Dynamic stochastic general equilibrium inference using a score-driven approach

In this paper, the benefits of statistical inference of score-driven state-spacemodels are incorporated into the inference of dynamic stochastic general equilibrium (DSGE)models. We focus on DSGE models, for which a Gaussian ABCD representation exists. Precisionof statistical estimation is improved, by using a score-driven multivariate t-distribution for theerrors. First, the updating term of the transition equation of the ABCD representation isreplaced by the conditional score of the log-likelihood (LL) with respect to location. Second,the time-constant scale parameters of the error terms in the measurement equation of the ABCDrepresentation are replaced by a dynamic parameter that is updated by the conditional score ofthe LL with respect to scale. Impulse response functions (IRFs) and conditions of the maximumlikelihood (ML) estimator are presented. In the empirical application, a benchmark DSGE modelis estimated for real data on US economic output, inflation and interest rate for the period of1954-2019. The score-driven ABCD representation improves the estimation precision of theGaussian ABCD representation. The score-driven ABCD representation with dynamic scaleprovides the best description of the time series data, by identifying a structural change in thesample period and providing the most precise IRF estimates.