Measuring Business Cycles in Economic Time Series

Kaiser, Regina; Maravall, Agustín

doi:10.1007/978-1-4613-0129-5

Cited by 91 publications

(83 citation statements)

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“…The most prominent attempts involve expanding the information set supplied to the filter in the temporal dimension, using either model-based forecasts (Kaiser and Maravall, 1999;Gomez, 2001;Kaiser and Maravall, 2001;Mise et al, 2005), or variables observed at a higher frequency (Aruoba et al, 2009); in the covariates dimension with the employment of model-based multivariate representations (Planas and Rossi, 2004; Valle e Azevedo et al, 2006;Altissimo et al, 2010;Valle e Azevedo, 2011;Marcellino and Musso, 2011), or jointly in both of these dimensions (Garratt et al, 2008;Clements and Galvão, 2012).…”

Section: Introductionmentioning

confidence: 99%

Improving the Reliability of Real-Time Hodrick-Prescott Filtering Using Survey Forecasts

Galimberti

Moura

2014

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Insper -Institute of Education and Research AbstractMeasuring economic activity in real-time is a crucial issue in applied research and in the decision-making process of policy makers; however, it also poses intricate challenges to statistical filtering methods that are built to operate optimally under the auspices of an infinite number of observations. In this paper, we propose and evaluate the use of survey forecasts to augment one of those methods, namely the largely used Hodrick-Prescott filter so as to attenuate the end-of-sample uncertainty observed in the resulting gap estimates. We find that this approach achieves powerful improvements to the real-time reliability of these economic activity measures, and we argue that the use of surveys is preferable relative to model-based forecasts due to both an usually superior accuracy in predicting current and future states of the economy and its parsimony.

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Section: Introductionmentioning

confidence: 99%

Improving the Reliability of Real-Time Hodrick-Prescott Filtering Using Survey Forecasts

Galimberti

Moura

2014

SSRN Journal

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“…The formula is a simple application of the general framework developed by Whittle (1983) and Bell (1984) and described by Harvey (1989) and Kaiser/Maravall (2001). The numerator is the autocovariance generating function of {µ t }, the denominator the autocovariance generating function of {y t }.…”

Section: The Optimal Ltermentioning

confidence: 99%

“…But, as Kaiser/Maravall (2001) notes, this function only tells part of the story. It is much more useful to consider the spectrum of a generated component.…”

Section: The Optimal Ltermentioning

confidence: 99%

“…An often used example is the case of a random walk as the input series (Kaiser/Maravall 2001). In this case, the HP lter typically produces cycles with periods between 8 and 10 years (λ = 1600).…”

Section: A Simulation Studymentioning

confidence: 99%

“…The features of this lter were intensively studied in the literature (see, among many others, King/Rebelo 1993;Harvey/Jaeger 1993;Cogley/Nason 1994;Kaiser/Maravall 2001). The properties of the HP lter in the time and frequency domain depend mainly on a smoothing parameter λ which governs the smoothness of the estimated trend and the shape of the estimated cycle.…”

Section: Introductionmentioning

confidence: 99%

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Why We Should Use High Values for the Smoothing Parameter of the Hodrick-Prescott Filter

Flaig

2015

Jahrbücher Für Nationalökonomie Und Statistik

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. The HP filter is the most popular filter for extracting the trend and cycle components from an observed time series. Many researchers consider the smoothing parameter λ = 1600 as something like an universal constant. It is well known that the HP filter is an optimal filter under some restrictive assumptions, especially that the "cycle" is white noise. In this paper we show that one gets a good approximation of the optimal Wiener-Kolmogorov filter for autocorrelated cycle components by using the HP filter with a much higher smoothing parameter than commonly used. In addition, a new method -based on the properties of the differences of the estimated trend -is proposed for the selection of the smoothing parameter. Terms of use: Documents in EconStor mayJEL-Code: C220, C520.

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Statistical Signal Extraction and Filtering: A Partial Survey

Pollock

2009

Handbook of Computational Econometrics

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A wide variety of techniques have been devised for the purpose of extracting the components of econometric data sequences. The traditional approach, which continues to prevail in the central statistical agencies of many countries, has relied upon filters that have been derived with refernce to the principles of actuarial graduation. Latterly, filters that are grounded in the Wiener-Kolmogorov theory of signal extraction have become prominent in academic research. There are several alternative ways of applying the Wiener-Kolmogorov filters to the data.

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Measuring Business Cycles in Economic Time Series

Cited by 91 publications

References 0 publications

Improving the Reliability of Real-Time Hodrick-Prescott Filtering Using Survey Forecasts

Improving the Reliability of Real-Time Hodrick-Prescott Filtering Using Survey Forecasts

Why We Should Use High Values for the Smoothing Parameter of the Hodrick-Prescott Filter

Statistical Signal Extraction and Filtering: A Partial Survey

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