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Testing Serial Correlation in Semiparametric Time Series Models




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 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.

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

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    References listed on IDEAS

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    5. Chung-Ming Kuan, 2006. "Artificial Neural Networks," IEAS Working Paper : academic research 06-A010, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    6. Medeiros, Marcelo & Veiga, Alvaro, 2000. "A Flexible Coefficient Smooth Transition Time Series Model," SSE/EFI Working Paper Series in Economics and Finance 360, Stockholm School of Economics, revised 29 Apr 2004.
    7. Eitrheim, Oyvind & Terasvirta, Timo, 1996. "Testing the adequacy of smooth transition autoregressive models," Journal of Econometrics, Elsevier, vol. 74(1), pages 59-75, September.
    8. T. S. Breusch & A. R. Pagan, 1980. "The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics," Review of Economic Studies, Oxford University Press, vol. 47(1), pages 239-253.
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    1. repec:spr:stpapr:v:58:y:2017:i:2:d:10.1007_s00362-015-0705-z is not listed on IDEAS
    2. 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.
    3. 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.
    4. Daniel L. Millimet & John A. List & Thanasis Stengos, 2003. "The Environmental Kuznets Curve: Real Progress or Misspecified Models?," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 1038-1047, November.
    5. He, Jie & Richard, Patrick, 2010. "Environmental Kuznets curve for CO2 in Canada," Ecological Economics, Elsevier, vol. 69(5), pages 1083-1093, March.
    6. 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.
    7. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, number 8355.
    8. Neophyta Empora & Theofanis Mamuneas, 2011. "The Effect of Emissions on U.S. State Total Factor Productivity Growth," Review of Economic Analysis, Rimini Centre for Economic Analysis, vol. 3(2), pages 149-172, October.
    9. 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(01), April.

    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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