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面板数据分析中的结构识别AI 解读

Panel data analysis is an important topic in statistics and econometrics. In such analysis, it is very common to assume the impact of a covariate on the response variable remains constant across all individuals. While the modelling based on this assumption is reasonable when only the global effect is of interest, in general, it may overlook some individual/subgroup attributes of the true covariate impact. In this paper, we propose a data driven approach to identify the…

利用采购经理指数预测产出增长的多国方法AI 解读

This paper derives new theoretical results for forecasting with Global VAR (GVAR) models. It is shown that the presence of strong unobserved common factors can lead to an undetermined GVAR model. To solve this problem, we propose augmenting the GVAR with additional proxy equations for the strong factors and establish conditions under which forecasts from the augmented GVAR model (AugGVAR) uniformly converge in probability (as the panel dimensions N,T→j∞) to the infeasible…

偏斜纵向数据的偏正态前依赖模型AI 解读

Antedependence models, also known as transition models, have proven to be useful for longitudinal data exhibiting serial correlation, especially when the variances and/or same-lag correlations are time-varying. Statistical inference procedures associated with normal antedependence models are well-developed and have many nice properties, but they are not appropriate for longitudinal data that exhibit considerable skewness. We propose two direct extensions of normal…

控制点与数据合并：因子结构模型的识别和推断AI 解读

Factor structure models are widely used in economics to extract latent variables, such as personality traits, and to measure their impact on outcomes of interest. The identification and inference of these models, however, highly depend on the availability of rich longitudinal data. To overcome the common problem of data scarcity, this paper proposes to combine datasets that each identify some part of the likelihood, thereby recovering the identification of the complete…

面板数据模型中斜率同质性的拉格朗日乘数型检验AI 解读

In this paper, we employ the Lagrange multiplier (LM) principle to test parameter homogeneity across cross‐section units in panel data models. The test can be seen as a generalization of the Breusch–Pagan test against random individual effects to all regression coefficients. While the original test procedure assumes a likelihood framework under normality, several useful variants of the LM test are presented to allow for non‐normality, heteroscedasticity and serially…

大 N、大 T 非线性面板模型中的个体与时间效应AI 解读

We derive fixed effects estimators of parameters and average partial effects in (possibly dynamic) nonlinear panel data models with individual and time effects. They cover logit, probit, ordered probit, Poisson and Tobit models that are important for many empirical applications in micro and macroeconomics. Our estimators use analytical and jackknife bias corrections to deal with the incidental parameter problem, and are asymptotically unbiased under asymptotic sequences…

基于留一受试者交叉验证的模型平均AI 解读

This paper develops a frequentist model averaging method based on the leave-subject-out cross-validation. This method is applicable not only to averaging longitudinal data models, but also to averaging time series models which can have heteroscedastic errors. The resulting model averaging estimators are proved to be asymptotically optimal in the sense of achieving the lowest possible squared errors. Both simulation study and empirical example show the superiority of the…

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…

利用纵向盲法评估联合估计治疗效应与安慰剂效应AI 解读

In some therapeutic areas, treatment evaluation is frequently complicated by a possible placebo effect (i.e., the psychobiological effect of a patient's knowledge or belief of being treated). When a substantial placebo effect is likely to exist, it is important to distinguish the treatment and placebo effects in quantifying the clinical benefit of a new treatment. These causal effects can be formally defined in a joint causal model that includes treatment (e.g., new versus…

利用二元调查过程辅助数据校正调查分析中的测量误差AI 解读

Paradata refers here to data at unit level on an observed auxiliary variable, not usually of direct scientific interest, which may be informative about the quality of the survey data for the unit. There is increasing interest among survey researchers in how to use such data. Its use to reduce bias from nonresponse has received more attention so far than its use to correct for measurement error. This article considers the latter with a focus on binary paradata indicating the…

纵向数据半参数协方差分析模型的广义准似然比检验AI 解读

We model generalized longitudinal data from multiple treatment groups by a class of semiparametric analysis of covariance models, which take into account the parametric effects of time dependent covariates and the nonparametric time effects. In these models, the treatment effects are represented by nonparametric functions of time and we propose a generalized quasi-likelihood ratio test procedure to test if these functions are identical. Our estimation procedure is based on…

