2,057
Views
65
CrossRef citations to date
0
Altmetric
Original Articles

Large Bayesian VARs: A Flexible Kronecker Error Covariance Structure

ORCID Icon
Pages 68-79 | Received 01 Feb 2016, Published online: 18 Jun 2018

References

  • Ahelegbey, D. F., Billio, M., and Casarin, R. (2016a), “Bayesian Graphical Models for Structural Vector Autoregressive Processes,” Journal of Applied Econometrics, 31, 357–386.
  • ——— (2016b), “Sparse Graphical Multivariate Autoregression: A Bayesian approach,” Annals of Economics and Statistics, 123/124, 1–30.
  • Banbura, M., Giannone, D., and Reichlin, L. (2010), “Large Bayesian Vector Auto Regressions,” Journal of Applied Econometrics, 25, 71–92.
  • Bauwens, L., Lubrano, M., and Richard, J. (1999), Bayesian Inference in Dynamic Econometric Models, New York: Oxford University Press.
  • Carriero, A., Clark, T. E., and Marcellino, M. (2015a), “Bayesian VARs: Specification Choices and Forecast Accuracy,” Journal of Applied Econometrics, 30, 46–73.
  • ——— (2015b), “Large Vector Autoregressions with asymmetric priors,” Working Paper, School of Economics and Finance, Queen Mary University of London.
  • ——— (2016), “Common Drifting Volatility in Large Bayesian VARs,” Journal of Business and Economic Statistics, 34, 375–390.
  • Carriero, A., Kapetanios, G., and Marcellino, M. (2009), “Forecasting Exchange Rates with a Large Bayesian VAR,” International Journal of Forecasting, 25, 400–417.
  • Carvalho, C. M., and West, M. (2007), “Dynamic Matrix-Variate Graphical Models,” Bayesian Analysis, 2, 69–98.
  • Chan, J. C. C. (2013), “Moving Average Stochastic Volatility Models with Application to Inflation Forecast,” Journal of Econometrics, 176, 162–172.
  • ——— (2017), “The Stochastic Volatility in Mean Model with Time-Varying Parameters: An Application to Inflation Modeling,” Journal of Business and Economic Statistics, 35, 17–28.
  • Chan, J. C. C., Eisenstat, E., and Koop, G. (2016), “Large Bayesian VARMAs,” Journal of Econometrics, 192, 374–390.
  • Chan, J. C. C., and Grant, A. L. (2015), “Pitfalls of Estimating the Marginal Likelihood Using the Modified Harmonic Mean,” Economics Letters, 131, 29–33.
  • Chib, S. (1995), “Marginal Likelihood from the Gibbs Output,” Journal of the American Statistical Association, 90, 1313–1321.
  • Chiu, C. J., Mumtaz, H., and Pinter, G. (2017), “Forecasting with VAR Models: Fat Tails and Stochastic Volatility,” International Journal of Forecasting, 33, 1124–1143.
  • Clark, T. E. (2011), “Real-time Density Forecasts from Bayesian Vector Autoregressions with Stochastic Volatility,” Journal of Business and Economic Statistics, 29, 327–341.
  • Clark, T. E., and Ravazzolo, F. (2015), “Macroeconomic Forecasting Performance Under Alternative Specifications of Time-Varying Volatility,” Journal of Applied Econometrics, 30, 551–575.
  • Cogley, T., and Sargent, T. J. (2005), “Drifts and Volatilities: Monetary Policies and Outcomes in the Post WWII US,” Review of Economic Dynamics, 8, 262–302.
  • Cross, J., and Poon, A. (2016), “Forecasting Structural Change and Fat-Tailed Events in Australian Macroeconomic Variables,” Economic Modelling, 58, 34–51.
  • Cúrdia, V., Del Negro, M., and Greenwald, D. L. (2014), “Rare Shocks, Great Recessions,” Journal of Applied Econometrics, 29, 1031–1052.
  • D’Agostino, A., Gambetti, L., and Giannone, D. (2013), “Macroeconomic Forecasting and Structural Change,” Journal of Applied Econometrics, 28, 82–101.
  • Dimitrakopoulos, S., and Kolossiatis, M. (2017), “Bayesian Analysis of Moving Average Stochastic Volatility Models: Modelling in Mean Effects and Leverage for Financial Time Series,” Tech. Rep., available at SSRN: https://ssrn.com/abstract=2966886.
