Extracting the Cyclical Component in Hours Worked

Bernardi, Mauro; Corte, Giuseppe Della; Proietti, Tommaso

doi:10.2202/1558-3708.1818

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…The perspective that is taken here is that we either believe that the outliers are not going to affect the future of y t , or that we are inherently interested in forecasting the component y ‡ t . If interest lies in predicting y t , taking into account that outlier contamination can occur also in the future, so that the predictive density reflects the additional uncertainty due the presence of the outliers, other methods, such as modeling the distribution of the BSM disturbances as scale mixtures of normals, should be applied; see Bruce and Jurke (1996) and Bernardi et al (2011).…”

Section: Resultsmentioning

confidence: 99%

A Data-Cleaning Augmented Kalman Filter for Robust Estimation of State Space Models

2016

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This article presents a robust augmented Kalman filter that extends the data-cleaning filter (Masreliez and Martin, 1977) to the general state space model featuring nonstationary and regression effects. The robust filter shrinks the observations towards their one-step-ahead prediction based on the past, by bounding the effect of the information carried by a new observation according to an influence function. When maximum likelihood estimation is carried out on the replacement data, an M-type estimator is obtained. We investigate the performance of the robust AKF in two applications using as a modeling framework the basic structural time series model, a popular unobserved components model in the analysis of seasonal time series. First, a Monte Carlo experiment is conducted in order to evaluate the comparative accuracy of the proposed method for estimating the variance parameters. Second, the method is applied in a forecasting context to a large set of European trade statistics series.JEL Classification: C32, C53, C63

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Section: Resultsmentioning

confidence: 99%

A Data-Cleaning Augmented Kalman Filter for Robust Estimation of State Space Models

2016

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“…However, after two years it becomes negligible and it disappears in the long run in all cases. In other studies the cyclical pattern in hours worked is modelled as a simple AR(2) processes with complex roots (see, e.g., Bernardi et al, 2008) which produce autocorrelations (and impulse responses) decaying at an exponential rate. When using our approach the rate of decay is hyperbolic, i.e.…”

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confidence: 99%

Persistence and cycles in US hours worked

Caporale

Gil-Alana

2014

Economic Modelling

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This paper analyses monthly hours worked in the US over the sample period 1939m1 -2011m10 using a cyclical long memory model; this is based on Gegenbauer processes and characterised by autocorrelations decaying to zero cyclically and at a hyperbolic rate along with a spectral density that is unbounded at a non-zero frequency. The reason for choosing this specification is that the periodogram of the hours worked series has a peak at a frequency away from zero. The empirical results confirm that this model works extremely well for hours worked, and it is then employed to analyse their relationship with technology shocks. It is found that hours worked increase on impact in response to a technology shock (though the effect dies away rapidly), consistently with Real Business Cycle (RBC) models.

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Persistence and Cycles in US Hours Worked

Caporale

Gil-Alana

2012

SSRN Journal

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Extracting the Cyclical Component in Hours Worked

Cited by 4 publications

References 23 publications

A Data-Cleaning Augmented Kalman Filter for Robust Estimation of State Space Models

A Data-Cleaning Augmented Kalman Filter for Robust Estimation of State Space Models

Persistence and cycles in US hours worked

Persistence and Cycles in US Hours Worked

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