Working Paper: NBER ID: w12772
Authors: Jean Boivin; Marc Giannoni
Abstract: Standard practice for the estimation of dynamic stochastic general equilibrium (DSGE) models maintains the assumption that economic variables are properly measured by a single indicator, and that all relevant information for the estimation is summarized by a small number of data series. However, recent empirical research on factor models has shown that information contained in large data sets is relevant for the evolution of important macroeconomic series. This suggests that conventional model estimates and inference based on estimated DSGE models might be distorted. In this paper, we propose an empirical framework for the estimation of DSGE models that exploits the relevant information from a data-rich environment. This framework provides an interpretation of all information contained in a large data set, and in particular of the latent factors, through the lenses of a DSGE model. The estimation involves Markov-Chain Monte-Carlo (MCMC) methods. We apply this estimation approach to a state-of-the-art DSGE monetary model. We find evidence of imperfect measurement of the model's theoretical concepts, in particular for inflation. We show that exploiting more information is important for accurate estimation of the model's concepts and shocks, and that it implies different conclusions about key structural parameters and the sources of economic fluctuations.
Keywords: DSGE models; data-rich environment; MCMC methods; measurement error; macroeconomic indicators
JEL Codes: C10; C32; C53; E1; E32; E37
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| conventional practice of estimating DSGE models using a limited number of indicators (E13) | distorted inference regarding model parameters and economic shocks (C51) |
| utilizing a larger set of macroeconomic data (E39) | estimates of model parameters differ significantly (C51) |
| incorporating more data series (Y10) | necessity for fewer structural shocks to explain economic fluctuations (E32) |
| shocks to the efficiency of investment goods (E22) | become a primary driver of business cycle fluctuations (E32) |
| MCMC estimation approach (C59) | leads to more accurate representations of the underlying economic dynamics (E13) |