Wavelet-Based Testing for Serial Correlation of Unknown Form in Panel Models

Abstract

Wavelet analysis is a new mathematical tool developed as a unified field of science over the last decade. As spatially adaptive analytic tools, wavelets are useful for capturing serial correlation where the spectrum has peaks or kinks, as can arise from persistent/strong dependence, seasonality or use of seasonal data such as quarterly and monthly data, business cycles, and other kinds of periodicity. This paper proposes a new class of wavelet-based tests for serial correlation of unknown form in the estimated residuals of an error component model, where the error components can be one-way or two-way, the individual and time effects can be fixed or random, the regressors may contain lagged dependent variables or deterministic/stochastic trending variables. The proposed tests are applicable to unbalanced heterogeneous panel data. They have a convenient null limit N (0,1) distribution. No formulation of an alternative is required, and the tests are consistent against serial correlation of unknown form. We propose and justify a data-driven finest scale, in an automatic manner, converges to zero under the null hypothesis of no serial correlation and grows to infinity as the sample size increases under the alternative, ensuring the consistency of the proposed tests. Simulation studies show that the new tests perform rather well in small and finite samples in comparison with some existing popular tests for panel models and can be used as an effective evaluation procedure for panel models.

Keywords: Error component, Panel model, Hypothesis testing, Serial correlation of unknown form, Spectral peak, Unbalanced panel data, Wavelet

JEL Classification: C22, C23