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L²损失下密度估计的最优交叉验证AI 解读

We analyze the performance of cross-validation (CV) in the density estimation framework with two purposes: (i) risk estimation and (ii) model selection. The main focus is given to the so-called leave-$p$-out CV procedure (Lpo), where $p$ denotes the cardinality of the test set. Closed-form expressions are settled for the Lpo estimator of the risk of projection estimators. These expressions provide a great improvement upon $V$-fold cross-validation in terms of variability and…

超高维纵向数据的非参数独立筛选与结构识别AI 解读

Ultra-high dimensional longitudinal data are increasingly common and the analysis is challenging both theoretically and methodologically. We offer a new automatic procedure for finding a sparse semivarying coefficient model, which is widely accepted for longitudinal data analysis. Our proposed method first reduces the number of covariates to a moderate order by employing a screening procedure, and then identifies both the varying and constant coefficients using a group SCAD…

协整回归中对识别状态稳健的推断AI 解读

In cointegrating regressions, estimators and test statistics are nuisance parameter dependent. This paper addresses this problem from an identification-robust perspective. Confidence sets for the long-run coefficient (denoted β) are proposed that invert LR-tests against an unrestricted or a cointegration-restricted alternative. For empirically relevant special cases, we provide analytical solutions to the inversion problem. A simulation study, imposing and relaxing strong…

大量矩不等式下的一致估计AI 解读

In this paper, we consider estimation of the identified set when the number of moment inequalities is large relative to sample size, possibly infinite. Many applications in the recent literature on partially identified problems have this feature, including dynamic games, set-identified IV models, and parameters defined by a continuum of moment inequalities, in particular conditional moment inequalities. We provide a generic consistency result for criterion-based estimators…

弱工具变量回归中基于 t 统计量的检验AI 解读

This paper considers tests of the parameter of an endogenous variable in an instrumental variables regression model. The focus is on one-sided conditional t-tests. Theoretical and numerical work shows that the conditional 2SLS and Fuller t-tests perform well even when instruments are weakly correlated with the endogenous variable. When the population F-statistic is as small as two, their power is reasonably close to the power envelopes for similar and non-similar tests which…

私人信号相关的不完全信息二元决策博弈的半参数识别AI 解读

This paper studies the identification and estimation of a static binary decision game of incomplete information. We make no parametric assumptions on the joint distribution of private signals and allow them to be correlated. We show that the parameters of interest can be point-identified subject to a scale normalization under mild support requirements for the regressors (publicly observed state variables) and errors (private signals). Following Manski and Tamer (2002), we…

广义经验似然估计中的近外生性与弱识别：多矩渐近分析AI 解读

This paper investigates the Generalized Empirical Likelihood (GEL) estimators when there are local violations of the exogeneity condition (near exogeneity) in the case of many weak moments. We also examine the tradeoff between the degree of violation of the exogeneity and the number of nearly exogenous instruments. In this respect, this paper extends many weak moment asymptotics of Newey and Windmeijer (2009a). The overidentifying restrictions test can detect both mild and…

利用正则化 JIVE 处理大量弱工具变量的工具变量估计AI 解读

We consider instrumental variables regression in models where the number of available instruments may be larger than the sample size and consistent model selection in the first stage may not be possible. Such a situation may arise when there are many weak instruments. With many weak instruments, existing approaches to first-stage regularization can lead to a large bias relative to standard errors. We propose a jackknife instrumental variables estimator (JIVE) with…

照料年迈父母与欧洲成熟女性的就业状态AI 解读

We study the prevalence of informal caregiving to elderly parents by their mature daughters in Europe and the links between parental health, intense (daily) caregiving, and the employment status of daughters. We group data from SHARE into three country pools (North, Central, and South), which differ in the availability of public formal care services and female labor market attachment. There is a strong North-South gradient in the (positive) effect of parental ill health on…

含极端次序统计量的社会互动模型AI 解读

In this paper, we introduce a social interaction econometric model with an extreme order statistic to model peer effects. We show that the model is a well‐defined system of equations and that it is a static game with complete information. The social interaction model can include exogenous regressors and group effects. Instrumental variables estimators are proposed for the general model that includes exogenous regressors. We also consider distribution‐free methods that use…

类型相关的离散博弈估计AI 解读

In this paper, I focus on the identification and estimation of static games of incomplete information with correlated types. Instead of making the independence assumption on players' types in order to simplify the equilibrium set, I propose an approach that allows me to identify subsets of the space of covariates (i.e. publicly observed state variables in payoff functions), for which there exists a unique pure strategy Bayesian Nash equilibrium (BNE) and the equilibrium…

结构方程可分性的检验AI 解读

Separability is an important feature of structural equations, as it implies the absence of unobservable heterogeneity of effects and has significant implications for identification and efficiency of estimation. This paper provides a nonparametric test for separability in structural equations. The test is based on a conditional independence test recently developed by Huang et al. (2013), building on consistent procedures of Bierens (1982, 1990) and Stinchcombe and White…

