Data and Code for: Robust Predictions for DSGE Models with Incomplete Information

Principal Investigator(s):  View help for Principal Investigator(s) Ryan Chahrour, Boston College; Robert Ulbricht, Boston College

Version:  View help for Version V1

Name File Type Size Last Modified
  final_figures 01/21/2022 10:13:AM
  input_files 08/25/2021 02:42:PM
  output_files 08/25/2021 02:42:PM
ex1comp.m text/x-matlab 4.2 KB 08/25/2021 10:42:AM
ex1frontier.m text/x-matlab 2.6 KB 08/25/2021 10:42:AM
generate_file.m text/x-matlab 1.2 KB 08/25/2021 10:42:AM
macroC.m text/x-matlab 2.4 KB 08/25/2021 10:42:AM
main_prog.m text/x-objcsrc 853 bytes 08/25/2021 10:42:AM
make_all_ex1.m text/x-matlab 6.1 KB 08/25/2021 10:42:AM
microC.m text/x-matlab 2.4 KB 08/25/2021 10:42:AM

Project Description

Summary:  View help for Summary Replication files for numerical results in AEJ:Macro article. Abstract of article:

We provide predictions for DSGE models with incomplete information that are
robust across information structures. Our approach maps an incomplete-information
model into a full-information economy with time-varying expectation wedges and provides conditions that ensure the wedges are rationalizable by some information structure. Using our approach, we quantify the potential importance of information as a source of business cycle fluctuations in an otherwise frictionless model. Our approach uncovers a central role for firm-specific demand shocks in supporting aggregate confidence fluctuations. Only if firms face unobserved local demand shocks can confidence fluctuations account for a significant portion of the US business cycle.

Scope of Project

Subject Terms:  View help for Subject Terms Business Cycles; Incomplete Information
JEL Classification:  View help for JEL Classification
      D84 Expectations; Speculations
      E32 Business Fluctuations; Cycles
Geographic Coverage:  View help for Geographic Coverage USA
Data Type(s):  View help for Data Type(s) aggregate data


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Published Versions

V1 [2022-12-09]

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