Nonlinear State-Space Models for Microeconometric Panel Data

Heiss, Florian (2006): Nonlinear State-Space Models for Microeconometric Panel Data. Discussion Papers in Economics 2006-24

Abstract

In applied microeconometric panel data analyses, time-constant random effects and first-order Markov chains are the most prevalent structures to account for intertemporal correlations in limited dependent variable models. An example from health economics shows that the addition of a simple autoregressive error terms leads to a more plausible and parsimonious model which also captures the dynamic features better. The computational problems encountered in the estimation of such models - and a broader class formulated in the framework of nonlinear state space models - hampers their widespread use. This paper discusses the application of different nonlinear filtering approaches developed in the time-series literature to these models and suggests that a straightforward algorithm based on sequential Gaussian quadrature can be expected to perform well in this setting. This conjecture is impressively confirmed by an extensive analysis of the example application.

• Item Type: Paper (Discussion Paper)
• Keywords: LDV models, panel data, state space, numerical integration, health
• Collections: Economics; Economics > Discussion Papers in Economics; Economics > Discussion Papers in Economics > Statistical Methods
• Subjects: 300 Social sciences > 300 Social sciences, sociology and anthropology; 300 Social sciences > 330 Economics
• JEL Classification: C15, C23, C35, I10
• Language: English