The local to unity dynamic Tobit model

IF 9.9 3区 经济学 Q1 ECONOMICS Journal of Econometrics Pub Date : 2024-04-01 DOI:10.1016/j.jeconom.2024.105764
Anna Bykhovskaya , James A. Duffy
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Abstract

This paper considers highly persistent time series that are subject to nonlinearities in the form of censoring or an occasionally binding constraint, such as are regularly encountered in macroeconomics. A tractable candidate model for such series is the dynamic Tobit with a root local to unity. We show that this model generates a process that converges weakly to a non-standard limiting process, that is constrained (regulated) to be positive. Surprisingly, despite the presence of censoring, the OLS estimators of the model parameters are consistent. We show that this allows OLS-based inferences to be drawn on the overall persistence of the process (as measured by the sum of the autoregressive coefficients), and for the null of a unit root to be tested in the presence of censoring. Our simulations illustrate that the conventional ADF test substantially over-rejects when the data is generated by a dynamic Tobit with a unit root, whereas our proposed test is correctly sized. We provide an application of our methods to testing for a unit root in the Swiss franc/euro exchange rate, during a period when this was subject to an occasionally binding lower bound.

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局部到统一动态托比特模型
本文研究的是宏观经济学中经常遇到的、受非线性因素影响的高持久性时间序列,这些非线性因素包括普查或偶尔出现的约束条件。此类序列的一个可行候选模型是动态托比特模型,其根在本地为一。我们的研究表明,该模型生成的过程会弱收敛于一个非标准的极限过程,该过程受约束(调节)为正值。令人惊讶的是,尽管存在普查,模型参数的 OLS 估计值却是一致的。我们表明,这使得基于 OLS 的推论可以得出过程的总体持续性(以自回归系数之和衡量),并在存在剔除的情况下检验单位根的空值。我们的模拟结果表明,当数据由具有单位根的动态 Tobit 生成时,传统的 ADF 检验会出现严重的过度拒绝,而我们提出的检验却能正确地确定单位根的大小。我们将我们的方法应用于瑞士法郎/欧元汇率的单位根检验,在此期间,瑞士法郎/欧元汇率偶尔会受到约束性下限的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Econometrics
Journal of Econometrics 社会科学-数学跨学科应用
CiteScore
8.60
自引率
1.60%
发文量
220
审稿时长
3-8 weeks
期刊介绍: The Journal of Econometrics serves as an outlet for important, high quality, new research in both theoretical and applied econometrics. The scope of the Journal includes papers dealing with identification, estimation, testing, decision, and prediction issues encountered in economic research. Classical Bayesian statistics, and machine learning methods, are decidedly within the range of the Journal''s interests. The Annals of Econometrics is a supplement to the Journal of Econometrics.
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