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Testing Serial Correlation in Semiparametric Time Series Models

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  • DINGDING LI
  • THANASIS STENGOS

Abstract

. In 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 χ2 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.

Suggested Citation

  • Dingding Li & Thanasis Stengos, 2003. "Testing Serial Correlation in Semiparametric Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(3), pages 311-335, May.
  • Handle: RePEc:bla:jtsera:v:24:y:2003:i:3:p:311-335
    DOI: 10.1111/1467-9892.00309
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    References listed on IDEAS

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    1. Li, Qi, 1999. "Consistent model specification tests for time series econometric models," Journal of Econometrics, Elsevier, vol. 92(1), pages 101-147, September.
    2. Li, Q. & Hsiao, C., 1998. "Testing serial correlation in semiparametric panel data models," Journal of Econometrics, Elsevier, vol. 87(2), pages 207-237, September.
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    6. Godfrey, Leslie G, 1978. "Testing for Higher Order Serial Correlation in Regression Equations When the Regressors Include Lagged Dependent Variables," Econometrica, Econometric Society, vol. 46(6), pages 1303-1310, November.
    7. Durbin, J, 1970. "Testing for Serial Correlation in Least-Squares Regression When Some of the Regressors are Lagged Dependent Variables," Econometrica, Econometric Society, vol. 38(3), pages 410-421, May.
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    Cited by:

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    2. Neophyta Empora & Theofanis Mamuneas, 2011. "The Effect of Emissions on U.S. State Total Factor Productivity Growth," Review of Economic Analysis, Digital Initiatives at the University of Waterloo Library, vol. 3(2), pages 149-172, October.
    3. Polemis, Michael L. & Stengos, Thanasis, 2015. "Does market structure affect labour productivity and wages? Evidence from a smooth coefficient semiparametric panel model," Economics Letters, Elsevier, vol. 137(C), pages 182-186.
    4. Neophyta Empora, 2017. "Air pollution spillovers and U.S. state productivity growth," University of Cyprus Working Papers in Economics 06-2017, University of Cyprus Department of Economics.
    5. He, Jie & Richard, Patrick, 2010. "Environmental Kuznets curve for CO2 in Canada," Ecological Economics, Elsevier, vol. 69(5), pages 1083-1093, March.
    6. Poudel, Biswo N. & Paudel, Krishna P. & Bhattarai, Keshav, 2009. "Searching for an Environmental Kuznets Curve in Carbon Dioxide Pollutant in Latin American Countries," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 41(1), pages 1-15, April.
    7. Tianyong Zhang & Demei Yuan & Jiali Ma & Xuemei Hu, 2017. "Assessing white noise assumption with semi-parametric additive partial linear models," Statistical Papers, Springer, vol. 58(2), pages 417-431, June.
    8. Hu, Xuemei & Wang, Zhizhong & Liu, Feng, 2008. "Zero finite-order serial correlation test in a semi-parametric varying-coefficient partially linear errors-in-variables model," Statistics & Probability Letters, Elsevier, vol. 78(12), pages 1560-1569, September.
    9. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.

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    More about this item

    JEL classification:

    • 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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