List

Bin Jiang, Anastasios Panagiotelis, George Athanasopoulos, Rob J Hyndman, and Farshid Vahid.
Department of Econometrics & Business Statistics, Monash University, Clayton VIC 3800, Australia

 

Abstract
Estimating the rank of the coefficient matrix is a major challenge in multivariate regression, including vector autoregression (VAR). In this paper, we develop a novel fully Bayesian approach that allows for rank estimation. The key to our approach is reparameterizing the coefficient matrix using its singular value decomposition and conducting Bayesian inference on the decomposed parameters. By implementing a stochastic search variable selection on the singular values of the coefficient matrix, the ultimate selected rank can be identified as the number of nonzero singular values. Our approach is appropriate for small multivariate regressions as well as for higher dimensional models with up to about 40 predictors. In macroeconomic forecasting using VARs, the advantages of shrinkage through proper Bayesian priors is well documented. Consquently, the shrinkage approach proposed here that selects or average over low rank coefficient matrices is evaluated in a forecasting environment. We show in both simulations and empirical studies that our Bayesian approach provides forecasts that are highly competitive compared to two of most promising benchmarks methods, dynamic factor models and factor augmented VARs.

Download working paper

  Tag: econometrics

posts
January 30th, 2016

Bayesian rank selection in multivariate regressions

Bin Jiang, Anastasios Panagiotelis, George Athanasopoulos, Rob J Hyndman, and Farshid Vahid. Department of Econometrics & Business Statistics, Monash University, […]

January 24th, 2016

Long-term forecasts of age-specific participation rates with functional data models

Thomas Url1, Rob J Hyndman2, Alexander Dokumentov2 Vienna University of Economics and Business, Vienna, Austria Monash Business School, Monash University, […]

March 31st, 2011

The price elasticity of electricity demand in South Australia

Shu Fan and Rob J Hyndman Business and Economic Forecasting Unit, Monash University, Clayton, Victoria 3800, Australia Energy policy (2011), […]

January 1st, 2009

A multivariate innovations state space Beveridge-Nelson decomposition

Economic modelling (2009), 26(5), 1067-1074 Ashton de Silva, Rob J Hyndman and Ralph Snyder Abstract The Beveridge-Nelson vector innovations structural […]

March 16th, 2002

Using R to Teach Econometrics

Journal of Applied Econometrics (2002), 17(2), 149-174. Jeff Racine1 and Rob J Hyndman2 Department of Economics, University of South Florida, […]

July 16th, 2000

Pagan and Ullah. Nonparametric econometrics

January 16th, 1997

The pricing and trading of options using a hybrid neural network model with historical volatility

NeuroVe$t Journal (1997), 5(1), 27-41. (Later known as Journal of Computational Intelligence in Finance) Paul Lajbcygier, Andrew Flitman, Anthony Swan […]