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

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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 that, 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. KEY WORD: error component, panel model, hypothesis testing, serial correlation of unknown form, spectral peak, unbalanced panel data, wavelet.

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Bibliographic Info

Paper provided by Center for Policy Research, Maxwell School, Syracuse University in its series Center for Policy Research Working Papers with number 32.

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Length: 62 pages
Date of creation: Oct 2000
Date of revision:
Handle: RePEc:max:cprwps:32

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Cited by:
  1. Badi H. Baltagi & Byoung Cheol Jung & Seuck Heun Song, 2008. "Testing for Heteroskedasticity and Serial Correlation in a Random Effects Panel Data Model," Center for Policy Research Working Papers 111, Center for Policy Research, Maxwell School, Syracuse University.
  2. Fernandez, Viviana, 2006. "Does domestic cooperation lead to business-cycle convergence and financial linkages?," The Quarterly Review of Economics and Finance, Elsevier, vol. 46(3), pages 369-396, July.
  3. Li, Linyuan & Yao, Shan & Duchesne, Pierre, 2014. "On wavelet-based testing for serial correlation of unknown form using Fan’s adaptive Neyman method," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 308-327.
  4. Haven, Emmanuel & Liu, Xiaoquan & Shen, Liya, 2012. "De-noising option prices with the wavelet method," European Journal of Operational Research, Elsevier, vol. 222(1), pages 104-112.
  5. Li, Yushu & Andersson, Fredrik N. G., 2014. "A simple wavelet-based test for serial correlation in panel data models," Discussion Papers 2014/11, Department of Business and Management Science, Norwegian School of Economics.
  6. Gao, Jiti & Hong, Yongmiao, 2007. "Central limit theorems for weighted quadratic forms of dependent processes with applications in specification testing," MPRA Paper 11977, University Library of Munich, Germany, revised Dec 2007.
  7. Viviana Fernandez & Ali M. Kutan, 2005. "Do Regional Integration Agreements Increase Business-Cycle Convergence? Evidence From APEC and NAFTA," William Davidson Institute Working Papers Series wp765, William Davidson Institute at the University of Michigan.
  8. Fernandez, Viviana, 2006. "The CAPM and value at risk at different time-scales," International Review of Financial Analysis, Elsevier, vol. 15(3), pages 203-219.
  9. Okui, Ryo, 2009. "Testing serial correlation in fixed effects regression models based on asymptotically unbiased autocorrelation estimators," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(9), pages 2897-2909.
  10. Du, Zaichao, 2014. "Testing for serial independence of panel errors," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 248-261.
  11. Zhou, Yong & Wan, Alan T.K. & Xie, Shangyu & Wang, Xiaojing, 2010. "Wavelet analysis of change-points in a non-parametric regression with heteroscedastic variance," Journal of Econometrics, Elsevier, vol. 159(1), pages 183-201, November.
  12. Michis, Antonis A., 2014. "Time scale evaluation of economic forecasts," Economics Letters, Elsevier, vol. 123(3), pages 279-281.

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