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

Silvia Cagnone and Francesco Bartolucci

MPRA Paper from University Library of Munich, Germany

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.

Keywords: AR(1); categorical longitudinal data; Gaussian-Hermite quadrature; limited dependent variable models; stochastic volatility model (search for similar items in EconPapers)
JEL-codes: C13 C32 C33 (search for similar items in EconPapers)
Date: 2013-10-29
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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