Detecting long-range dependence with truncated ratios of periodogram ordinates

Reschenhofer, Erhard; Mangat, Manveer Kaur

doi:10.1080/03610926.2019.1709646

Cited by 4 publications

(2 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, note that the results from Table 5 are in line with the ones from Mangat and Reschenhofer (2020). The authors use some of the semiparametric estimators considered in this study along with the small-sample estimator developed by Reschenhofer and Mangat (2020). Using the same sampling frequency and bandwidth, the authors found that global and regional temperature series fall into the nonstationary range.…”

Section: Robustness Exercisessupporting

confidence: 69%

Temperature Anomalies, Long Memory, and Aggregation

Vera-Valdés

2021

Econometrics

View full text Add to dashboard Cite

Econometric studies for global heating have typically used regional or global temperature averages to study its long memory properties. One typical explanation behind the long memory properties of temperature averages is cross-sectional aggregation. Nonetheless, formal analysis regarding the effect that aggregation has on the long memory dynamics of temperature data has been missing. Thus, this paper studies the long memory properties of individual grid temperatures and compares them against the long memory dynamics of global and regional averages. Our results show that the long memory parameters in individual grid observations are smaller than those from regional averages. Global and regional long memory estimates are greatly affected by temperature measurements at the Tropics, where the data is less reliable. Thus, this paper supports the notion that aggregation may be exacerbating the long memory estimated in regional and global temperature data. The results are robust to the bandwidth parameter, limit for station radius of influence, and sampling frequency.

show abstract

Section: Robustness Exercisessupporting

confidence: 69%

Temperature Anomalies, Long Memory, and Aggregation

Vera-Valdés

2021

Econometrics

View full text Add to dashboard Cite

show abstract

“…Assuming that fractal dynamics does in fact exist in precious metal returns, they explored possible trading strategies that are based on local estimates of H. In contrast, Reschenhofer et al [66] found no evidence of long-range dependence, neither in stock index returns nor in gold returns. Mangat and Reschenhofer [40] and Reschenhofer and Mangat [41] prices, but also at the log absolute returns. Using log absolute returns instead of absolute returns or squared absolute returns for the investigation of volatility has the advantage that we do not have to work with extremely skewed distributions.…”

Section: Resultsmentioning

confidence: 99%

Improved Estimation of the Memory Parameter

Reschenhofer¹,

Mangat²,

Stark³

2020

TEL

Self Cite

View full text Add to dashboard Cite

In this paper, it is proposed to estimate the memory parameter of a potentially long-range dependent time series by applying goodness-of-fit tests to the cumulative normalized periodogram in the neighborhood of frequency zero. The results of an extensive simulation study show that this new estimator performs well compared to conventional frequency-domain estimators which are based on the Whittle likelihood or are obtained from the popular log periodogram estimator by trimming, smoothing, and utilizing non-Fourier frequencies, respectively. In an empirical investigation of log absolute daily index returns, we find evidence of long-range dependence with values of the memory parameter in the range between 0.2 and 0.3 both in developed and developing stock markets. There are no indications of long-range dependence in the case of the original index returns.

show abstract

Frequency-Domain Evidence for Climate Change

Mangat

Reschenhofer

2020

Econometrics

Self Cite

View full text Add to dashboard Cite

The goal of this paper is to search for conclusive evidence against the stationarity of the global air surface temperature, which is one of the most important indicators of climate change. For this purpose, possible long-range dependencies are investigated in the frequency-domain. Since conventional tests of hypotheses about the memory parameter, which measures the degree of long-range dependence, are typically based on asymptotic arguments and are therefore of limited practical value in case of small or medium sample sizes, we employ a new small-sample test as well as a related estimator for the memory parameter. To safeguard against false positive findings, simulation studies are carried out to examine the suitability of the employed methods and hemispheric datasets are used to check the robustness of the empirical findings against low-frequency natural variability caused by oceanic cycles. Overall, our frequency-domain analysis provides strong evidence of non-stationarity, which is consistent with previous results obtained in the time domain with models allowing for stochastic or deterministic trends.

show abstract

Detecting long-range dependence with truncated ratios of periodogram ordinates

Cited by 4 publications

References 40 publications

Temperature Anomalies, Long Memory, and Aggregation

Temperature Anomalies, Long Memory, and Aggregation

Improved Estimation of the Memory Parameter

Frequency-Domain Evidence for Climate Change

Contact Info

Product

Resources

About