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纵向与聚类数据半变系数模型的有效估计AI 解读

In semivarying coefficient modeling of longitudinal/clustered data, of primary interest is usually the parametric component which involves unknown constant coefficients. First, we study semiparametric efficiency bound for estimation of the constant coefficients in a general setup. It can be achieved by spline regression using the true within-subject covariance matrices, which are often unavailable in reality. Thus, we propose an estimator when the covariance matrices are…

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…

高维面板模型中的推断：枪支管制应用AI 解读

We consider estimation and inference in panel data models with additive unobserved individual specific heterogeneity in a high-dimensional setting. The setting allows the number of time-varying regressors to be larger than the sample size. To make informative estimation and inference feasible, we require that the overall contribution of the time-varying variables after eliminating the individual specific heterogeneity can be captured by a relatively small number of the…

从新闻流中提炼信息以分析股票反应AI 解读

The gargantuan plethora of opinions, facts, and tweets on financial business offers the opportunity to test and analyze the influence of such text sources on future directions of stocks. It also creates though the necessity to distill via statistical technology the informative elements of this prodigious and indeed colossal data source. Using mixed text sources from professional platforms, blog fora, and stock message boards, we distill via different lexica sentiment…

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,…

通过分位数回归估计非线性面板模型AI 解读

We introduce a class of quantile regression estimators for short panels. Our framework covers static and dynamic autoregressive models, models with general predetermined regressors and models with multiple individual effects. We use quantile regression as a flexible tool to model the relationships between outcomes, covariates and heterogeneity. We develop an iterative simulation‐based approach for estimation, which exploits the computational simplicity of ordinary quantile…

未知对角系数时空模型的广义 Yule–Walker 估计AI 解读

We consider a class of spatio-temporal models which extend popular econometric spatial autoregressive panel data models by allowing the scalar coefficients for each location (or panel) different from each other. To overcome the innate endogeneity, we propose a generalized Yule–Walker estimation method which applies the least squares estimation to a Yule–Walker equation. The asymptotic theory is developed under the setting that both the sample size and the number of locations…

存在结构不稳定性的高维因子模型收缩估计AI 解读

In large-scale panel data models with latent factors the number of factors and their loadings may change over time. Treating the break date as unknown, this article proposes an adaptive group-LASSO estimator that consistently determines the numbers of pre- and post-break factors and the stability of factor loadings if the number of factors is constant. We develop a cross-validation procedure to fine-tune the data-dependent LASSO penalties and show that after the number of…

固定效应自回归模型中的似然推断AI 解读

We calculate the bias of the profile score for the regression coefficients in a multistratum autoregressive model with stratum-specific intercepts. The bias is free of incidental parameters. Centering the profile score delivers an unbiased estimating equation and, upon integration, an adjusted profile likelihood. A variety of other approaches to constructing modified profile likelihoods are shown to yield equivalent results. However, the global maximizer of the adjusted…

内生删失面板数据模型的识别AI 解读

We study inference on parameters in linear panel data models when outcomes are censored. We allow the censoring to depend on both observable and unobservable variables in arbitrary ways. Generally, these models are set identified and the main contribution of this paper is to derive and characterize the identified sets under general conditions. Our main characterization theorems show that every parameter in the sharp set–and only those parameters–can generate the observed…

结构嵌套均值模型的拟合优度检验AI 解读

Coarse structural nested mean models are tools to estimate treatment effects from longitudinal observational data with time-dependent confounding. There is, however, no guidance on how to specify the treatment effect model, and model misspecification can lead to bias. We derive a goodness-of-fit test based on modified overidentification restrictions tests for evaluating a treatment effect model, and show that our test statistic is doubly-robust in the sense that, with a…

存在退出的纵向研究中的内在效率与多重稳健性：表1AI 解读

Intrinsic efficiency and multiple robustness are desirable properties in missing data analysis. We establish both for estimating the mean of a response at the end of a longitudinal study with drop-out. The idea is to calibrate the estimated missingness probability at each visit using data from past visits. We consider one working model for the missingness probability and multiple working models for the data distribution. Intrinsic efficiency guarantees that, when the…

