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Partial parametric estimation for nonstationary nonlinear regressions

  • Kim, Chang Sik
  • Kim, In-Moo

This paper proposes an estimation method for a partial parametric model with multiple integrated time series. Our estimation procedure is based on the decomposition of the nonparametric part of the regression function into homogeneous and integrable components. It consists of two steps: In the first step we parameterize and fit the homogeneous component of the nonparametric part by the nonlinear least squares with other parametric terms in the model, and use in the second step the standard kernel method to nonparametrically estimate the integrable component of the nonparametric part from the residuals in the first step. We establish consistency and obtain the asymptotic distribution of our estimator. A simulation shows that our estimator performs well in finite samples. For the empirical illustration, we estimate the money demand functions for the US and Japan using our model and methodology.

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Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 167 (2012)
Issue (Month): 2 ()
Pages: 448-457

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Handle: RePEc:eee:econom:v:167:y:2012:i:2:p:448-457
Contact details of provider: Web page: http://www.elsevier.com/locate/jeconom

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  1. Ippei Fujiwara & Tomoyuki Nakajima & Nao Sudo & Yuki Teranishi, 2011. "Global Liquidity Trap," KIER Working Papers 780, Kyoto University, Institute of Economic Research.
  2. Joon Y. Park & Peter C. B. Phillips, 1999. "Nonlinear Regressions with Integrated Time Series," Working Paper Series no6, Institute of Economic Research, Seoul National University.
  3. 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.
  4. Wang, Qiying & Phillips, Peter C.B., 2009. "Asymptotic Theory For Local Time Density Estimation And Nonparametric Cointegrating Regression," Econometric Theory, Cambridge University Press, vol. 25(03), pages 710-738, June.
  5. Laurence Ball, 1998. "Another Look at Long-Run Money Demand," NBER Working Papers 6597, National Bureau of Economic Research, Inc.
  6. Chung, Heetaik & Park, Joon Y., 2007. "Nonstationary nonlinear heteroskedasticity in regression," Journal of Econometrics, Elsevier, vol. 137(1), pages 230-259, March.
  7. 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.
  8. Chang Sik Kim & Joon Park, 2010. "Cointegrating Regressions with Time Heterogeneity," Econometric Reviews, Taylor & Francis Journals, vol. 29(4), pages 397-438.
  9. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-54, July.
  10. Duca, John V. & VanHoose, David D., 2004. "Recent developments in understanding the demand for money," Journal of Economics and Business, Elsevier, vol. 56(4), pages 247-272.
  11. Qiying Wang & Peter C. B. Phillips, 2009. "Structural Nonparametric Cointegrating Regression," Econometrica, Econometric Society, vol. 77(6), pages 1901-1948, November.
  12. Paul R. Krugman, 1998. "It's Baaack: Japan's Slump and the Return of the Liquidity Trap," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 29(2), pages 137-206.
  13. Youngsoo Bae & Robert M. de Jong, 2007. "Money demand function estimation by nonlinear cointegration," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(4), pages 767-793.
  14. James H. Stock & Mark W. Watson, 1991. "A simple estimator of cointegrating vectors in higher order integrated systems," Working Paper Series, Macroeconomic Issues 91-3, Federal Reserve Bank of Chicago.
  15. Bae, Youngsoo & Kakkar, Vikas & Ogaki, Masao, 2006. "Money Demand in Japan and Nonlinear Cointegration," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(6), pages 1659-1667, September.
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