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Model Specification between Parametric and Nonparametric Cointegration

Author

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  • Jiti Gao

  • Dag Tjøstheim

  • Jiying Yin

Abstract

This paper considers a general model specification between a parametric co-integrating model and a nonparametric co-integrating model in a multivariate regression model, which involves a univariate integrated time series regressor and a vector of stationary time series regressors. A new and simple test is proposed and the resulting asymptotic theory is established. The test statistic is constructed based on a natural distance function between a nonparametric estimate and a smoothed parametric counterpart. The asymptotic distribution of the test statistic under the parametric specification is proportional to that of a local-time random variable with a known distribution. In addition, the finite sample performance of the proposed test is evaluated through using both simulated and real data examples.

Suggested Citation

  • Jiti Gao & Dag Tjøstheim & Jiying Yin, 2012. "Model Specification between Parametric and Nonparametric Cointegration," Monash Econometrics and Business Statistics Working Papers 18/12, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2012-18
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    File URL: http://business.monash.edu/econometrics-and-business-statistics/research/publications/ebs/wp18-12.pdf
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    References listed on IDEAS

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    1. Yoosoon Chang & Joon Y. Park & Peter C. B. Phillips, 2001. "Nonlinear econometric models with cointegrated and deterministically trending regressors," Econometrics Journal, Royal Economic Society, vol. 4(1), pages 1-36.
    2. Cai, Zongwu & Li, Qi & Park, Joon Y., 2009. "Functional-coefficient models for nonstationary time series data," Journal of Econometrics, Elsevier, vol. 148(2), pages 101-113, February.
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    Cited by:

    1. Jun Wang & Dianpeng Wang & Yubin Tian, 2022. "Multidimensional specification test based on non-stationary time series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(2), pages 348-372, June.
    2. Patrick Saart & Jiti Gao & Nam Hyun Kim, 2014. "Semiparametric methods in nonlinear time series analysis: a selective review," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(1), pages 141-169, March.
    3. Sepideh Mosaferi & Mark S. Kaiser, 2021. "Nonparametric Cointegrating Regression Functions with Endogeneity and Semi-Long Memory," Papers 2111.00972, arXiv.org, revised Jan 2025.
    4. Qiying Wang & Peter C. B. Phillips & Ying Wang, 2023. "New asymptotics applied to functional coefficient regression and climate sensitivity analysis," Cowles Foundation Discussion Papers 2365, Cowles Foundation for Research in Economics, Yale University.

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