This paper investigates the forecasting performance of the class of small-scale New Keynesian Dynamic Stochastic General Equilibrium (DSGE) business cycle monetary policy models under the assumption that the agents are `boundedly rational' and have quasi-rational expectations (QRE). The QRE hypothesis maintains that the agents compute their conditional forecasts by using their `best fitting' statistical model for the variables. The agents' forecasting model is a VAR system when all variables are observed and is a state-space system when some of the variables are unobserved. In particular a QRE-DSGE model is obtained from the baseline DSGE model by suitable augmenting its dynamic structure such that the reduced form solution of the system has the same time-series representation of the agents' forecasting model. This approach provides a `natural' remedy to the typical difficulties DSGE models based on rational expectations have to account for the rich contemporaneous and dynamic correlation structure of the data. The proposed approach suggests a way to connect DSGE and VAR modeling. An application based on U.S data illustrates the advantages of using QRE in terms of forecasting performance.
Estimation of Quasi Rational DSGE Models
ANGELINI, GIOVANNI
2014
Abstract
This paper investigates the forecasting performance of the class of small-scale New Keynesian Dynamic Stochastic General Equilibrium (DSGE) business cycle monetary policy models under the assumption that the agents are `boundedly rational' and have quasi-rational expectations (QRE). The QRE hypothesis maintains that the agents compute their conditional forecasts by using their `best fitting' statistical model for the variables. The agents' forecasting model is a VAR system when all variables are observed and is a state-space system when some of the variables are unobserved. In particular a QRE-DSGE model is obtained from the baseline DSGE model by suitable augmenting its dynamic structure such that the reduced form solution of the system has the same time-series representation of the agents' forecasting model. This approach provides a `natural' remedy to the typical difficulties DSGE models based on rational expectations have to account for the rich contemporaneous and dynamic correlation structure of the data. The proposed approach suggests a way to connect DSGE and VAR modeling. An application based on U.S data illustrates the advantages of using QRE in terms of forecasting performance.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
