compute_dic: Deviance Information Criterion of Multivariate Time Series...

In bvhar: Bayesian Vector Heterogeneous Autoregressive Modeling

Deviance Information Criterion of Multivariate Time Series Model

Description

Compute DIC of BVAR and BVHAR.

Usage

compute_dic(object, ...)

## S3 method for class 'bvarmn'
compute_dic(object, n_iter = 100L, ...)

Arguments

object	Model fit
...	not used
n_iter	Number to sample

Details

Deviance information criteria (DIC) is

- 2 \log p(y \mid \hat\theta_{bayes}) + 2 p_{DIC}

where p_{DIC} is the effective number of parameters defined by

p_{DIC} = 2 ( \log p(y \mid \hat\theta_{bayes}) - E_{post} \log p(y \mid \theta) )

Random sampling from posterior distribution gives its computation, \theta_i \sim \theta \mid y, i = 1, \ldots, M

p_{DIC}^{computed} = 2 ( \log p(y \mid \hat\theta_{bayes}) - \frac{1}{M} \sum_i \log p(y \mid \theta_i) )

Value

DIC value.

References

Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2013). Bayesian data analysis. Chapman and Hall/CRC.

Spiegelhalter, D.J., Best, N.G., Carlin, B.P. and Van Der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64: 583-639.

bvhar documentation built on April 4, 2025, 5:22 a.m.