A Wavelet Evaluation of Some Leading Business Cycle Indicators for the German Economy

Krüger, Jens J.

doi:10.1007/s41549-021-00060-8

Cited by 7 publications

(6 citation statements)

References 49 publications

(54 reference statements)

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“…For visual interpretation of phase angles (indicated by arrows in figures), CPUEs were allocated to series 1 and environmental variables to series 2. After series 1 and 2 were defined, arrows Krüger, 2021;Rösch & Schmidbauer, 2018).…”

Section: Discussionmentioning

confidence: 99%

\emptyset_{xy}

of the complex coherency is called the phase‐difference (phase lead of x over y ) (Aguiar‐Conraria & Soares, 2011).…”

Section: Methodsmentioning

confidence: 99%

“…That is, considering one angle

\emptyset_{xy}

of the complex coherency is called the phase‐difference (phase lead of x over y ) (Aguiar‐Conraria & Soares, 2011). A phase‐difference of zero idicates that the time series move together at the specified time–frequency; if

\emptyset_{xy} ∊ ()(,, 0, \frac{π}{2})

, then the series move in phase, but the time series x leads y ; if

\emptyset_{xy} ∊ ()(,, \frac{- π}{2}, 0)

, then it is y that is leading; a phase difference of

π

(or

- π

) indicates an antiphase relation; if

\emptyset_{xy} ∊ ()(,, \frac{π}{2}, π)

, then y is leading; time series x is leading if

\emptyset_{xy} ∊ ()(,, - π, \frac{- π}{2})

(Aguiar‐Conraria & Soares, 2011; Krüger, 2021; Rösch & Schmidbauer, 2018).…”

Section: Methodsmentioning

confidence: 99%

“…The WCs of the two time series were defined by Equation (2) (Torrence & Webster, 1999):

R_{n}^{2} ()(, s) goodbreak= \frac{{(||, S ()(, s^{- 1} W_{n}^{XY} ()(, s)))}^{2}}{S ()(, s^{- 1} {|W_{n}^{X} (s)|}^{2}) S ()(, s^{- 1} {|W_{n}^{Y} (s)|}^{2})}

where S = a smoothing operator and

W_{n}^{X} ()(, s)

= a wavelet transformed continuously from series X . This analysis enabled evaluation of CPUEs and explanatory variables and irregular cycles, to determine whether responses were immediate or misphased, and whether correlations were positive or negative (Krüger, 2021).…”

Section: Methodsmentioning

confidence: 99%

“…t i s were analyzed followingKrüger (2021). That is, considering one angle ∅ xy of the complex coherency is called the phase-difference (phase lead of x over y)(Aguiar-Conraria & Soares, 2011).…”

mentioning

confidence: 99%

See 4 more Smart Citations

How does climate change affect small scale fisheries? A case study of the Lower Amazon in Brazil

Furtado,

Queiroz,

Bentes

et al. 2023

Fisheries Management Eco

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Effects of global climate change on inland artisanal fisheries have received little attention from scientists. We investigated fisheries of the Lower Amazon in Brazil using a wavelet analysis of a 13‐year data series of fishery catches, environmental variables, and climatic indices, to determine how these variables affected catches of different species. The catch per unit of effort (CPUE) of gillnet and line fisheries increased over time. Relative humidity, latent heat, and sea surface temperatures influenced productivity of both fisheries. El Niño had a negative and immediate effect on gillnet CPUE, while its effect on line fishing was positive, with an eight month lag. Our findings indicate that fishery productivity was modulated by climate events, which influenced ecosystems and modified habitats and ecological niches, andthereby impacted fishery productivity.