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…

截面依赖指数：估计与推断AI 解读

Summary This paper provides a characterisation of the degree of cross‐sectional dependence in a two dimensional array, { x i t , i = 1,2,... N ; t = 1,2,..., T } in terms of the rate at which the variance of the cross‐sectional average of the observed data varies with N . Under certain conditions this is equivalent to the rate at which the largest eigenvalue of the covariance matrix of x t =( x 1 t , x 2 t ,..., x N t )′ rises with N . We represent the degree of…

含共同随机趋势的面板误差修正检验AI 解读

Summary This paper develops panel data tests for the null hypothesis of no error correction in a model with common stochastic trends. The asymptotic distributions of the new test statistics are derived and simulation results are provided to suggest that they perform well in small samples. Copyright © 2015 John Wiley & Sons, Ltd.

混频动态因子分析与预测的加权极大似然方法AI 解读

For the purpose of forecasting key macroeconomic or financial variables from a panel of time series variables, we adopt the dynamic factor model and propose a weighted likelihood-based method for parameter estimation. The loglikelihood function is split into two parts that are weighted differently. The first part is associated with the key variables while the second part is associated with the related variables which may contribute to the forecasting of key variables. We…

用于居住区位选择推拉效应估计的纵向混合 Logit 模型AI 解读

We develop a random effects discrete choice model for the analysis of households’ choice of neighborhood over time. The model is parameterized in a way that exploits longitudinal data to separate the influence of neighborhood characteristics on the decision to move out of the current area (“push” effects) and on the choice of one destination over another (“pull” effects). Random effects are included to allow for unobserved heterogeneity between households in their propensity…

贝叶斯非参数纵向数据分析AI 解读

Fernando A. Quintanaa*, Wesley O. Johnsonb, L. Elaine Waetjenc & Ellen B. Goldca Departamento de Estadística, Pontificia Universidad Católica de Chile Santiago, Chileb Department of Statistics, University of California Irvine, Irvine, CA, USAc Department of Obstetrics and Gynecology, and Department of Public Health Sciences, Division of Epidemiology, University of California Davis, Davis, CA, USACONTACT Fernando A. Quintana quintana@mat.uc.cl Departamento de Estadística,…

纵向数据折线回归模型参数的快速估计AI 解读

Estimation of change-point locations in the broken-stick model has significant applications in modeling important biological phenomena. In this article we present a computationally economical likelihood-based approach for estimating change-point(s) efficiently in both cross-sectional and longitudinal settings. Our method, based on local smoothing in a shrinking neighborhood of each change-point, is shown via simulations to be computationally more viable than existing methods…

基于收入阶层隔离的社会张力度量AI 解读

We develop an index that effectively measures the level of social tension generated by income class segregation. We adopt the basic concepts of between-group difference (or alienation) and within-group similarity (or identification) from the income [bi]polarization literature; but we allow for asymmetric degrees of between-group antagonism in the alienation function, and construct a more effective identification function using both the relative degree of within-group…

面板结果的贝叶斯处理效应与变量选择：产假对收入的影响AI 解读

We propose two alternative Bayesian treatment effect modeling and inferential frameworks for panel outcomes to estimate dynamic earnings effects of a long maternity leave on mothers’ subsequent earnings. Modeling of the endogeneity of the treatment and the panel structure of the earnings are based on the modeling tradition of the Roy switching regression model and the shared factor approach, respectively. We implement stochastic variable selection to test, for example, for…

面板数据的平滑分位数回归AI 解读

This paper studies fixed effects estimation of quantile regression models for panel data. Under an asymptotic framework where both the numbers of individuals and time periods grow at the same rate, we show that the fixed-effects estimator for the smoothed objective function has a limiting normal distribution with a bias in the mean, and provide the analytic form of the asymptotic bias. We propose a one-step bias correction estimator based on the analytic bias formula…

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…

区域政策评估：交互固定效应与合成控制AI 解读

In this paper, we investigate the use of interactive effect or linear factor models in regional policy evaluation. We contrast treatment effect estimates obtained using Bai (2009) with those obtained using difference in differences and synthetic controls (Abadie and coauthors). We show that difference in differences are generically biased, and we derive support conditions for synthetic controls. We construct Monte Carlo experiments to compare these estimation methods in…