sieve Estimation of Time-Varying Panel Data Models With Latent Structures (2019, methodology): “Following the literature on smooth time-varying regressionmodels (e.g., Cai 2007; Robinson 2012; Chen, Gao, and Li 2012, Zhang, Su, and Phillips 2011), we assume that βi(·)’s and αk(·)’s are smooth functions of t/T .”
Identifying Latent Structures in Panel Data (2016, methodology): “Perhaps the most common method is to use a random coefficient structure in which the parameters are assumed to be independent draws from a common distribution — see Hsiao and Pesaran (2008) for an overview of the approach.”