Subsampling hypothesis tests for nonstationary panels with applications to exchange rates and stock prices
非平稳面板的子抽样假设检验:汇率与股票价格应用AI 解读 ↓
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Abstract This paper studies subsampling hypothesis tests for panel data that may be nonstationary, cross‐sectionally correlated, and cross‐sectionally cointegrated. The subsampling approach provides approximations to the finite sample distributions of the tests without estimating nuisance parameters. The tests include panel unit root and cointegration tests as special cases. The number of cross‐sectional units is assumed to be finite and that of time‐series observations infinite. It is shown that subsampling provides asymptotic distributions that are equivalent to the asymptotic distributions of the panel tests. In addition, the tests using critical values from subsampling are shown to be consistent. The subsampling methods are applied to panel unit root tests. The panel unit root tests considered are Levin, Lin, and Chu's ( 2002 ) t ‐test; Im, Pesaran, and Shin's ( 2003 ) averaged t ‐test; and Choi's ( 2001 ) inverse normal test. Simulation results regarding the subsampling panel unit root tests and some existing unit root tests for cross‐sectionally correlated panels are reported. In using the subsampling approach to examine the real exchange rates of the G7 countries and a group of 26 OECD countries, we find only mixed support for the purchasing power parity (PPP) hypothesis. We then examine a panel of 17 developed stock market indexes, and also find only mixed empirical support for them exhibiting relative mean reversion with respect to the US stock market index. Copyright © 2007 John Wiley & Sons, Ltd.
非平稳数据下的反事实分析与推断
Recently, there has been growing interest in developing econometric tools to conduct counterfactual analysis with aggregate data when a single “treated” unit suffers an intervention, such as a policy change, and there is no obvious control group. Usually, the proposed methods are based on the construction of an artificial/synthetic counterfactual from a pool of “untreated” peers, organized in a panel data structure. In this article, we investigate the consequences of…
对横截面依赖稳健的面板单位根区块自助检验
In this paper we consider the issue of unit root testing in cross-sectionally dependent panels. We consider panels that may be characterized by various forms of cross-sectional dependence including (but not exclusive to) the popular common factor framework. We consider block bootstrap versions of the group-mean (Im et al., 2003) and the pooled (Levin et al., 2002) unit root coefficient DF tests for panel data, originally proposed for a setting of no cross-sectional…
异质性面板中的微观与宏观协整
We consider the issue of cross-sectional aggregation in nonstationary and heterogeneous panels where each unit cointegrates. We derive asymptotic properties of the aggregate estimate, and necessary and sufficient conditions for cointegration to hold in the aggregate relationship. We then analyze the case when cointegration does not carry through the aggregation process, and we investigate whether the violation of the formal conditions for perfect aggregation can still lead…
意大利内部迁移的长期趋势:存在个体依赖的面板协整研究
Abstract The objective of this paper is to examine the long‐run determinants of internal migrations from southern Italy. In order to accomplish this task, the paper develops a bootstrap test for panel cointegration analysis with dependent units. Monte Carlo simulations show that the test, based on the Continuous‐Path Block bootstrap, has good power and size properties and is robust to both short‐ and long‐run dependence across units. The empirical analysis points to income…
更高功效的面板单位根检验及其实际汇率均值回复应用
Abstract Unit root tests, seeking mean or trend reversion, are frequently applied to panel data. We show that more powerful variants of commonly applied tests are readily available. Moreover, power gains persist when the modifications are applied to bootstrap procedures that may be employed when cross‐correlation of a rather general sort among individual panel members is suspected. Copyright © 2004 John Wiley & Sons, Ltd.