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Summary We propose a novel bootstrap-based test for cross-sectional dependence (CD) in panel models, maintaining robustness against serial dependence. While serial dependence is common in panel data, existing tests often assume serial independence. Our cluster wild bootstrap CD test procedure mirrors Pesaran’s original CD test and is very simple to implement. This procedure preserves serial dependence while testing for cross-sectional independence. Theoretical validity is…

具有多重结构变化的高维因子模型估计与检验AI 解读

This paper considers multiple changes in the factor loadings of a high dimensional factor model occurring at dates that are unknown but common to all subjects. Since the factors are unobservable, the problem is converted to estimating and testing structural changes in the second moments of the pseudo factors. We consider both joint and sequential estimation of the change points and show that the distance between the estimated and the true change points is Op(1). We find that…

含内生回归变量的异质面板中的结构变化AI 解读

Summary This paper extends Pesaran's ( Econometrica , 2006, 74 , 967–1012) common correlated effects (CCE) by allowing for endogenous regressors in large heterogeneous panels with unknown common structural changes in slopes and error factor structure. Since endogenous regressors and structural breaks are often encountered in empirical studies with large panels, this extension makes Pesaran's CCE approach empirically more appealing. In addition to allowing for slope…

结构不稳定高维因子模型的识别与估计AI 解读

This paper tackles the identification and estimation of a high dimensional factor model with unknown number of latent factors and a single break in the number of factors and/or factor loadings occurring at unknown common date. First, we propose a least squares estimator of the change point based on the second moments of estimated pseudo factors and show that the estimation error of the proposed estimator is Op(1). We also show that the proposed estimator has some degree of…

存在结构突变的异质面板估计AI 解读

This paper extends Pesaran’s (2006) work on common correlated effects (CCE) estimators for large heterogeneous panels with a general multifactor error structure by allowing for unknown common structural breaks. Structural breaks due to new policy implementation or major technological shocks, are more likely to occur over a longer time span. Consequently, ignoring structural breaks may lead to inconsistent estimation and invalid inference. We propose a general framework that…

固定效应面板模型中横截面依赖的拉格朗日乘子检验AI 解读

It is well known that the standard Breusch and Pagan (1980) LM test for cross-equation correlation in a SUR model is not appropriate for testing cross-sectional dependence in panel data models when the number of cross-sectional units (n) is large and the number of time periods (T) is small. In fact, a scaled version of this LM test was proposed by Pesaran (2004) and its finite sample bias was corrected by Pesaran et al. (2008). This was done in the context of a heterogeneous…

This paper proposes a test for sphericity in a fixed effects panel data model. It uses the Random Matrix Theory based approach of Ledoit and Wolf (2002) to test for sphericity of the error terms in a fixed effects panel model with a large number of cross-sectional units and time series observations. Since the errors are unobservable, the residuals from the fixed effects regression are used. The limiting distribution of the proposed test statistic is derived. Additionally,…

含全球随机趋势的面板协整AI 解读

This paper studies estimation of panel cointegration models with cross-sectional dependence generated by unobserved global stochastic trends. The standard least squares estimator is, in general, inconsistent owing to the spuriousness induced by the unobservable I(1) trends. We propose two iterative procedures that jointly estimate the slope parameters and the stochastic trends. The resulting estimators are referred to respectively as CupBC (continuously-updated and…

This paper studies the asymptotic properties of standard panel data estimators in a simple panel regression model with random error component disturbances. Both the regressor and the remainder disturbance term are assumed to be autoregressive and possibly non-stationary. Asymptotic distributions are derived for the standard panel data estimators including ordinary least squares (OLS), fixed effects (FE), first-difference (FD) and generalized least squares (GLS) estimators…

面板模型中未知形式序列相关的小波检验AI 解读

Wavelet analysis is a new mathematical method developed as a unified field of science over the last decade or so. As a spatially adaptive analytic tool, wavelets are useful for capturing serial correlation where the spectrum has peaks or kinks, as can arise from persistent dependence, seasonality, and other kinds of periodicity. This paper proposes a new class of generally applicable wavelet-based tests for serial correlation of unknown form in the estimated residuals of a…

面板数据中的伪回归与残差型协整检验AI 解读

In the first half of the paper I study spurious regressions in panel data. Asymptotic properties of the least-squares dummy variable (LSDV) estimator and other conventional statistics are examined. The asymptotics of LSDV estimator are different from those of the spurious regression in the pure time-series. This has an important consequence for residual-based cointegration tests in panel data, because the null distribution of residual-based cointegration tests depends on the…

含有序不可观测变量的线性模型两步估计：公司债券应用AI 解读

This article proposes a two-step method for estimating the impact of bond indenture provisions and other financial variables on the risk and yields of investment-grade and speculative corporate bonds. In the first step, the default risk of bonds is estimated as a function of indenture provisions and the characteristics of bonds and the issuing firms by an ordered probit. In the second step, the effects of default risk and bond characteristics on yields are estimated after a…

非线性有界影响估计在美联储银行总借款中的应用AI 解读

Forecasting aggregate discount window borrowing has posed difficulties for the Federal Reserve, due in part to outliers resulting from numerous institutional changes and special borrowing situations. This article applies bounded influence estimation and influence diagnostics to identify and adjust for outliers in the case of discount window borrowing. Since most banks borrow from the Federal Reserve infrequently, the model is nonlinear and of the switching regression class.…