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动态面板同期相关的异方差稳健 Breusch–Pagan 检验AI 解读

This paper proposes a heteroskedasticity-robust Breusch–Pagan test of the null hypothesis of zero cross-section (or contemporaneous) correlation in linear panel data models, without necessarily assuming independence of the cross-sections. The procedure allows for either fixed, strictly exogenous and/or lagged dependent regressor variables, as well as quite general forms of both non-normality and heteroskedasticity in the error distribution. The asymptotic validity of the…

不规则时间间隔下的动态面板模型：幼儿发展应用AI 解读

With the increased availability of longitudinal data, dynamic panel data models have become commonplace. Moreover, the properties of various estimators of such models are well known. However, we show that these estimators break down when the data are irregularly spaced along the time dimension. Unfortunately, this is an increasingly frequent occurrence as many longitudinal surveys are collected at non-uniform intervals and no solution is currently available when time-varying…

固定效应动态空间面板模型与脉冲响应分析AI 解读

Real data often have complicated correlations over cross section and time. Such correlations are of particular interests in empirical studies. This paper considers using high order spatial lags and high order time lags to model complicated correlations over cross section and time. We propose to use the quasi maximum likelihood (QML) method to estimate the model. We establish the asymptotic theory of the quasi maximum likelihood estimator (QMLE), including the consistency and…

具有交互固定效应的空间动态面板模型AI 解读

This paper studies the estimation of a dynamic spatial panel data model with interactive individual and time effects with large n and T. The model has a rich spatial structure including contemporaneous spatial interaction and spatial heterogeneity. Dynamic features include individual time lag and spatial diffusion. The interactive effects capture heterogeneous impacts of time effects on cross sectional units. The interactive effects are treated as parameters, so as to allow…

内生时变空间权重下空间动态面板模型的拟极大似然估计AI 解读

In spatial panel data models, when a spatial weights matrix is constructed from economic or social distance, spatial weights could be endogenous and also time varying. This paper presents model specification and proposes QMLE estimation of spatial dynamic panel data models with endogenous time varying spatial weights matrices. Asymptotic properties of the proposed QMLE are rigorously established. We extend the notion of spatial near-epoch dependence to allow time dependence.…

收入与民主：平滑变系数模型的重新考察AI 解读

Summary Acemoglu et al. ( American Economic Review 2008; 98 : 808–842) find no effect of income on democracy when controlling for fixed effects in a dynamic panel model. Work by Moral‐Benito and Bartolucci ( Economics Letters 2012; 117 : 844–847) and Cervellati et al. ( American Economic Review 2014; 104 : 707–719) suggests that the original model might have been misspecified and proposes alternative specifications instead. We formally test these parametric specifications by…

公共债务对产出增长存在门槛效应吗？AI 解读

This paper studies the relationship between public debt expansion and economic growth and investigates whether the debt-growth relation varies with the level of indebtedness. We contribute theoretically by developing tests for threshold effects in the context of dynamic heterogeneous panel data models with cross-sectionally dependent errors. In the empirical application, using data on a sample of forty countries over the 1965–2010 period, we find no evidence for a…

不等时间间隔动态面板数据的识别与估计AI 解读

We propose conditions under which parameters of fixed-effect dynamic models are identified with unequally spaced panel data. Under predeterminedness, weak stationarity, and empirically testable rank conditions, AR(1) parameters are identified given the availability of “two pairs of two consecutive time gaps”, which generalizes “two pairs of two consecutive time periods”. This result extends to models with multiple covariates, higher-order autoregressions, and partial…

强横截面相关下大规模动态面板的推断与时间断点检验AI 解读

In this paper we provide a new Central Limit Theorem for estimators of the slope papers in large dynamic panel data models (where both n and T increase without bound) in the presence of, possibly, strong cross-sectional dependence. We proceed by providing two related tests for breaks/homogeneity in the time dimension. The first test is based on the CUSUM principle; the second test is based on a Hausman–Durbin–Wu approach. Some of the key features of the tests are that they…

具有交互固定效应的动态线性面板回归AI 解读

We analyze linear panel regression models with interactive fixed effects and predetermined regressors, for example lagged-dependent variables. The first-order asymptotic theory of the least squares (LS) estimator of the regression coefficients is worked out in the limit where both the cross-sectional dimension and the number of time periods become large. We find two sources of asymptotic bias of the LS estimator: bias due to correlation or heteroscedasticity of the…

具有门槛效应与内生性的动态面板模型AI 解读

This paper addresses an important issue of modeling nonlinear asymmetric dynamics and unobserved individual heterogeneity in the threshold panel data framework, simultaneously. As a general approach, we develop the first-differenced GMM estimator, which allows both threshold variable and regressors to be endogenous. When the threshold variable becomes strictly exogenous, we propose a more efficient two-step least squares estimator. We provide asymptotic theory and develop…

含交互固定效应与多重结构突变的面板数据模型AI 解读

In this article, we consider estimation of common structural breaks in panel data models with unobservable interactive fixed effects. We introduce a penalized principal component (PPC) estimation procedure with an adaptive group fused LASSO to detect the multiple structural breaks in the models. Under some mild conditions, we show that with probability approaching one the proposed method can correctly determine the unknown number of breaks and consistently estimate the…

