Identification and Estimation of Dichotomous Latent Variables Models Using Panel Data
نویسندگان
چکیده
منابع مشابه
Estimation of Count Data using Bivariate Negative Binomial Regression Models
Abstract Negative binomial regression model (NBR) is a popular approach for modeling overdispersed count data with covariates. Several parameterizations have been performed for NBR, and the two well-known models, negative binomial-1 regression model (NBR-1) and negative binomial-2 regression model (NBR-2), have been applied. Another parameterization of NBR is negative binomial-P regression mode...
متن کاملEstimation of Linear Panel Data Models Using Gmm
In this chapter we study GMM estimation of linear panel data models. Several different types of models are considered, including the linear regression model with strictly or weakly exogenous regressors, the simultaneous regression model, and a dynamic linear model containing a lagged dependent variable as a regressor. In each case, different assumptions about the exogeneity of the explanatory v...
متن کاملTwo-step estimation of panel data models with censored endogenous variables and selection bias
This paper presents some two-step estimators for a wide range of parametric panel data models with censored endogenous variables and sample selection bias. Our approach is to derive estimates of the unobserved heterogeneity responsible for the endogeneity/selection bias to include as additional explanatory variables in the primary equation. These are obtained through a decomposition of the redu...
متن کاملIdentification and Estimation of Marginal Effects in Nonlinear Panel Models
This paper gives identification and estimation results for marginal effects in nonlinear panel models. We find that linear fixed effects estimators are not consistent, due in part to marginal effects not being identified. We derive bounds for marginal effects and show that they can tighten rapidly as the number of time series observations grows. We also show in numerical calculations that the b...
متن کاملPoor identification and estimation problems in panel data models with random effects and autocorrelated errors
The paper shows that poor identifiability of parameters can arise in the context of linear panel data model with random effects and autocorrelated disturbances. This causes problems when estimating the model by (Gaussian) maximum likelihood. Corner solutions occur quite frequently for the variance of the random effects, with a consequent bimodal distribution of the other variance and of the aut...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Review of Economic Studies
سال: 1991
ISSN: 0034-6527
DOI: 10.2307/2297829