Working Paper: CEPR ID: DP11032
Authors: Jess Fernández-Villaverde; Juan Francisco Rubio-RamÃrez; Frank Schorfheide
Abstract: This paper provides an overview of solution and estimation techniques for dynamic stochastic general equilibrium (DSGE) models. We cover the foundations of numerical approximation techniques as well as statistical inference and survey the latest developments in the field.
Keywords: approximation; error analysis; bayesian inference; dsge model; frequentist inference; gmm estimation; impulse response function; matching; likelihood-based inference; metropolis-hastings algorithm; minimum distance estimation; particle filter; perturbation methods; projection methods; sequential monte carlo
JEL Codes: C11; C13; C32; C52; C61; C63; E32; E52
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| DSGE models structure (E13) | macroeconomic outcomes (E66) |
| preferences, technologies, and policies (O33) | economic behavior (D22) |
| estimation methods (C13) | outcomes predicted by models (C52) |
| lack of familiarity with numerical methods (C60) | misinterpretations of causal relationships (C32) |