Konferenzbeitrag

Reliable Real-time Output Gap Estimates Based on a Modified Hamilton Filter

We propose a simple modification of the time series filter by Hamilton (2018b) that yields reliable and economically meaningful real-time output gap estimates. The original filter relies on 8-quarter ahead forecasts errors of an autoregression. While this approach yields a cyclical component of GDP that is hardly revised with new incoming data due to the one-sided filtering approach, it does not cover typical business cycle frequencies evenly, but short business cycles are muted and medium length business cycles are amplified. Further, the estimated trend is as volatile as GDP and can thus hardly be interpreted as potential GDP. A simple modification that is based on the mean of 4- to 12-quarter-ahead forecast errors shares the favorable real-time properties of the Hamilton filter, but leads to a much better coverage of typical business cycle frequencies and a smooth estimated trend. Based on output growth and inflation forecasts and a comparison to revised output gap estimates from policy institutions, we find that real-time output gaps based on the modified Hamilton filter are economically much more meaningful measures of the business cycles than those based on other simple statistical trend-cycle decomposition techniques such as the HP or the Bandpass filter.

Sprache
Englisch

Erschienen in
Series: Beiträge zur Jahrestagung des Vereins für Socialpolitik 2019: 30 Jahre Mauerfall - Demokratie und Marktwirtschaft - Session: Applied Macroeconometrics I ; No. A13-V1

Klassifikation
Wirtschaft
Methodological Issues: General
Business Fluctuations; Cycles
Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
Thema
Output gap
potential output
trend-cycle decomposition
Hamilton filter
real-time data
inflation forecasting

Ereignis
Geistige Schöpfung
(wer)
Quast, Josefine
Wolters, Maik H.
Ereignis
Veröffentlichung
(wer)
ZBW - Leibniz-Informationszentrum Wirtschaft
(wo)
Kiel, Hamburg
(wann)
2019

Handle
Letzte Aktualisierung
10.03.2025, 11:44 MEZ

Datenpartner

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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Konferenzbeitrag

Beteiligte

  • Quast, Josefine
  • Wolters, Maik H.
  • ZBW - Leibniz-Informationszentrum Wirtschaft

Entstanden

  • 2019

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