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Accelerated Gaussian Importance Sampler with application to dynamic latent variable models
Author(s) -
Danielsson J.,
Richard J.F.
Publication year - 1993
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.3950080510
Subject(s) - latent variable , econometrics , stochastic volatility , gaussian , autoregressive conditional heteroskedasticity , volatility (finance) , monte carlo method , computer science , variable (mathematics) , mathematics , statistics , mathematical analysis , physics , quantum mechanics
We propose a new generic and highly efficient Accelerated Gaussian Importance Sampler (AGIS) for the numerical evaluation of (very) high‐dimensional density functions. A specific case of interest to us is the evaluation of likelihood functions for a broad class of dynamic latent variable models. The feasibility of our method is strikingly illustrated by means of an application to a first‐order dynamic stochastic volatility model for daily stock returns, whose likelihood for an actual sample of size 2022 (!) is evaluated with high numerical accuracy by means of 10,000 Monte Carlo replications. The estimated model parsimoniously dominates ARCH and GARCH alternatives, one of which includes twelve lags.