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Local Constant Kernel Estimation of a Partially Linear Varying Coefficient Cointegration Model

Author

Listed:
  • Luya Wang

    (School of Banking and Finance, University of International Business and Economics)

  • Zhongwen Liang

    (Department of Economics, University at Albany)

  • Juan Lin

    (Department of Finance, School of Economics, and Wang Yanan Institute for Studies in Economics, Xiamen University)

  • Qi Li

    (Department of Economics, Texas A&M University
    School of Economics and Management, Tsinghua University)

Abstract

In this paper, we consider a partially linear varying coefficient cointegration model. We focus on the estimation of constant coefficients. We derive the asymptotic result for the local constant kernel estimator, which complements the results in Li, Li, Liang and Hsiao (2013) where the local polynomial estimation methods are studied. However, Li et al. (2013) impose stronger conditions to rule out the local constant estimation due to technical difficulties. We give the full treatment of the local constant method in this paper based on a novel proof. From the simulation results reported in the paper, we show that the local constant and local linear estimators perform similarly, but the local constant method requires less data. Also, in fnite sample applications the local linear estimation could suffer from the matrix singularity problem.

Suggested Citation

  • Luya Wang & Zhongwen Liang & Juan Lin & Qi Li, 2015. "Local Constant Kernel Estimation of a Partially Linear Varying Coefficient Cointegration Model," Annals of Economics and Finance, Society for AEF, vol. 16(2), pages 353-369, November.
    • Handle: RePEc:cuf:journl:y:2015:v:16:i:2:wang
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    References listed on IDEAS

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    More about this item

    Keywords

    Varying coefficient model; Partially linear model; Nonstationary; Cointegration;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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