Arbeitspapier

Reliable real-time output gap estimates based on a modified Hamilton filter

We propose a simple modification of Hamilton's (2018) time series filter that yields reliable and economically meaningful real-time output gap estimates. The original filter relies on 8 quarter ahead forecast errors of a simple autoregression of real GDP. While this approach yields a cyclical component 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 mutes short and amplifies medium length cycles. Further, as the estimated trend contains high frequency noise, it can hardly be interpreted as potential GDP. A simple modification 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 and the original Hamilton filter are economically much more meaningful measures of the business cycle than those based on other simple statistical trend-cycle decomposition techniques, such as the HP or bandpass filter, and should thus be used preferably.

Language
Englisch

Bibliographic citation
Series: Kiel Working Paper ; No. 2158

Classification
Wirtschaft
Methodological Issues: General
Business Fluctuations; Cycles
Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
Subject
business cycle measurement
potential output
trend-cycle decomposition
real-time data
inflation forecasting
output growth forecasting

Event
Geistige Schöpfung
(who)
Quast, Josefine
Wolters, Maik H.
Event
Veröffentlichung
(who)
Kiel Institute for the World Economy (IfW)
(where)
Kiel
(when)
2020

Handle
Last update
10.03.2025, 11:42 AM CET

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Object type

  • Arbeitspapier

Associated

  • Quast, Josefine
  • Wolters, Maik H.
  • Kiel Institute for the World Economy (IfW)

Time of origin

  • 2020

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