存在不可观测异质性的处理效应：集合识别方法AI 解读

We propose the sharp identifiable bounds of the potential outcome distributions using panel data. We allow for the possibility that statistical randomization of treatment assignments is not achieved until unobserved heterogeneity is properly controlled for. We use certain stationarity assumptions to obtain the sharp bounds. Our approach allows for dynamic treatment decisions, where the current treatment decisions may depend on the past treatments or the past observed…

未知共同因子的识别：引领者与跟随者AI 解读

This article has the following contributions. First, this article develops a new criterion for identifying whether or not a particular time series variable is a common factor in the conventional approximate factor model. Second, by modeling observed factors as a set of potential factors to be identified, this article reveals how to easily pin down the factor without performing a large number of estimations. This allows the researcher to check whether or not each individual…

This article proposes a class of weighted differences of averages (WDA) statistics to test and estimate possible change-points in variance for time series with weakly dependent blocks and dependent panel data without specific distributional assumptions. We derive the asymptotic distributions of the test statistics for testing the existence of a single variance change-point under the null and local alternatives. We also study the consistency of the change-point estimator.…

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…

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…

通过自适应组融合LASSO估计面板共同突变AI 解读

In this paper we consider estimation and inference of common breaks in panel data models via adaptive group fused Lasso. We consider two approaches—penalized least squares (PLS) for first-differenced models without endogenous regressors, and penalized GMM (PGMM) for first-differenced models with endogeneity. We show that with probability tending to one, both methods can correctly determine the unknown number of breaks and estimate the common break dates consistently. We…

纵向数据充分降维的估计方程方法AI 解读

Sufficient dimension reduction has been extensively explored in the context of independent and identically distributed data. In this article we generalize sufficient dimension reduction to longitudinal data and propose an estimating equation approach to estimating the central mean subspace. The proposed method accounts for the covariance structure within each subject and improves estimation efficiency when the covariance structure is correctly specified. Even if the…

基于误差分布估计的参数混合效应模型拟合优度检验AI 解读

We address the problem of testing for a parametric function of fixed effects in mixed models. We propose a test based on the distance between two empirical error distribution functions, which are constructed from residuals calculated under the opposing hypotheses. The proposed test statistic has power against all alternatives, and its asymptotic distribution is derived. A simulation study shows that the test outperforms others in the literature. The test is applied to…

J检验向空间面板数据框架的扩展AI 解读

Summary Kelejian ( Letters in Spatial and Resources Sciences ; 1 : 3–11) extended the J ‐test procedure to a spatial framework. Although his suggested test was computationally simple and intuitive, it did not use the available information in an efficient manner. Kelejian and Piras ( Regional Science and Urban Economics ; 41 : 281–292) generalized and modified Kelejian's test to account for all the available information. However, neither Kelejian ( ) nor Kelejian & Piras ( )…

大规模协方差矩阵与精度矩阵估计综述AI 解读

The estimation of large covariance and precision matrices is fundamental in modern multivariate analysis. However, problems arise from the statistical analysis of large panel economic and financial data. The covariance matrix reveals marginal correlations between variables, while the precision matrix encodes conditional correlations between pairs of variables given the remaining variables. In this paper, we provide a selective review of several recent developments on the…

方向距离函数：最优内生方向AI 解读

A substantial literature has dealt with the problem of estimating multiple-input and multiple-output production functions, where inputs and outputs can be good and bad. Numerous studies can be found in the areas of productivity analysis, industrial organization, labor economics, and health economics. While many papers have estimated the more restrictive output- and input-oriented distance functions, here we estimate a more general directional distance function. A seminal…

若干具有内生性的随机前沿模型AI 解读

We consider mostly Bayesian estimation of stochastic frontier models where one-sided inefficiencies and/or the idiosyncratic error term are correlated with the regressors. We begin with a model where a Chamberlain–Mundlak device is used to relate a transformation of time-invariant effects to the regressors. This basic model is then extended in two directions: first an extra one-sided error term is added to allow for time-varying efficiencies. Second, a model with an equation…

具有非对称效率溢出的面板空间自回归随机前沿模型AI 解读

By blending seminal literature on non-spatial stochastic frontier models with key contributions to spatial econometrics we develop a spatial autoregressive (SAR) stochastic frontier for panel data. The specification of the SAR frontier allows efficiency to vary over time and across the cross-sections. Efficiency is calculated from a composed error structure by assuming a half-normal distribution for inefficiency. The SAR frontier is estimated using maximum likelihood methods…