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Large Bayesian VARMAs

Author

Listed:
  • Joshua Chan

    () (Research School of Economics, Australian National University, Australia)

  • Eric Eisenstat

    () (Faculty of Business Administration, University of Bucharest, Romania)

  • Gary Koop

    () (Department of Economics, University of Strathclyde, UK; The Rimini Centre for Economic Analysis, Italy)

Abstract

Vector Autoregressive Moving Average (VARMA) models have many theoretical properties which should make them popular among empirical macroeconomists. However, they are rarely used in practice due to over-parameterization concerns, difficulties in ensuring identification and computational challenges. With the growing interest in multivariate time series models of high dimension, these problems with VARMAs become even more acute, accounting for the dominance of VARs in this field. In this paper, we develop a Bayesian approach for inference in VARMAs which surmounts these problems. It jointly ensures identification and parsimony in the context of an efficient Markov Chain Monte Carlo (MCMC) algorithm. We use this approach in a macroeconomic application involving up to twelve dependent variables. We find our algorithm t work successfully and provide insights beyond those provided by VARs.

Suggested Citation

  • Joshua Chan & Eric Eisenstat & Gary Koop, 2015. "Large Bayesian VARMAs," Working Paper series 15-36, Rimini Centre for Economic Analysis.
  • Handle: RePEc:rim:rimwps:15-36
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    References listed on IDEAS

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    Cited by:

    1. Silvia Miranda-Agrippino & Giovanni Ricco, 2015. "The Transmission of Monetary Policy Shocks," Discussion Papers 1711, Centre for Macroeconomics (CFM), revised Feb 2017.
    2. Joshua C.C. Chan & Eric Eisenstat, 2015. "Efficient estimation of Bayesian VARMAs with time-varying coefficients," CAMA Working Papers 2015-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    3. Joshua C.C. Chan, 2015. "Large Bayesian VARs: A flexible Kronecker error covariance structure," CAMA Working Papers 2015-41, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    4. repec:eee:econom:v:202:y:2018:i:1:p:75-91 is not listed on IDEAS

    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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