Data and Code for: A Sufficient Statistics Approach for Macro Policy

Principal Investigator(s): View help for Principal Investigator(s) Regis Barnichon, Federal Reserve Bank of San Francisco; Geert Mesters, Universitat Pompeu Fabra

Version: View help for Version V1

Name	File Type	Size	Last Modified
EstimationIR			09/15/2023 08:55:PM
EstimationSEPdist			09/15/2023 08:55:PM
Results			09/16/2023 12:00:AM
lib			09/16/2023 12:00:AM
FedHistory_lambda1_BVAR_ZLB.mat	application/x-matlab-data	652 MB	09/15/2023 04:53:PM
Main.m	text/x-matlab	802 bytes	09/15/2023 04:53:PM
OPP_2008M4_BVAR.m	text/x-objcsrc	16.1 KB	09/15/2023 04:53:PM
OPP_2010M4_BVAR.m	text/x-objcsrc	16.6 KB	09/15/2023 04:53:PM
OPP_2021M10_BVAR.m	text/x-objcsrc	19.4 KB	09/15/2023 04:53:PM
OPP_2021M3_BVAR.m	text/x-objcsrc	19.4 KB	09/15/2023 04:53:PM

Project Description

Summary: The evaluation of macroeconomic policy decisions has traditionally relied on the formulation of a specific economic model. In this work, we show that two statistics are sufficient to detect, often even correct, non-optimal policies, i.e., policies that do not minimize the loss function. The two sufficient statistics are (i) forecasts for the policy objectives conditional on the policy choice, and (ii) the impulse responses of the policy objectives to policy shocks. Both statistics can be estimated without relying on a specific structural economic model. We illustrate the method by studying US monetary policy decisions.

Scope of Project

JEL Classification:
      C14 Semiparametric and Nonparametric Methods: General
      C32 Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
      E32 Business Fluctuations; Cycles
      E52 Monetary Policy

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Published Versions

V1 [2023-11-14]

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