Testing for Common Trends in Semiparametric Panel Data Models with Fixed Eﬀects
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 (R²). 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 R² for each cross section unit. The proposed test statistic is obtained by averaging all cross sectional nonparametric R²’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.
Keywords: Common trends, Local polynomial estimation, Nonparametric goodness-of-ﬁt, Panel data, Proﬁle least squares