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Longrun Relationships Evolving Over Time

Author

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  • Joon Y. Park

    ()

  • Jonghan park

Abstract

This paper considers the cointegrating regression with errors whose variances change smoothly over time. The model can be used to describe a longrun cointegrating relationship, the tightness of which varies along with time. Heteroskedasticity in the errors is modelled nonparametrically and is assumed to be generated by a smooth function. We show that it can be consistently estimated by the kernel method. Given consistent estimates for error variances, the cointegrating relationship can be efficiently estimted by the usual GLS correction for heteroskedastic errors. It is shown that the US money demand function, both for M1 and M2, is well fitted to such a cointegrating model with growing variance. Moreover, we found that the bilateral purchasing power parities among many industrialized countries including the US, Germany, Japan, Canada, and the UK have been changed somewhat conspicuously over the past twenty years. They all had been monotonically loosened in the 70's and 80's, but most of them became tightened in the 90's.

Suggested Citation

  • Joon Y. Park & Jonghan park, 1999. "Longrun Relationships Evolving Over Time," Working Paper Series no8, Institute of Economic Research, Seoul National University.
  • Handle: RePEc:snu:ioerwp:no8
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    File URL: http://econ.snu.ac.kr/~ecores/activity/paper/no8.pdf
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    References listed on IDEAS

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    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Hansen, Bruce E., 1992. "Convergence to Stochastic Integrals for Dependent Heterogeneous Processes," Econometric Theory, Cambridge University Press, vol. 8(04), pages 489-500, December.
    3. Donald W. K. Andrews & C. John McDermott, 1995. "Nonlinear Econometric Models with Deterministically Trending Variables," Review of Economic Studies, Oxford University Press, vol. 62(3), pages 343-360.
    4. Phillips, P.C.B., 1986. "Understanding spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 33(3), pages 311-340, December.
    5. Phillips, P C B, 1987. "Time Series Regression with a Unit Root," Econometrica, Econometric Society, pages 277-301.
    6. Peter C.B. Phillips & Joon Y. Park, 1998. "Nonstationary Density Estimation and Kernel Autoregression," Cowles Foundation Discussion Papers 1181, Cowles Foundation for Research in Economics, Yale University.
    7. Park, Joon Y. & Phillips, Peter C.B., 1988. "Statistical Inference in Regressions with Integrated Processes: Part 1," Econometric Theory, Cambridge University Press, pages 468-497.
    8. Phillips, Peter C B & Ploberger, Werner, 1996. "An Asymptotic Theory of Bayesian Inference for Time Series," Econometrica, Econometric Society, vol. 64(2), pages 381-412, March.
    9. Phillips, P C B, 1991. "Optimal Inference in Cointegrated Systems," Econometrica, Econometric Society, pages 283-306.
    10. Park, Joon Y. & Phillips, Peter C.B., 1999. "Asymptotics For Nonlinear Transformations Of Integrated Time Series," Econometric Theory, Cambridge University Press, vol. 15(03), pages 269-298, June.
    11. repec:cup:etheor:v:8:y:1992:i:4:p:489-500 is not listed on IDEAS
    12. Phillips, P C B, 1987. "Time Series Regression with a Unit Root," Econometrica, Econometric Society, pages 277-301.
    13. repec:cup:etheor:v:11:y:1995:i:5:p:888-911 is not listed on IDEAS
    14. Peter C.B. Phillips & Victor Solo, 1989. "Asymptotics for Linear Processes," Cowles Foundation Discussion Papers 932, Cowles Foundation for Research in Economics, Yale University.
    15. Saikkonen, Pentti, 1995. "Problems with the Asymptotic Theory of Maximum Likelihood Estimation in Integrated and Cointegrated Systems," Econometric Theory, Cambridge University Press, vol. 11(05), pages 888-911, October.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Cointegrating Regression; Time Heterogeneity; Kernel Estimation; GLS Correction for Heteroskedasticity;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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