YADA Manual — Computational Details

evaluation of Dynamic Stochastic General Equilibrium and vector autoregressive models. This pa-per provides the mathematical details for the various functions used by the software. First, some rather famous examples of DSGE models are presented and all these models are included as ex-amples in the YADA distribution. YADA supports a number of different algorithms for solving log-linearized DSGE models. The fastest algorithm is the so called Anderson-Moore algorithm (AiM), but the approaches of Klein and Sims are also covered and have the benefit of being nu-merically more robust in certain situations. The AiM parser is used to translate the DSGE model equations into a structural form that the solution algorithms can make use of. The solution of the DSGE model is expressed as a VAR(1) system that represents the state equations of the state-space representation. Thereafter, the different prior distributions that are supported, the state-space representation and the Kalman filter used to evaluate the log-likelihood are presented. Furthermore, it discusses how the posterior mode is computed, including how the original model parameters can be transformed internally to facilitate the posterior mode estimation. Next, the paper provides some details on the algorithms used for sampling from the posterior distribution: the random walk Metropolis and slice sampling algorithms. In order to conduct inference base