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Cointegrating Regressions with Time Heterogeneity


  • Chang Sik Kim
  • Joon Park


This article considers the cointegrating regression with errors whose variances change smoothly over time. The model can be used to describe a long-run cointegrating relationship, the tightness of which varies along with time. Heteroskedasticity in the errors is modeled nonparametrically and is assumed to be generated by a smooth function of time. We show that it can be consistently estimated by the kernel method. Given consistent estimates for error variances, the cointegrating relationship can be efficiently estimated by the usual generalized least squares (GLS) correction for heteroskedastic errors. It is shown that the U.S. money demand function, both for M1 and M2, is well fitted to such a cointegrating model with an increasing trend in error variances. Moreover, we found that the bilateral purchasing power parities among the leading industrialized countries such as the United States, Japan, Canada, and the United Kingdom have been changed somewhat conspicuously over the past thirty years. In particular, it appears that they all have generally become more tightened during the period.

Suggested Citation

  • Chang Sik Kim & Joon Park, 2010. "Cointegrating Regressions with Time Heterogeneity," Econometric Reviews, Taylor & Francis Journals, vol. 29(4), pages 397-438.
  • Handle: RePEc:taf:emetrv:v:29:y:2010:i:4:p:397-438
    DOI: 10.1080/07474930903562221

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    Cited by:

    1. Angeliki Papana & Catherine Kyrtsou & Dimitris Kugiumtzis & Cees Diks, 2016. "Detecting Causality in Non-stationary Time Series Using Partial Symbolic Transfer Entropy: Evidence in Financial Data," Computational Economics, Springer;Society for Computational Economics, vol. 47(3), pages 341-365, March.
    2. Kim, Chang Sik & Kim, In-Moo, 2012. "Partial parametric estimation for nonstationary nonlinear regressions," Journal of Econometrics, Elsevier, vol. 167(2), pages 448-457.


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