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Reducing Dimensions in a Large TVP-VAR

Author

Listed:
  • Eric Eisenstat

    (University of Queensland, Australia)

  • Joshua C.C. Chan

    (University of Technology Sydney, Australia)

  • Rodney W. Strachan

    () (School of Economics, University of Queensland, Australia; Rimini Centre for Economic Analysis; Centre for Applied Macroeconomic Analysis)

Abstract

This paper proposes a new approach to estimating high dimensional time varying parameter structural vector autoregressive models (TVP-SVARs) by taking advantage of an empirical feature of TVP-(S)VARs. TVP-(S)VAR models are rarely used with more than 4-5 variables. However recent work has shown the advantages of modelling VARs with large numbers of variables and interest has naturally increased in modelling large dimensional TVP-VARs. A feature that has not yet been utilized is that the covariance matrix for the state equation, when estimated freely, is often near singular. We propose a specification that uses this singularity to develop a factor-like structure to estimate a TVP-SVAR for 15 variables. Using a generalization of the recentering approach, a rank reduced state covariance matrix and judicious parameter expansions, we obtain efficient and simple computation of a high dimensional TVP-SVAR. An advantage of our approach is that we retain a formal inferential framework such that we can propose formal inference on impulse responses, variance decompositions and, important for our model, the rank of the state equation covariance matrix. We show clear empirical evidence in favour of our model and improvements in estimates of impulse responses.

Suggested Citation

  • Eric Eisenstat & Joshua C.C. Chan & Rodney W. Strachan, 2018. "Reducing Dimensions in a Large TVP-VAR," Working Paper series 18-37, Rimini Centre for Economic Analysis.
  • Handle: RePEc:rim:rimwps:18-37
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Large VAR; time varying parameter; reduced rank covariance matrix;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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