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含内生性的时变线性变换面板模型中的偏效应AI 解读

This article develops a new estimator for an average partial effect in nonlinear panel models, where outcomes are time-varying monotonic transformations of latent variables that include fixed effects and endogenous regressors. The partial effect can be time-varying and the counterfactual shift is scale invariant—key advantages over the linear model. We exploit a conditional moment restriction and address the ill-posed nature of recovering the transformation functions using…

利用面板数据估计交互效应AI 解读

ABSTRACT This paper analyzes how interaction effects can be consistently estimated under economically plausible assumptions in linear panel models with a fixed ‐dimension. We advocate for a correlated interaction term effects (CITE) estimator and show that it is consistent under conditions that are not sufficient for consistency of the interaction term effect estimator that is most common in applied econometric work. Our paper discusses the empirical content of these…

非线性面板模型中时变反事实参数的识别AI 解读

We develop a general framework for the identification of counterfactual parameters in a class of nonlinear semiparametric panel models with fixed effects and time effects. Our method applies to models for discrete outcomes (e.g., two-way fixed effects binary choice) or continuous outcomes (e.g., censored regression), with discrete or continuous regressors. Our results do not require parametric assumptions on the error terms or time-homogeneity on the outcome equation. Our…

含固定效应的动态有序Logit模型AI 解读

Abstract We study a fixed-T panel data logit model for ordered outcomes that accommodates fixed effects and state dependence. We provide identification results for the autoregressive parameter, regression coefficients, and the threshold parameters in this model. Our results require only four observations on the outcome variable. We provide conditions under which a composite conditional maximum likelihood estimator is consistent and asymptotically normal. We use our estimator…

含因子面板模型中工具变量估计量的组合AI 解读

We address the estimation of factor-augmented panel data models using observed measurements to proxy for unobserved factors or loadings and explore the use of internal instruments to address the resulting endogeneity.The main challenge consists in that economic theory rarely provides insights into which measurements to choose as proxies when several are available.To overcome this problem, we propose a new class of estimators that are linear combinations of instrumental…

动态有序面板 Logit 模型AI 解读

This paper studies a dynamic ordered logit model for panel data with fixed effects. The main contribution of the paper is to construct a set of valid moment conditions that are free of the fixed effects. The moment functions can be computed using four or more periods of data, and the paper presents sufficient conditions for the moment conditions to identify the common parameters of the model, namely the regression coefficients, the autoregressive parameters, and the…

含固定效应的时变变换模型识别及家庭资源份额应用AI 解读

We provide new results showing identification of a large class of fixed-T panel models, where the response variable is an unknown, weakly monotone, time-varying transformation of a latent linear index of fixed effects, regressors, and an error term drawn from an unknown stationary distribution. Our results identify the transformation, the coefficient on regressors, and features of the distribution of the fixed effects. We then develop a full-commitment intertemporal…

不完整数据下的有效GMM估计AI 解读

In the standard missing data model, data are either complete or completely missing. However, applied researchers face situations with an arbitrary number of strata of incompleteness. Examples include unbalanced panels and instrumental variables settings where some observations are missing some instruments. I propose a model for settings where observations may be incomplete, with an arbitrary number of strata of incompleteness. I derive a set of moment conditions that…

具有固定效应的区间删失回归AI 解读

Summary This paper considers identification and estimation of a fixed‐effects model with an interval‐censored dependent variable. In each time period, the researcher observes the interval (with known endpoints) in which the dependent variable lies but not the value of the dependent variable itself. Two versions of the model are considered: a parametric model with logistic errors and a semiparametric model with errors having an unspecified distribution. In both cases, the…

随机经济—气候模型中的期望效用与灾难性风险AI 解读

We analyze a stochastic dynamic finite-horizon economic model with climate change, in which the social planner faces uncertainty about future climate change and its economic damages. Our model (SDICE*) incorporates, possibly heavy-tailed, stochasticity in Nordhaus’ deterministic DICE model. We develop a regression-based numerical method for solving a general class of dynamic finite-horizon economy–climate models with potentially heavy-tailed uncertainty and general utility…

This paper introduces a new estimator for the fixed-effects ordered logit model. The proposed method has two advantages over existing estimators. First, it estimates the differences in the cut points along with the regression coefficient, leading to provide bounds on partial effects. Second, the proposed estimator for the regression coefficient is more efficient. I use the fact that the ordered logit model with J outcomes and T observations can be converted to a binary…

处理效应半参数估计中的模型平均AI 解读

Choosing the covariates and functional form of the propensity score is an important choice for estimating treatment effects. This paper proposes a data-driven way of averaging the estimators over candidate specifications to resolve the specification uncertainty in the propensity score weighting estimation of the ATT. The proposed procedures minimize the estimated MSE of the ATT estimator in a local asymptotic framework. We formulate model averaging as a statistical decision…

面板数据模型的方差矩阵设定AI 解读

Many regression models have two dimensions, say time ( t = 1,…, T ) and households ( i = 1,…, N ), as in panel data, error components, or spatial econometrics. In estimating such models we need to specify the structure of the error variance matrix Ω , which is of dimension T N × T N . If T N is large, then direct computation of the determinant and inverse of Ω is not practical. In this note we define structures of Ω that allow the computation of its determinant and inverse,…