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Research Seminar Series in Statistics and Mathematics

Wirtschaftsuniversität Wien, Departments 4 D4.4.00812:30 - 14:00

Type Lecture / discussion
LanguageEnglish
SpeakerEric Eisenstat (School of Economics, The University of Queensland, Brisbane, Australia)
Organizer Institut für Statistik und Mathematik
Contact katrin.artner@wu.ac.at

Eric Eisenstat (School of Economics, The University of Queensland, Brisbane, Australia) about “Efficient Estimation of Structural VARMAs with Stochastic Volatility”

The Institute for Statistics and Mathematics (Department of Finance, Accounting and Statistics) cordially invites everyone interested to attend the talks in our Research Seminar Series, where internationally renowned scholars from leading universities present and discuss their (working) papers.

The list of talks for the summer term 2018 is available via the following link:
Summer Term 2018

Abstract:

This paper develops Markov chain Monte Carlo algorithms for structural vector autoregressive moving average (VARMA) models with fix coefficients and time-varying error covariances, modeled as a multivariate stochastic volatility process. A particular benefit of allowing for time variation in the covariances in this setting is that it induces uniqueness in terms of fundamental and various non-fundamental VARMA representations. Hence, it resolves an important issue in applying multivariate time series models to structural macroeconomic problems. Although computation in this setting is more challenging, the conditionally Gaussian nature of the model renders efficient sampling algorithms feasible. The algorithm presented in this paper uses two innovative approaches to achieve sampling efficiency: (i) the time-varying covariances are sampled jointly using particle Gibbs with ancestry sampling, and (ii) the moving average coefficients are sampled jointly using an extension of the Whittle likelihood approximation. We provide Monte Carlo evidence that the algorithm performs well in practice. We further employ the algorithm to assess the extent to which commonly used SVAR models satisfy their underlying fundamentalness assumption and the effect that this assumption has on structural inference.



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