  • Doan, T., Litterman, R., and Sims, C. (1984), “Forecasting and Conditional Projection using Realistic Prior Distributions,” Econometric Reviews, 3, 1–100.
  • Eltoft, T., Kim, T., and Lee, T. (2006a), “Multivariate Scale Mixture of Gaussians Modeling,” in Independent Component Analysis and Blind Signal Separation, eds. Rosca, J., Erdogmus, D., Principe, J., and Haykin, S., Berlin: Springer, vol. 3889 of Lecture Notes in Computer Science, pp. 799–806.
  • ——— (2006b), “On the Multivariate Laplace distribution,” Signal Processing Letters, IEEE, 13, 300–303.
  • Forni, M., Hallin, M., Lippi, M., and Reichlin, L. (2003), “Do Financial Variables help Forecasting Inflation and Real Activity in the Euro Area?” Journal of Monetary Economics, 50, 1243–1255.
  • Frühwirth-Schnatter, S., and Wagner, H. (2008), “Marginal Likelihoods for Non-Gaussian Models using Auxiliary Mixture Sampling,” Computational Statistics and Data Analysis, 52, 4608–4624.
  • Geweke, J. (1993), “Bayesian Treatment of the Independent Student-t Linear Model,” Journal of Applied Econometrics, 8, S19–S40.
  • Geweke, J., and Amisano, G. (2011), “Hierarchical Markov Normal Mixture Models with Applications to Financial Asset Returns,” Journal of Applied Econometrics, 26, 1–29.
  • Golub, G. H., and van Loan, C. F. (1983), Matrix Computations, Baltimore, MD: Johns Hopkins University Press.
  • Kadiyala, K., and Karlsson, S. (1997), “Numerical Methods for Estimation and Inference in Bayesian VAR-models,” Journal of Applied Econometrics, 12, 99–132.
  • Karlsson, S. (2013), “Forecasting with Bayesian Vector Autoregressions,” in Handbook of Economic Forecasting, eds. Elliott, G. and Timmermann, A., Elsevier, vol. 2 of Handbook of Economic Forecasting, pp. 791–897.
  • Kim, S., Shephard, N., and Chib, S. (1998), “Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models,” Review of Economic Studies, 65, 361–393.
  • Koop, G. (2003), Bayesian Econometrics, New York: Wiley.
  • ——— (2013), “Forecasting with medium and large Bayesian VARs,” Journal of Applied Econometrics, 28, 177–203.
  • Koop, G., and Korobilis, D. (2010), “Bayesian Multivariate Time Series Methods for Empirical Macroeconomics,” Foundations and Trends in Econometrics, 3, 267–358.
  • ——— (2013), “Large Time-Varying Parameter VARs,” Journal of Econometrics, 177, 185–198.
  • Koop, G., Korobilis, D., and Pettenuzzo, D. (2017), “Bayesian Compressed Vector Autoregression,” Journal of Econometrics, forthcoming.
  • Li, Y., Zeng, T., and Yu, J. (2012), “Robust Deviance Information Criterion for Latent Variable Models,” SMU Economics and Statistics Working Paper Series.
  • Litterman, R. (1986), “Forecasting With Bayesian Vector Autoregressions—Five Years of Experience,” Journal of Business and Economic Statistics, 4, 25–38.
  • Mumtaz, H. (2016), “The Evolving Transmission of Uncertainty Shocks in the United Kingdom,” Econometrics, 4, 16.
  • Mumtaz, H., and Theodoridis, K. (2018), “The Changing Transmission of Uncertainty Shocks in the U.S.,” Journal of Business and Economic Statistics, 36, 239–252.
  • Nonejad, N. (2015), “Particle Gibbs with Ancestor Sampling for Stochastic Volatility Models with: Heavy Tails, in mean Effects, Leverage, Serial Dependence and Structural Breaks,” Studies in Nonlinear Dynamics & Econometrics, 19, 561–584.
  • Primiceri, G. E. (2005), “Time Varying Structural Vector Autoregressions and Monetary Policy,” Review of Economic Studies, 72, 821–852.
  • Stock, J. H., and Watson, M. W. (2002), “Macroeconomic Forecasting Using Diffusion Indexes,” Journal of Business and Economic Statistics, 20, 147–162.
  • Wang, H., Reeson, C., and Carvalho, C. M. (2011), “Dynamic Financial Index Models: Modeling Conditional Dependencies via Graphs,” Bayesian Analysis, 6, 639–664.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.