Cyclical Dynamics and Trend/Cycle Definitions: Comparing the HP and Hamilton Filters

Jönsson, Kristian

doi:10.1007/s41549-020-00039-x

Cited by 13 publications

(9 citation statements)

References 16 publications

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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%

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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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Section: Introductionmentioning

confidence: 99%

“…A number of studies have provided theoretical as well as empirical valuations of Hamilton's suggested methodology, such as [4][5][6][30][31][32], and also [9].…”

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confidence: 99%

Comparison of HP Filter and the Hamilton’s Regression

Dritsaki

2022

Mathematics

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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%

“…Jönsson ( 2020a ) has assessed the extent to which HP and Hamilton filters introduce spurious dynamics to a business cycle’s cyclical component, and report that similar dynamics can be found in the cyclical component of HP-filtered and Hamilton-filtered series. 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.…”

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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Does Hamilton’s OLS Regression Provide a “better alternative” to the Hodrick-Prescott Filter? A New Zealand Business Cycle Perspective

Hall

Thomson

2021

J Bus Cycle Res

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The boosted Hodrick‐Prescott filter is more general than you might think

Mei,

Phillips,

Shi

2024

J of Applied Econometrics

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SummaryThe global financial crisis and Covid‐19 recession have renewed discussion concerning trend‐cycle discovery in macroeconomic data, and boosting has recently upgraded the popular Hodrick‐Prescott filter to a modern machine learning device suited to data‐rich and rapid computational environments. This paper extends boosting's trend determination capability to higher order integrated processes and time series with roots that are local to unity. The theory is established by understanding the asymptotic effect of boosting on a simple exponential function. Given a universe of time series in FRED databases that exhibit various dynamic patterns, boosting timely captures downturns at crises and recoveries that follow.

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Cyclical Dynamics and Trend/Cycle Definitions: Comparing the HP and Hamilton Filters

Cited by 13 publications

References 16 publications

Comparison of HP Filter and the Hamilton’s Regression

Comparison of HP Filter and the Hamilton’s Regression

Does Hamilton’s OLS Regression Provide a “better alternative” to the Hodrick-Prescott Filter? A New Zealand Business Cycle Perspective

The boosted Hodrick‐Prescott filter is more general than you might think

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