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Essays on Testing Hypotheses When Non-Stationarity Exists in Panel Data Models.

机译：面板数据模型中存在非平稳性时测试假设的论文。

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摘要

This dissertation consists of two essays on testing hypotheses in panel data models when non-stationarity exists in the model. This is done under the high-dimensional framework where both n (cross-section dimension) and T (time series dimension) are large. In the first essay, I discuss the limiting distribution of the t-statistic; using different panel data estimators and propose using the t-statistic based on Feasible GLS estimator. In the second essay, I develop the bootstrap F-statistic for cross-sectional independence in a panel data model with factor structure.;The first essay considers the problem of hypotheses testing in a simple panel data regression model with random individual effects and serially correlated disturbances. Following Baltagi, Kao and Liu (2008), I allow for the possibility of non-stationarity in the regressor and/or the disturbance term. While Baltagi et al. (2008) focus on the asymptotic properties and distributions of the standard panel data estimators, this essay focuses on test of hypotheses in this setting. One important finding, is that unlike the time series case, one does not necessarily need to rely on the "super-efficient" type AR estimator by Perron and Yabu (2009) to make inference in panel data. In fact, I show that the simple t-ratio always converges to the standard normal distribution regardless of whether the disturbances and/or the regressor are stationary. One caveat is that this may not be robust to heteroskedasticity of the error terms, but it is robust to heterogenous AR parameters across individuals. The Monte Carlo simulations in support of all the results are also provided in this essay.;The second essay discusses testing hypotheses of cross-sectional dependence in a panel data model with an introduction of factor structure. Following Bai (2003, 2004, 2009) and Bai, Kao and Ng (2009), I again allow for the possibility of non-stationarity in the regressor and the factor. I give attention to test of hypotheses using F-tests in this setting. The limiting distribution of F-statistics under the high-dimensional framework has not been derived yet in the literature perhaps because of its theoretical complexity. To circumvent this difficulty, this essay suggests the use of wild bootstrap F-tests based on simulation results under various cases where both regressors and factors can be stationary or non-stationary. The Monte Carlo results show that the bootstrap F-tests perform well in testing cross-sectional independence and are recommended in practice. They have the advantage of being feasible even when we do not observe the factors and do not require for formal theoretical approximations. It is also shown that the bootstrap F-tests are robust to heteroskedasticity but sensitive to serial correlation.
机译：本文由两篇关于面板数据模型中假设不稳定的检验假设的论文组成。这是在n（横截面尺寸）和T（时间序列尺寸）都很大的高维框架下完成的。在第一篇文章中，我讨论了t统计量的极限分布。使用不同的面板数据估计量，并建议使用基于可行GLS估计量的t统计量。在第二篇文章中，我开发了具有因子结构的面板数据模型中用于横截面独立性的Bootstrap F统计量;第一篇文章考虑了在具有随机个体效应且具有序列相关性的简单面板数据回归模型中进行假设检验的问题干扰。根据Baltagi，Kao和Liu（2008）的研究，我考虑了回归变量和/或扰动项存在非平稳性的可能性。而Baltagi等。 （2008年）侧重于标准面板数据估计量的渐近性质和分布，本文着重于在这种情况下检验假设。一个重要的发现是，与时间序列的情况不同，不一定需要依靠Perron和Yabu（2009）的“超高效” AR估计量来推断面板数据。实际上，我证明了简单的t比率总是收敛于标准正态分布，而不管扰动和/或回归变量是否是固定的。一个警告是，这可能对误差项的异方差性不强，但对个体之间的异质AR参数则强健。本文还提供了支持所有结果的蒙特卡洛模拟。第二篇文章讨论了在面板数据模型中测试截面相关性假设的假设，并介绍了因子结构。继Bai（2003，2004，2009）以及Bai，Kao和Ng（2009）之后，我再次考虑了回归变量和因子不稳定的可能性。在这种情况下，我会注意使用F检验的假设检验。在高维框架下F统计量的极限分布可能还因为其理论上的复杂性而尚未在文献中得出。为了避免这一困难，本文建议在各种情况下，回归变量和因素可能是固定的或非固定的，都应根据模拟结果使用野生自举F检验。蒙特卡洛结果表明，自举F检验在测试横截面独立性方面表现良好，建议在实践中使用。即使我们不注意这些因素并且不需要形式上的理论近似，它们也具有可行性。还显示自举F检验对异方差具有鲁棒性，但对序列相关性敏感。

著录项

  • 作者

    Na, Sang Gon.;

  • 作者单位

    Syracuse University.;

  • 授予单位 Syracuse University.;
  • 学科 Economics General.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 258 p.
  • 总页数 258
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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