若干有限混合模型中的似然推断AI 解读

Parametric mixture models are commonly used in applied work, especially empirical economics, where these models are often employed to learn for example about the proportions of various types in a given population. This paper examines the inference question on the proportions (mixing probability) in a simple mixture model in the presence of nuisance parameters when sample size is large. It is well known that likelihood inference in mixture models is complicated due to (1)…

半强识别下的条件矩模型AI 解读

We consider conditional moment models under semi-strong identification. Identification strength is directly defined through the conditional moments that flatten as the sample size increases. Our new minimum distance estimator is consistent, asymptotically normal, robust to semi-strong identification, and does not rely on the choice of a user-chosen parameter, such as the number of instruments or some smoothing parameter. Heteroskedasticity-robust inference is possible…

部分识别模型的两阶段程序AI 解读

This paper studies a two-stage procedure for estimating partially identified models, based on Chernozhukov, Hong, and Tamer’s (2007) theory of set estimation and inference. We consider the case where a sub-vector of parameters or their identified set can be estimated separately from the rest, possibly subject to a priori restrictions. Our procedure constructs the second-stage set estimator and confidence set by taking appropriate level sets of a criterion function, using a…

We propose the use of instrumental variables and pairwise matching to identify the average treatment effect on variance in potential outcomes. We show that identifying and estimating program impact on dispersion of potential outcomes in an endogenous-switching model is possible, without using the identification-at-infinity argument, if we impose semi-parametric conditions or shape restrictions on the error structure. In the presence of a multi-valued or continuously…

主要基于逆概率加权识别因果机制AI 解读

This paper demonstrates the identification of causal mechanisms of a binary treatment under selection on observables, (primarily) based on inverse probability weighting; i.e. we consider the average indirect effect of the treatment, which operates through an intermediate variable (or mediator) that is situated on the causal path between the treatment and the outcome, as well as the (unmediated) direct effect. Even under random treatment assignment, subsequent selection into…

条件矩不等式推断中的加权 KS 统计量AI 解读

This paper proposes set estimators and conservative confidence regions for the identified set in conditional moment inequality models using Kolmogorov–Smirnov statistics with a truncated inverse variance weighting with increasing truncation points. The new weighting differs from those proposed in the literature in two important ways. First, this paper shows that estimators based on KS statistics with the proposed weighting function converge to the identified set at a faster…

学历误报、多重测量与教育资格的工资回报AI 解读

We consider the identification and estimation of the average wage return to attaining educational qualifications when attainment is potentially measured with error. By exploiting two independent measures of qualifications, we identify the extent of misclassification in administrative and self-reported data on educational attainment. The availability of multiple self-reported educational measures additionally allows us to identify the temporal patterns of individual…

含内生变量的面板数据模型之非参数分析AI 解读

This paper considers the estimation of panel data models by first differences in the presence of endogenous variables and under an instrumental variables condition. This framework leads to the resolution of linear inverse problems solved using a Tikhonov regularization with L2 or Sobolev penalty. Rates of convergence and data driven selection of the regularization parameters are proposed. The practical implementation of our estimators is presented and some Monte Carlo…

弱工具变量与协方差约束下的面板结构建模AI 解读

This paper considers estimating a panel data simultaneous equations model under both coefficient and covariance matrix restrictions in a scenario where one or the other set of identifying restrictions may be invalid or may hold only weakly. We study the limiting properties of various estimators in an asymptotic framework, which takes both the cross-sectional dimension N and the time dimension T to infinity. In this setting as in the pure cross-sectional setup, the…

连续时间模型的计量分析：Phillips 工作综述与若干新结果AI 解读

Econometric analysis of continuous time models has drawn the attention of Peter Phillips for 40 years, resulting in many important publications by him. In these publications he has dealt with a wide range of continuous time models and the associated econometric problems. He has investigated problems from univariate equations to systems of equations, from asymptotic theory to finite sample issues, from parametric models to nonparametric models, from identification problems to…

SUMMARY A new estimator is proposed for linear triangular systems, where identification results from the model errors following a bivariate and diagonal GARCH(1,1) process with potentially time‐varying error covariances. This estimator applies when traditional instruments are unavailable. I demonstrate its usefulness on asset pricing models like the capital asset pricing model and Fama–French three‐factor model. In the context of a standard two‐pass cross‐sectional…

基于惩罚样条与狄利克雷过程混合的贝叶斯非参数工具变量回归AI 解读

We propose a Bayesian nonparametric instrumental variable approach under additive separability that allows us to correct for endogeneity bias in regression models where the covariate effects enter with unknown functional form. Bias correction relies on a simultaneous equations specification with flexible modeling of the joint error distribution implemented via a Dirichlet process mixture prior. Both the structural and instrumental variable equation are specified in terms of…