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交互效应面板模型中的多重结构突变AI 解读

ABSTRACT This paper develops new econometric methods for multiple structural break detection in panel data models with interactive fixed effects. The new methods include tests for the presence of structural breaks, estimators for the number of breaks and their location, and a method for constructing asymptotically valid break date confidence intervals. The new methodology is applied to a large panel of US banks for a period characterized by massive quantitative easing…

使用 CCE 估计因子的嵌套模型等预测准确性检验AI 解读

In this article, we propose new tests of equal predictive ability between nested models when factor-augmented regressions are used to forecast. In contrast to the previous literature, the unknown factors are not estimated by principal components but by the common correlated effects (CCE) approach, which employs cross-sectional averages of blocks of variables. This makes for easy interpretation of the estimated factors, and the resulting tests are easy to implement and they…

利用横截面均值增广时间序列回归进行预测AI 解读

Summary There is a large and growing body of literature concerned with forecasting time series variables by the use of factor-augmented regression models. The workhorse of this literature is a two-step approach in which the factors are first estimated by applying the principal components method to a large panel of variables, and the forecast regression is then estimated, conditional on the first-step factor estimates. Another stream of research that has attracted much…

近单位根交互效应面板中的因子分析方法AI 解读

In a recent study, Bai (2013) proposes a new factor analytical (FA) method for estimation of stationary dynamic panel data models with fixed effects. Our interest in this method originates with the fact it does not require explicit demeaning of the data, a practice that is known to cause problems of bias and low power in near unit root panels. The purpose is to study the properties of FA when applied to such panels when the common component admits to a interactive effects…

适用于固定T面板的截面平均主成分方法AI 解读

Summary Because of the increased availability of large panel data sets, common factor models have become very popular. The workhorse of the literature is the principal components (PC) method, which is based on an eigen‐analysis of the sample covariance matrix of the data. Some of its uses are to estimate the factors and their loadings, to determine the number of factors, and to conduct inference when estimated factors are used in panel regression models. The bulk of the…

引入协变量的最优面板单位根检验AI 解读

Summary This paper provides asymptotic optimality results for panel unit root tests with covariates by deriving the Gaussian power envelope. The main conclusion is that the use of covariates holds considerable promise in the panel data context, much more so than in the time series context. In fact, the use of the covariates not only leads to increased power, but can actually have an order effect on the shrinking neighbourhoods around unity for which power is non-negligible.

面板单位根检验中 GLS 去均值的作用AI 解读

One of the most well-known facts about unit root testing in time series is that the Dickey–Fuller (DF) test based on ordinary least squares (OLS) demeaned data suffers from low power, and that the use of generalized least squares (GLS) demeaning can lead to substantial power gains. Of course, this development has not gone unnoticed in the panel unit root literature. However, while the potential of using GLS demeaning is widely recognized, oddly enough, there are still no…

因子增广面板回归中 CCE 估计的秩条件作用AI 解读

A popular approach to factor-augmented panel regressions is the common correlated effects (CCE) estimator of Pesaran (2006). This paper points to a problem with the CCE approach that appears in the empirically relevant case when the number of factors is strictly less than the number of observables used in their estimation. Specifically, the use of too many observables causes the second moment matrix of the estimated factors to become asymptotically singular, an issue that…

PANICCA：结合横截面均值的面板非平稳性分析AI 解读

Summary The cross‐section average (CA) augmentation approach of Pesaran (A simple panel unit root test in presence of cross‐section dependence. Journal of Applied Econometrics 2007; 22 : 265–312) and Pesaran et al. (Panel unit root test in the presence of a multifactor error structure. Journal of Econometrics 2013; 175 : 94–115), and the principal components‐based panel analysis of non‐stationarity in idiosyncratic and common components (PANIC) of Bai and Ng (A PANIC attack…

理解面板单位根检验局部渐近功效的新工具AI 解读

Motivated by the previously documented discrepancy between actual and predicted power, the present paper provides new tools for analyzing the local asymptotic power of panel unit root tests. These tools are appropriate in general when considering panel data with a dominant autoregressive root of the form ρi=1+ciN−κT−τ, where i=1,…,N indexes the cross-sectional units, T is the number of time periods and ci is a random local-to-unity parameter. A limit theory for the sample…

