Munich Personal RePEc Archive

Adaptive quadrature for likelihood inference on dynamic latent variable models for time-series and panel data

Cagnone, Silvia and Bartolucci, Francesco (2013): Adaptive quadrature for likelihood inference on dynamic latent variable models for time-series and panel data.

[img]
Preview
PDF
MPRA_paper_51037.pdf

Download (289kB) | Preview

Abstract

Maximum likelihood estimation of dynamic latent variable models requires to solve integrals that are not analytically tractable. Numerical approximations represent a possible solution to this problem. We propose to use the Adaptive Gaussian-Hermite (AGH) numerical quadrature approximation for a class of dynamic latent variable models for time-series and panel data. These models are based on continuous time-varying latent variables which follow an autoregressive process of order 1, AR(1). Two examples of such models are the stochastic volatility models for the analysis of financial time-series and the limited dependent variable models for the analysis of panel data. A comparison between the performance of AGH methods and alternative approximation methods proposed in the literature is carried out by simulation. Examples on real data are also used to illustrate the proposed approach.

UB_LMU-Logo
MPRA is a RePEc service hosted by
the Munich University Library in Germany.