Name File Type Size Last Modified
getM.m text/x-matlab 555 bytes 01/04/2022 07:51:AM
getXy.m text/x-matlab 974 bytes 01/04/2022 07:51:AM
get_perc_IRFs.m text/x-objcsrc 2.1 KB 01/04/2022 07:51:AM
getmstar.m text/x-matlab 97 bytes 01/04/2022 07:51:AM
gettau.m text/x-matlab 249 bytes 01/04/2022 07:51:AM
impulse_response_1SD.m text/x-matlab 222 bytes 01/04/2022 07:51:AM
kiliandata.txt text/plain 20.9 KB 01/04/2022 07:51:AM
lag0.m text/x-matlab 1.2 KB 01/04/2022 07:51:AM
lagn.m text/x-matlab 101 bytes 01/04/2022 07:51:AM
main_column2_Bayes_Cholesky.m text/x-matlab 10.4 KB 01/04/2022 07:51:AM

Project Description

Summary:  View help for Summary
The problem of identification is often the core challenge of empirical economic research. The traditional approach to identification is to bring in additional information in the form of identifying assumptions, such as restrictions that certain magnitudes have to be zero. In this paper we suggest that what are usually thought of as identifying assumptions should more generally be described as information that the analyst had about the economic structure before seeing the data. Such information is most naturally represented as a Bayesian prior distribution over certain features of the economic structure. Here we provide the data and code for an empirical illustration of this approach.

Scope of Project

Subject Terms:  View help for Subject Terms structural vector autoregressions; Bayesian analysis; identification; sign restrictions
JEL Classification:  View help for JEL Classification
      C11 Bayesian Analysis: General
      C32 Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
      Q43 Energy and the Macroeconomy


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