Open AccessWhich Output Gap Estimates Are Stable in Real Time and Why?
Author(s) -
Alessandro Barbarino,
Travis J. Berge,
Han Chen,
Andrea Stella
Publication year - 2020
Publication title -
finance and economics discussion series
Language(s) - English
Resource type - Journals
eISSN - 2767-3898
pISSN - 1936-2854
DOI - 10.17016/feds.2020.102
Subject(s) - output gap , metric (unit) , stability (learning theory) , econometrics , suite , decomposition , potential output , economics , unemployment , point (geometry) , unemployment rate , mathematics , computer science , interest rate , macroeconomics , history , ecology , monetary policy , operations management , geometry , archaeology , machine learning , biology
Output gaps that are estimated in real time can differ substantially from those estimated after the fact. We aim to understand the real-time instability of output gap estimates by comparing a suite of reduced-form models. We propose a new statistical decomposition and find that including a Okun’s law relationship improves real-time stability by alleviating the end-point problem. Models that include the unemployment rate also produce output gaps with relevant economic content. However, we find that no model of the output gap is clearly superior to the others along each metric we consider.