Bayesian inference with tmbstan for a state-space model with VAR(1) state equation

TL;DR

This study evaluates the impact of using Laplace approximation in tmbstan for Bayesian inference on a VAR(1) state-space model, revealing it may reduce efficiency unless ample data is available, where it can improve accuracy and efficiency.

Contribution

The paper provides practical guidelines on when to use Laplace approximation in tmbstan for complex state-space models based on simulation results.

Findings

Laplace approximation may lower computational efficiency in tmbstan.

More data points improve estimation accuracy for transition and scale parameters.

Increasing sample size at each time point does not enhance parameter estimation.

Abstract

When using R package tmbstan for Bayesian inference, the built-in feature Laplace approximation to the marginal likelihood with random effects integrated out can be switched on and off. There exists no guideline on whether Laplace approximation should be used to achieve better efficiency especially when the statistical model for estimating selection is complicated. To answer this question, we conducted simulation studies under different scenarios with a state-space model employing a VAR(1) state equation. We found that turning on Laplace approximation in tmbstan would probably lower the computational efficiency, and only when there is a good amount of data, both tmbstan with and without Laplace approximation are worth trying since in this case, Laplace approximation is more likely to be accurate and may also lead to slightly higher computational efficiency. The transition parameters and scale parameters in a VAR(1) process are hard to be estimated accurately and increasing the sample size at each time point do not help in the estimation, only more time points in the data contain more information on these parameters and make the likelihood dominate the posterior likelihood, thus lead to accurate estimates for them.