Details
| Original language | English |
|---|---|
| Pages (from-to) | 1489-1494 |
| Number of pages | 6 |
| Journal | Applied Economics Letters |
| Volume | 27 |
| Issue number | 18 |
| Early online date | 19 Nov 2019 |
| Publication status | Published - 23 Oct 2020 |
Abstract
Time series that have seasonal effects with long periods relative to the observation frequency can exhibit spurious long memory. The effect of these seasonalities on the periodogram is similar to that of structural breaks and non-periodic trends, but it only influences the seasonal frequencies and their harmonics. Still, the effect causes a sizable bias of popular estimators such as the local Whittle estimator. To overcome this, we propose a robust local Whittle estimator based on the omission of the affected periodogram ordinates. In a Monte Carlo study, we compare this estimator with a robust log-periodogram regression-based estimator known in the literature. An application to electricity load series demonstrates the potential of robust estimators for empirical research.
Keywords
- Long memory, seasonality, semiparametric estimation, Whittle likelihood
ASJC Scopus subject areas
- Economics, Econometrics and Finance(all)
- Economics and Econometrics
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In: Applied Economics Letters, Vol. 27, No. 18, 23.10.2020, p. 1489-1494.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Seasonality robust local whittle estimation
AU - Wingert, Simon
AU - Leschinski, Christian
AU - Sibbertsen, Philipp
N1 - Funding Information: We would like to thank the referee for detailed comments that helped us to improve the manuscript substantially. Financial support of the Deutsche Forschungsgemeinschaft (DFG) is gratefully acknowledged.
PY - 2020/10/23
Y1 - 2020/10/23
N2 - Time series that have seasonal effects with long periods relative to the observation frequency can exhibit spurious long memory. The effect of these seasonalities on the periodogram is similar to that of structural breaks and non-periodic trends, but it only influences the seasonal frequencies and their harmonics. Still, the effect causes a sizable bias of popular estimators such as the local Whittle estimator. To overcome this, we propose a robust local Whittle estimator based on the omission of the affected periodogram ordinates. In a Monte Carlo study, we compare this estimator with a robust log-periodogram regression-based estimator known in the literature. An application to electricity load series demonstrates the potential of robust estimators for empirical research.
AB - Time series that have seasonal effects with long periods relative to the observation frequency can exhibit spurious long memory. The effect of these seasonalities on the periodogram is similar to that of structural breaks and non-periodic trends, but it only influences the seasonal frequencies and their harmonics. Still, the effect causes a sizable bias of popular estimators such as the local Whittle estimator. To overcome this, we propose a robust local Whittle estimator based on the omission of the affected periodogram ordinates. In a Monte Carlo study, we compare this estimator with a robust log-periodogram regression-based estimator known in the literature. An application to electricity load series demonstrates the potential of robust estimators for empirical research.
KW - Long memory
KW - seasonality
KW - semiparametric estimation
KW - Whittle likelihood
UR - http://www.scopus.com/inward/record.url?scp=85075208354&partnerID=8YFLogxK
U2 - 10.1080/13504851.2019.1691710
DO - 10.1080/13504851.2019.1691710
M3 - Article
AN - SCOPUS:85075208354
VL - 27
SP - 1489
EP - 1494
JO - Applied Economics Letters
JF - Applied Economics Letters
SN - 1350-4851
IS - 18
ER -