Testing Serial Correlation in Semiparametric Time Series Models
AbstractIn this paper, we propose two test statistics for testing serial correlation in semiparametric time series model that could allow lagged dependent variables as explanatory variables. The first one is testing for zero first-order serial correlation and the second is for testing higher-order serial correlation. The test statistics are shown to have asymptotic normal or chi^2 distributions under the assumption of a martingale difference error process. Our results generalize some of the test statistics of Li and Hsiao (1998), that were developed for the case of panel data with a large N and a fixed T, to the case of a large T with N either small or large. Copyright 2003 Blackwell Publishing Ltd.
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Bibliographic InfoArticle provided by Wiley Blackwell in its journal Journal of Time Series Analysis.
Volume (Year): 24 (2003)
Issue (Month): 3 (05)
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Web page: http://www.blackwellpublishing.com/journal.asp?ref=0143-9782
Other versions of this item:
- Li, D. & Stengos, T., 1999. "Testing Serial Correlation in Semiparametric Time Series Model," Working Papers, University of Guelph, Department of Economics and Finance 1999-4, University of Guelph, Department of Economics and Finance.
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
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