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Composite likelihood methods for large Bayesian VARs with stochastic volatility
Author(s) -
Chan Joshua C. C.,
Eisenstat Eric,
Hou Chenghan,
Koop Gary
Publication year - 2020
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.2793
Subject(s) - stochastic volatility , econometrics , quasi maximum likelihood , volatility (finance) , multivariate statistics , bayesian probability , heteroscedasticity , vector autoregression , marginal likelihood , mathematics , computer science , maximum likelihood , statistics , likelihood function
Summary Adding multivariate stochastic volatility of a flexible form to large vector autoregressions (VARs) involving over 100 variables has proved challenging owing to computational considerations and overparametrization concerns. The existing literature works with either homoskedastic models or smaller models with restrictive forms for the stochastic volatility. In this paper, we develop composite likelihood methods for large VARs with multivariate stochastic volatility. These involve estimating large numbers of parsimonious models and then taking a weighted average across these models. We discuss various schemes for choosing the weights. In our empirical work involving VARs of up to 196 variables, we show that composite likelihood methods forecast much better than the most popular large VAR approach, which is computationally practical in very high dimensions: the homoskedastic VAR with Minnesota prior. We also compare our methods to various popular approaches that allow for stochastic volatility using medium and small VARs involving up to 20 variables. We find our methods to forecast appreciably better than these as well.