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Section: Discussionmentioning

confidence: 99%

\emptyset_{xy}

of the complex coherency is called the phase‐difference (phase lead of x over y ) (Aguiar‐Conraria & Soares, 2011).…”

Section: Methodsmentioning

confidence: 99%

“…That is, considering one angle

\emptyset_{xy}

\emptyset_{xy} ∊ ()(,, 0, \frac{π}{2})

, then the series move in phase, but the time series x leads y ; if

\emptyset_{xy} ∊ ()(,, \frac{- π}{2}, 0)

, then it is y that is leading; a phase difference of

π

(or

- π

) indicates an antiphase relation; if

\emptyset_{xy} ∊ ()(,, \frac{π}{2}, π)

, then y is leading; time series x is leading if

\emptyset_{xy} ∊ ()(,, - π, \frac{- π}{2})

(Aguiar‐Conraria & Soares, 2011; Krüger, 2021; Rösch & Schmidbauer, 2018).…”

Section: Methodsmentioning

confidence: 99%

“…The WCs of the two time series were defined by Equation (2) (Torrence & Webster, 1999):

R_{n}^{2} ()(, s) goodbreak= \frac{{(||, S ()(, s^{- 1} W_{n}^{XY} ()(, s)))}^{2}}{S ()(, s^{- 1} {|W_{n}^{X} (s)|}^{2}) S ()(, s^{- 1} {|W_{n}^{Y} (s)|}^{2})}

where S = a smoothing operator and

W_{n}^{X} ()(, s)

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

How does climate change affect small scale fisheries? A case study of the Lower Amazon in Brazil

Furtado,

Queiroz,

Bentes

et al. 2023

Fisheries Management Eco

View full text Add to dashboard Cite

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Global lead-lag changes between climate variability series coincide with major phase shifts in the Pacific decadal oscillation

Seip,

Grøn,

Wang

2023

Theor Appl Climatol

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We show that there are distinct periods when three ocean variability series in the Atlantic and the Pacific Oceans persistently lead or lag each other, as well as distinct periods when ocean variability series lead the rate of changes in global temperature anomaly (∆GTA) and in atmospheric CO2 concentration (1880–2019). The superimposed lead-lag (LL) relations that can be formed from the five climate series (three ocean series, GTA and CO2), ΣLL(10), change directions or weaken synchronously at 6 years: 1900, 1926, 1965, 1977, 1997, and 2013. During the same years, the Pacific decadal oscillation (PDO) changes between positive ( +) and negative (-) phases, but with an additional phase shift in 1947/48. We find bi-decadal oscillations in the rate of change in global temperature, ∆GTA, during the same years. Since the hiatus periods are closely related to the cold phase (-) in PDO, the hiatus periods may also be related to global changes in ocean interactions.

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Scoring Six Detrending Methods on Timing, Lead-Lag Relations, and Cycle Periods: An Empirical Study of US and UK Recessions 1977–2020

Seip,

Zhang

2024

Comput Econ

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This study evaluates six commonly used detrending methods and discuss how detrending may change the timing of events, the identification of lead-lag relations between GDP and employment, and the identification of cycle periods. The detrending methods examined includes linear detrending, polynomial detrending, the first-order differencing, locally weighted scatterplot smoothing (LOESS), Hodrick–Prescott filter, and the Hamilton filter. We apply the detrending methods to the United States and United Kingdom gross domestic product (GDP) from 1977 to 2020. We find that for the GDP series the first-order differencing score best on all three criteria, however, it also shows more false recessions than the other detrending methods. A linear, a polynomial, and a LOESS trend all scored well. The three methods miss-specified the timing of the recessions with less than one quarter and all three gave results that would comply with stylized facts in macroeconomics. The Hodrick–Prescott (HP) filter and Hamilton filter did not achieve high scores on one or two of the criteria and scored worst on average performance.

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A Wavelet Evaluation of Some Leading Business Cycle Indicators for the German Economy

Cited by 7 publications

References 49 publications

How does climate change affect small scale fisheries? A case study of the Lower Amazon in Brazil

How does climate change affect small scale fisheries? A case study of the Lower Amazon in Brazil

Global lead-lag changes between climate variability series coincide with major phase shifts in the Pacific decadal oscillation

Scoring Six Detrending Methods on Timing, Lead-Lag Relations, and Cycle Periods: An Empirical Study of US and UK Recessions 1977–2020

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