Multiple fixed effects in binary response panel data models

Estimation of Structural Parameters and Marginal Effects in Binary Choice Panel Data Models with Fixed Effects

Fixed effects estimates of structural parameters in nonlinear panel models can be severely biased due to the incidental parameters problem. In this paper I show that the most important component of this incidental parameters bias for probit fixed effects estimators of index coefficients is proportional to the true parameter value, using a large-T expansion of the bias. This result allows me to ...

Testing for Serial Correlation in Fixed-Effects Panel Data Models

In this paper, we propose three new tests for serial correlation in the disturbances of fixed-effects panel data models. First, a modified Bhargava, Franzini and Narendranathan (1982) panel Durbin-Watson statistic that does not need to be tabulated as it follows a standard normal distribution. Second, a modified Baltagi and Li (1991) LM statistic with limit distribution independent of T , and, ...

Binary Response Correlated Random Coefficient Panel Data Models

In this paper, we consider binary response correlated random coefficient (CRC) panel data models which are frequently used in the analysis of treatment effects and demand of products. We focus on the nonparametric identification and estimation of panel data models under unobserved heterogeneity which is captured by random coefficients and when these random coefficients are correlated with regre...

Binary Response Models for Panel Data: Identification and Information

Estimation of spatial autoregressive panel data models with fixed effects

This paper establishes asymptotic properties of quasi-maximum likelihood estimators for SAR panel data models with fixed effects and SAR disturbances. A direct approach is to estimate all the parameters including the fixed effects. Because of the incidental parameter problem, some parameter estimatorsmay be inconsistent or their distributions are not properly centered. We propose an alternative...