重新审视面板单位根检验的单变量方法：用协变量解决附带趋势问题AI 解读

In an influential article, Hansen showed that covariate augmentation can lead to substantial power gains when compared to univariate tests. In this article, we ask if this result extends also to the panel data context? The answer turns out to be yes, which is maybe not that surprising. What is surprising, however, is the extent of the power gain, which is shown to more than outweigh the well-known power loss in the presence of incidental trends. That is, the covariates have…

非平稳面板的非参数秩检验AI 解读

We develop a set of nonparametric rank tests for non-stationary panels based on multivariate variance ratios which use untruncated kernels. As such, the tests do not require the choice of tuning parameters associated with bandwidth or lag length and also do not require choices with respect to numbers of common factors. The tests allow for unrestricted cross-sectional dependence and dynamic heterogeneity among the units of the panel, provided simply that a joint functional…

横截面均值与主成分方法的比较AI 解读

In spite of the increased use of factor-augmented regressions in recent years, little is known regarding the relative merits of the two main approaches to estimation and inference, namely, the cross-sectional average and principal component estimators. By providing a formal comparison of the approaches, the current paper fills this gap in the literature.

递归去趋势对面板单位根检验的影响AI 解读

This paper analyzes the properties of panel unit root tests based on recursively detrended data. The analysis is conducted while allowing for a (potentially) non-linear trend function, which represents a more general consideration than the current state of affairs with (at most) a linear trend. A new test statistic is proposed whose asymptotic behavior under the unit root null hypothesis, and the simplifying assumptions of a polynomial trend and iid errors are shown to be…

PANIC面板单位根检验的功效AI 解读

The current paper considers the asymptotic local power of second-generation panel unit root tests that are robust to the presence of cross-section dependence in the form of common factors. As a basis for our analysis, we take the PANIC approach of Bai and Ng (2004, 2010), which is one of the single most popular and general second-generation approaches around.

异方差稳健的面板单位根检验AI 解读

This article proposes new unit root tests for panels where the errors may be not only serial and/or cross-correlated, but also unconditionally heteroscedastic. Despite their generality, the test statistics are shown to be very simple to implement, requiring only minimal corrections and still the limiting distributions under the null hypothesis are completely free from nuisance parameters. Monte Carlo evidence is also provided to suggest that the new tests perform well in…

随机系数面板模型中的单位根检验AI 解读

This paper proposes new unit root tests in the context of a random autoregressive coefficient panel data model, in which the null of a unit root corresponds to the joint restriction that the autoregressive coefficient has unit mean and zero variance. The asymptotic distributions of the test statistics are derived and simulation results are provided to suggest that they perform very well in small samples.

协整面板的参数可合并性新检验AI 解读

This paper proposes a new test of the null hypothesis that the parameters in a cointegrated panel data regression are equal across the cross-section. The asymptotic distribution of the new test statistic is derived and simulation results are provided to suggest that it performs very well in small samples. An empirical application to the monetary exchange rate model is also provided. Copyright (C) 2009 John Wiley & Sons, Ltd.

关于个体 PANIC 单位根检验合并的说明AI 解读

One of the most cited studies in recent years within the field of nonstationary panel data analysis is that of Bai and Ng (2004), in which the authors propose PANIC, a new framework for analyzing the nonstationarity of panels with idiosyncratic and common components. The problem is that the asymptotic validity of PANIC as a platform for constructing pooled panel unit root tests based on averaging is not fully proven. This paper provides the required results, whose usefulness…

费雪效应的面板协整检验AI 解读

Abstract Most empirical evidence suggests that the Fisher effect, stating that inflation and nominal interest rates should cointegrate with a unit slope on inflation, does not hold, a finding at odds with many theoretical models. This paper argues that these results can be attributed in part to the low power of univariate tests, and that the use of panel data can generate more powerful tests. For this purpose, we propose two new panel cointegration tests that can be applied…