Maximum likelihood estimation for vector autoregressions with multivariate stochastic volatility

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

This paper analyzes the maximum likelihood estimation for vector autoregressions with stochastic volatility. The stochastic volatility is modeled following Uhlig (1997). The asymptotic distribution of the maximum likelihood estimate is discussed under mild regularity conditions. The maximum likelihood estimate can be obtained via an iterative method. In that case, the maximum likelihood estimate becomes the iteratively reweighted least squares estimate analyzed in Rubin (1983). The iteratively reweighted least squares estimate is computationally much simpler than the Bayesian method offered by Uhlig (1997).

Original languageEnglish
Pages (from-to)282-286
Number of pages5
JournalEconomics Letters
Volume123
Issue number3
DOIs
Publication statusPublished - 2014 Jan 1

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Vector autoregression
Maximum likelihood estimation
Multivariate stochastic volatility
Maximum likelihood
Least squares
Stochastic volatility
Regularity
Bayesian methods
Asymptotic distribution

Keywords

  • Heteroskedasticity
  • Iteratively reweighted least squares
  • Local scale

ASJC Scopus subject areas

  • Economics and Econometrics
  • Finance

Cite this

Maximum likelihood estimation for vector autoregressions with multivariate stochastic volatility. / Kim, Dukpa.

In: Economics Letters, Vol. 123, No. 3, 01.01.2014, p. 282-286.

Research output: Contribution to journalArticle

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