Unit Root Test In A Threshold Autoregression: Asymptotic Theory And Residual-Based Block Bootstrap
AbstractThere is a growing literature on unit root testing in threshold autoregressive models. This paper makes two contributions to the literature. First, an asymptotic theory is developed for unit root testing in a threshold autoregression, in which the errors are allowed to be dependent and heterogeneous, and the lagged level of the dependent variable is employed as the threshold variable. The asymptotic distribution of the proposed Wald test is non-standard and depends on nuisance parameters. Second, the consistency of the proposed residual-based block bootstrap is established based on a newly developed asymptotic theory for this bootstrap. It is demonstrated by a set of Monte Carlo simulations that the Wald test exhibits considerable power gains over the ADF test that neglects threshold effects. The law of one price hypothesis is investigated among used car markets in the US
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Bibliographic InfoArticle provided by Cambridge University Press in its journal Econometric Theory.
Volume (Year): 24 (2008)
Issue (Month): 06 (December)
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Other versions of this item:
- Myunghwan Seo, 2004. "Unit Root Test in a Threshold Autoregression: Asymptotic Theory and Residual-based Block Bootstrap," Econometric Society 2004 North American Summer Meetings 494, Econometric Society.
- Myunghwan Seo, 2005. "Unit Root Test in a Threshold Autoregression: Asymptotic Theory and Residual-based Block Bootstrap," STICERD - Econometrics Paper Series /2005/484, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
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