Dynamic Ordered Panel Logit Models

TL;DR

This paper develops a novel method for estimating dynamic ordered logit models with fixed effects in panel data, enabling consistent estimation of key parameters using moment conditions and GMM, demonstrated through simulations and real data.

Contribution

It introduces fixed-effects free moment conditions for dynamic ordered logit models, allowing consistent estimation of parameters with multiple data periods.

Findings

Moment conditions are valid with four or more periods.

GMM estimator performs well in simulations.

Empirical application to health data illustrates practical usefulness.

Abstract

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 threshold parameters. The availability of moment conditions suggests that these common parameters can be estimated using the generalized method of moments, and the paper documents the performance of this estimator using Monte Carlo simulations and an empirical illustration to self-reported health status using the British Household Panel Survey.