含时变内生空间权重、共同因子与随机系数的空间面板自回归模型：贝叶斯分析AI 解读

This article examines spatial panel autoregressive (SAR) models with dynamic, time-varying endogenous spatial weights matrices, common factors, and random coefficients. An empirical application is on the spillover effects of state Medicaid spending. Endogeneity of spatial weights matrices comes from the correlation of “economic distance” and the disturbances in the SAR equation. Common factors control for common shocks to all states and random coefficients may capture…

短时间维度下内生空间权重动态面板模型的工具变量估计AI 解读

The spatial dynamic panel data (SDPD) model is a standard tool for analysing data with both spatial correlation and dynamic dependences among economic units. Conventional estimation methods rely on the key assumption that the spatial weight matrix is exogenous, which would likely be violated in some empirical applications where spatial weights are determined by economic factors. In this paper, we propose an SDPD model with individual fixed effects in a short time dimension,…

随机系数面板模型中的统计推断AI 解读

This paper studies the asymptotics of the Weighted Least Squares (WLS) estimator of the autoregressive root in a panel Random Coefficient Autoregression (RCA). We show that, in an RCA context, there is no “unit root problem” : the WLS estimator is always asymptotically normal, irrespective of the average value of the autoregressive root, of whether the autoregressive coefficient is random or not, and of the presence and degree of cross dependence. Our simulations indicate…

GEL 估计量的模型误设稳健自助法与高阶渐近改进AI 解读

I propose a nonparametric iid bootstrap procedure for the empirical likelihood, the exponential tilting, and the exponentially tilted empirical likelihood estimators that achieves asymptotic refinements for t tests and confidence intervals, and Wald tests and confidence regions based on such estimators. Furthermore, the proposed bootstrap is robust to model misspecification, i.e., it achieves asymptotic refinements regardless of whether the assumed moment condition model is…

模型不确定性与弱外生性下的面板增长实证分析AI 解读

This paper considers panel growth regressions in the presence of model uncertainty and reverse causality concerns. For this purpose, my econometric framework combines Bayesian model averaging with a suitable likelihood function for dynamic panel models with weakly exogenous regressors and fixed effects. An application of this econometric methodology to a panel of countries over the 1960–2000 period highlights the difficulties in identifying the sources of economic growth by…

交互固定效应动态面板模型的收缩估计AI 解读

We consider the problem of determining the number of factors and selecting the proper regressors in linear dynamic panel data models with interactive fixed effects. Based on the preliminary estimates of the slope parameters and factors a la Bai (2009) and Moon and Weidner (2015), we propose a method for simultaneous selection of regressors and factors and estimation through the method of adaptive group Lasso (least absolute shrinkage and selection operator). We show that…

横截面依赖下动态面板模型的估计：利用簇依赖提高效率AI 解读

Summary This paper considers the estimation of dynamic panel data models when data are suspected to exhibit cross‐sectional dependence. A new estimator is defined that uses cross‐sectional dependence for efficiency while being robust to the misspecification of the form of the cross‐sectional dependence. We show that using cross‐sectional dependence for estimation is important to obtain an estimator that is more efficient than existing estimators. This new estimator also uses…

空间 Durbin 面板模型的识别AI 解读

Summary This paper considers identification of spatial Durbin dynamic panel models under 2SLS and ML estimations. We show that the parameters are generally identified via 2SLS moment relations or expected log‐likelihood or quasi‐likelihood functions. Monte Carlo experiments suggest that omitting relevant Durbin terms can result in significant biases in regression estimates, while including an irrelevant Durbin term causes no obvious loss of efficiency. Empirical illustration…

存在附带参数时的模型选择AI 解读

This paper considers model selection in panels where incidental parameters are present. Primary interest centers on selecting a model that best approximates the underlying structure involving parameters that are common within the panel. It is well known that conventional model selection procedures are often inconsistent in panel models and this can be so even without nuisance parameters. Modifications are then needed to achieve consistency. New model selection information…

含弱外生解释变量的异质动态面板共同相关效应估计AI 解读

This paper extends the Common Correlated Effects (CCE) approach developed by Pesaran (2006) to heterogeneous panel data models with lagged dependent variables and/or weakly exogenous regressors. We show that the CCE mean group estimator continues to be valid but the following two conditions must be satisfied to deal with the dynamics: a sufficient number of lags of cross section averages must be included in individual equations of the panel, and the number of cross section…

动态面板数据中的异质性与选择AI 解读

The data generating process (DGP) for generic dynamic panel data consists of a law of state dynamics g, a selection or attrition rule h, and an initial condition F. I study nonparametric identifiability of this complete DGP (g,h,F) using short unbalanced panel data, allowing for nonseparability between observed states and unobserved heterogeneity in each of g, h and F. For T⩾3, the DGP is identified by using a proxy variable. For T⩾6, the three additional periods construct a…

横截面异方差下动态面板变换似然估计的稳健标准误AI 解读

This paper extends the transformed maximum likelihood approach for estimation of dynamic panel data models by Hsiao et al. (2002) to the case where the errors are cross-sectionally heteroskedastic. This extension is not trivial due to the incidental parameters problem and its implications for estimation and inference. We approach the problem by working with a mis-specified homoskedastic model, and then show that the transformed maximum likelihood estimator continues to be…