Real-time US GDP gap properties using Hamilton’s regression-based filter

Jönsson, Kristian

doi:10.1007/s00181-019-01631-6

Cited by 18 publications

(12 citation statements)

References 7 publications

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“…filter are small relative to the amplitude of the output gap and are mainly caused by data revisions as also shown byJönsson (2019) for the original Hamilton filter. By contrast, final and real-time output gap estimates of the HP and the BP filter differ markedly from each other.…”

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confidence: 87%

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

Quast

Wolters

2020

Journal of Business & Economic Statistics

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We propose a simple modification of the time series filter by Hamilton (2018) 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 log real GDP. 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 itself 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 cycle than those based on other simple statistical trend-cycle decomposition techniques such as the HP or the Bandpass filter.

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confidence: 87%

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

Quast

Wolters

2020

Journal of Business & Economic Statistics

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“…The arguments concerning the advantages and disadvantages of the use of empirical modeling on both filters in the literature vary [4][5][6][7][8][9]. Some papers point out that the HP filter causes spurious cycles when it is applied to statistical data.…”

Section: Introductionmentioning

confidence: 99%

Comparison of HP Filter and the Hamilton’s Regression

Dritsaki

2022

Mathematics

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In this paper we examine if the use of Hamilton’s regression filter significantly modifies the cyclical components concerning unemployment in Greece compared with those using the Hodrick–Prescott double filter (HP). Hamilton suggested the use of a regression filter in order to overcome some of the drawbacks of the HP filter, which contains the presence of false cycles, the bias in the end of the sample, and the ad-hoc assumptions for the parameters’ smoothing. Thus, our paper examines two widely used detrending methods for the extraction of cyclical components, including techniques of deterministic detrending as well as stochastic detrending. Using quarterly data for the unemployment of Greece in a macroeconomic model decomposition, we indicate that trend components and cycle components of Hamilton’s filter regression led to significantly larger cycle volatilities than those from the HP filter. The dynamic forecasting in the sample, occurred both with autoregressive forecasting, that yields steady forecasts for a wide variety of non-stationary procedures, and with the HP filter, along with its constraints at the end of the time series. The results of the paper showed that the dynamic forecasting of the HP filter is better than that of Hamilton’s in all assessment measures.

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“…An increasing number of studies have provided theoretical and empirical evaluations of Hamilton’s proposed methodology. See, for example, Phillips and Shi ( 2021 ), Hodrick ( 2020 ), Jönsson ( 2020a , 2020b ), Quast and Wolters ( 2020 ), Schüler ( 2018 ), and Drehmann and Yetman ( 2018 ) among others.…”

Section: Introductionmentioning

confidence: 99%

“…The paper takes no stand on which of the two methods should be used when decomposing a series into trend and cycle components, and concludes that choosing between the two filters may turn out to be harder than at first thought. Jönsson ( 2020b ) compares the HP and Hamilton filters with respect to real-time stability in US GDP gap estimation and finds that the Hamilton filter outperforms the HP filter when it comes to real-time revisions. The source of the inferior performance of the HP filter is that trend and cycle estimates close to the end of the sample are revised to a large extent as more data are added to the series, a finding also documented by other authors.…”

Section: Introductionmentioning

confidence: 99%

“…An increasing number of studies have provided theoretical and empirical evaluations of Hamilton's proposed methodology. See, for example, Phillips and Shi (2021), Hodrick (2020), Jönsson (2020aJönsson ( , 2020b, Quast and Wolters (2020), Schüler (2018), and Drehmann and Yetman (2018) among others. Phillips and Shi (2021) provide detailed responses to Hamilton's (2018) critique of the HP filter, and analyse the performance of Hamilton's regression approach relative to their boosted HP filter (bHP).…”

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confidence: 99%

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