Replication data for: Learning in a Medium-Scale DSGE Model with Expectations Based on Small Forecasting Models

Principal Investigator(s): View help for Principal Investigator(s) Sergey Slobodyan; Raf Wouters

Name	File Type	Size	Last Modified
Code			10/12/2019 09:31:PM
Data			10/12/2019 09:31:PM
Fig_3			10/12/2019 09:31:PM
Readme.txt	text/plain	1.6 KB	10/12/2019 05:31:PM

Project Description

Summary: This paper evaluates the empirical performance of a medium-scale DSGE model with agents forming expectations using small forecasting models updated by the Kalman filter. The adaptive learning model fits the data better than the rational expectations (RE) model. Beliefs about the inflation persistence explain the observed decline in the mean and the volatility of inflation as well as Phillips curve flattening. Learning about inflation results in lower estimates for the persistence of the exogenous shocks that drive price and wage dynamics in the RE version of the model. Expectations based on small forecasting models are closely related to the survey evidence on inflation expectations. (JEL C53, D83, D84, E13, E17, E31)

Scope of Project

JEL Classification:
      C53 Forecasting Models; Simulation Methods
      D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
      D84 Expectations; Speculations
      E13 General Aggregative Models: Neoclassical
      E17 General Aggregative Models: Forecasting and Simulation: Models and Applications
      E31 Price Level; Inflation; Deflation

Related Publications

Download this project

Published Versions

V1 [2019-10-12]

Export Metadata

Dublin Core

DDI 2.5

Report a Problem

Found a serious problem with the data, such as disclosure risk or copyrighted content? Let us know.

This material is distributed exactly as it arrived from the data depositor. ICPSR has not checked or processed this material. Users should consult the investigator(s) if further information is desired.