DSGE-SVt: An Econometric Toolkit for High-Dimensional DSGE Models with SV and t Errors

Presently there is growing interest in dynamic stochastic general equilibrium (DSGE) models with more parameters, endogenous variables, exogenous shocks, and observable variables than the Smets and Wouters (Am Econ Rev 97(3):586–606, 2007) model, and the incorporation of non-Gaussian distribution and time-varying volatility. A primary goal of this paper is to introduce a user-friendly MATLAB toolkit designed to reliably estimate such high-dimensional models. It simulates the posterior distribution by the tailored random block Metropolis-Hastings (TaRB-MH) algorithm of Chib and Ramamurthy (J Econom 155(1):19–38, 2010), calculates the marginal likelihood by the method of Chib (J Am Stat Assoc 90:1313–1312, 1995) and Chib and Jeliazkov (J Am Stat Assoc 96(453):270–281, 2001), and includes various post-estimation tools that are important for policy analysis, for example, functions for generating point and density forecasts. We also introduce two novel features, i.e., tailoring-at-random-frequency and parallel computing, to boost the overall computational efficiency. Another goal is to provide pointers on the prior, estimation, and comparison of these DSGE models. To demonstrate the performance of our toolkit, we apply it to estimate an extended version of the new Keynesian model of Leeper et al (Am Econom Rev 107(8):2409–2454, 2017) that has 51 parameters, 21 endogenous variables, 8 exogenous shocks, 8 observable variables, and 1494 non-Gaussian and nonlinear latent variables.