Discussion of “Bayesian forecasting of multivariate time series: scalability, structure uncertainty and decisions”

  • 35 Accesses


The author focuses on the “decoupling and recoupling” idea that can critically increase both computational and forecasting efficiencies in practical problems for economic and financial data. My discussion is twofold. First, I briefly describe the idea with an example of time-varying vector autoregressions, which are widely used in the context. Second, I highlight the issue of how to assess patterns of simultaneous relationships.

This is a preview of subscription content, log in to check access.

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 99

This is the net price. Taxes to be calculated in checkout.


  1. Christiano, L. J., Eichenbaum, M., Evans, C. L. (1999). Monetary policy shocks: What have we learned and to what end? In J. B. Taylor & M. Woodford (Eds.), Handbook of macroeconomics, Vol. 1A, pp. 65–148. Amsterdam: Elsevier Science.

  2. Diebold, F. X., Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. Economic Journal, 119, 158–171.

  3. Geraci, M. V., Gnabo, J. Y. (2018). Measuring interconnectedness between financial institutions with Bayesian time-varying vector autoregressions. Journal of Financial and Quantitative Analysis, 53, 1371–1390.

  4. Nakajima, J. (2011). Time-varying parameter VAR model with stochastic volatility: An overview of methodology and empirical applications. Monetary and Economic Studies, 29, 107–142.

  5. Nakajima, J., West, M. (2013). Bayesian analysis of latent threshold dynamic models. Journal of Business and Economic Statistics, 31, 151–164.

  6. Nakajima, J., West, M. (2015). Dynamic network signal processing using latent threshold models. Digital Signal Processing, 47, 5–16.

  7. Primiceri, G. E. (2005). Time varying structural vector autoregressions and monetary policy. Review of Economic Studies, 72, 821–852.

  8. Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48, 1–48.

  9. West, M., Harrison, P. J. (1997). Bayesian forecasting and dynamic models, 2nd edn. New York: Springer.

Download references

Author information

Correspondence to Jouchi Nakajima.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The views expressed herein are those of the author alone and do not necessarily reflect those of the Bank of Japan.

The Related Articles are;;

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Nakajima, J. Discussion of “Bayesian forecasting of multivariate time series: scalability, structure uncertainty and decisions”. Ann Inst Stat Math 72, 33–36 (2020) doi:10.1007/s10463-019-00742-2

Download citation


  • Bayesian forecasting
  • Decouple/recouple
  • Time-varying vector autoregressions
  • Multivariate time-series models