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Transformation Models with Cointegrated and Deterministically Trending Regressors

In: Essays in Honor of Joon Y. Park: Econometric Theory

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  • Yingqian Lin
  • Yundong Tu

Abstract

This chapter develops an asymptotic theory for a general transformation model with a time trend, stationary regressors, and unit root nonstationary regressors. This model extends that of Han (1987) to incorporate time trend and nonstationary regressors. When the transformation is specified as an identity function, the model reduces to the conventional cointegrating regression, possibly with a time trend and other stationary regressors, which has been studied in Phillips and Durlauf (1986) and Park and Phillips (1988, 1989). The limiting distributions of the extremum estimator of the transformation parameter and the plug-in estimators of other model parameters are found to critically depend upon the transformation function and the order of the time trend. Simulations demonstrate that the estimators perform well in finite samples.

Suggested Citation

  • Yingqian Lin & Yundong Tu, 2023. "Transformation Models with Cointegrated and Deterministically Trending Regressors," Advances in Econometrics, in: Essays in Honor of Joon Y. Park: Econometric Theory, volume 45, pages 207-232, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-90532023000045a007
    DOI: 10.1108/S0731-90532023000045A007
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    More about this item

    Keywords

    Cointegration; extremum estimation; nonlinear model; time trend; transformation model; unit root; C13; C22; C51;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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