Noninformative priors and frequentist risks of bayesian estimators of vector-autoregressive models

By Shawn Ni, Dongchu Sun. 2003

58 citations recorded by the source corpus.

In this study, we examine posterior properties and frequentist risks of Bayesian estimators based on several noninformative priors in vector autoregressive (VAR) models. We prove existence of the posterior distributions and posterior moments under a general class of priors. Using a variety of priors in this class we conduct numerical simulations of posteriors. We find that in most examples Bayesian estimators with a shrinkage prior on the VAR coefficients and the reference prior of Yang and Berger (Ann. Statist. 22 (1994) 1195) on the VAR covariance matrix dominate MLE, Bayesian estimators with the diffuse prior, and Bayesian estimators with the prior used in RATS. We also examine the informative Minnesota prior and find that its performance depends on the nature of the data sample and on the tightness of the Minnesota prior. A tightly set Minnesota prior is better when the data generating processes are similar to random walks, but the shrinkage prior or constant prior can be better otherwise.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/fb3dcd5f51518c4f54f34a65679615c8db712af9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Dongchu Sun
2. Shawn Ni
3. Lutz Kilian
4. Mark F. J. Steel
5. Darren J. Wilkinson

In Statistics and Probability

1. Dongchu Sun
2. Shawn Ni
3. Jan Hannig
4. Zoltan Dienes

Citations in the corpus

• Prior Selection for Vector Autoregressions (2015, unknown): “…are Lopes et al. (1999), who use a hierarchical approach to estimate a small-scale VAR of the Brazilian economy with a Minnesota prior, and Ni and Sun (2003), who exploit an appealing but restrictive hierarchical structure where the hyperparameter controlling the variance of the prior can…”
• Bayesian Estimates for Vector Autoregressive Models (2005, background): “Ni and Sun (2003) offered proofs for some of the foregoing facts as well as detailed algorithms of MCMC procedures.”