Transformations and moment conditions for dynamic fixed effects logit models
动态固定效应 logit 模型的变换与矩条件AI 解读 ↓
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动态固定效应 logit 模型的变换与矩条件AI 解读 ↓
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This study proposes transformations for dynamic fixed effects logit models. First, these transformations construct valid moment conditions for the case with neither explanatory variables nor time dummies. Using valid moment conditions, we can obtain the first-order condition of the conditional maximum likelihood estimator proposed by Chamberlain (1985). Next, we derive valid moment conditions based on the transformations for the cases with strictly exogenous continuous explanatory variables and/or time dummies when the number of time periods is four or more. Transformations of these moment conditions exactly coincide with a subset of those obtained by Honoré and Weidner (2020) using the functional differencing approach proposed by Bonhomme (2012).
面板 Probit 中的异方差误设:GMM 与模拟最大似然的小样本比较
This paper compares generalized method of moments (GMM) and simulated maximum likelihood (SML) approaches to the estimation of the panel probit model. Both techniques circumvent multiple integration of joint density functions without the need to restrict the error term variance-covariance matrix of the latent normal regression model. Particular attention is paid to a three-stage GMM estimator based on nonparametric estimation of the optimal instruments for given conditional…
动态面板多项Logit模型
This paper provides moment conditions for dynamic multinomial logit panel data models with individual specific fixed effects. The moment conditions do not depend on the fixed effects and thus form the basis for generalized method of moments estimation. For a leading case, we provide sufficient conditions under which the moment conditions identify the common parameters, and we illustrate their use in an empirical application to household soft drink purchases. The construction…
通过正则化最优传输推断部分识别矩模型
Many statistical and econometric problems involve parameters defined by moments of a joint distribution when only marginal distributions are observed, leading naturally to partial identification. We develop a methodology for identification, estimation, and inference in the corresponding partially identified GMM model. We characterize the sharp identified set for the parameter of interest via a support-function/optimal-transport (OT) representation. To estimate the identified…
含交互固定效应的高维离散选择模型及其因果推断应用
ABSTRACT We propose a two‐step procedure to estimate a high dimensional discrete choice panel with interactive fixed effects where the initial and final estimators are obtained via a nuclear‐norm regularized (NNR) maximum likelihood estimation and post‐NNR iterated estimation, respectively. We apply the method to make counterfactual predictions of choice probabilities. Simulations demonstrate nice finite sample performance in estimation and tests. An illustrative application…
固定效应动态面板 Logit 模型的根号 n 一致条件极大似然估计
In this paper we first propose a root-n-consistent Conditional Maximum Likelihood (CML) estimator for all the common parameters in the panel logit AR(p) model with strictly exogenous covariates and fixed effects. Our CML estimator (CMLE) converges in probability faster and is more easily computed than the kernel-weighted CMLE of Honoré and Kyriazidou (2000). Next, we propose a root-n-consistent CMLE for the coefficients of the exogenous covariates only. We also discuss new…
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…