Monthly US business cycle indicators: a new multivariate approach based on a band-pass filter

Marczak, Martyna; Gómez, Víctor

doi:10.1007/s00181-016-1108-2

Cited by 3 publications

(2 citation statements)

References 33 publications

(33 reference statements)

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“…Since IIP is calculated on the basis of real activity, it can mimic business cycle features of an economy. There have been studies which show synchronicity between industrial output data and real GDP‐based business cycles (e.g., Marczak & Gómez, 2017). Though most of earlier studies employed the Hodrick–Prescott time series filter for extraction of cyclical components, it has been criticized for its arbitrary choice of the smoothing parameter.…”

Section: Methodsmentioning

confidence: 99%

Financial cycle, business cycle, and policy uncertainty in India: An empirical investigation

Paramanik

Bhandari

Kamaiah

2021

Bulletin of Econ Res

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This paper is an empirical attempt to assess the relations among business cycles, financial cycles, and economic uncertainty for India during the period January 2003 to January 2020. Empirical findings from the vector autoregression model suggest a bidirectional causal relationship between the real and financial markets. Further, the aforementioned markets are found to influence the uncertainty index in India. We employ the Granger causality test in the frequency domain which exhibits the dynamic nature of causal relations, while our novel fractal connectivity approach reveals the long‐run correlation structure among all three variables.

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Section: Methodsmentioning

confidence: 99%

Financial cycle, business cycle, and policy uncertainty in India: An empirical investigation

Paramanik

Bhandari

Kamaiah

2021

Bulletin of Econ Res

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“…Along with these variables, the Economic Cycle indicator was also employed as an explanatory variable. The latter cycle indicator was extracted through a time series of the industrial production index as an alternative monthly indicator (see, for example, Marczak and G omez, 2017;Alqaralleh, 2019).…”

Section: Data and Main Features Of The Economic And Oil Cyclesmentioning

confidence: 99%

Are the macroeconomic effects on oil price asymmetric? An asymmetric quantile regression approach

Alqaralleh

2022

IJESM

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Purpose This paper aims to contribute to the clarification of whether the dependence and causality between oil and the macrofundamentals change across different quantiles of the distribution function. Design/methodology/approach Within the context of an asymmetric quantile approach, we drop the assumption that variables operate at the upper tails of the distribution in the way that they operate at the mean. Findings Our innovative approach indicates that the response of oil prices not only differs according to the underlying source of the variables shock but also differs across the quantiles. Originality/value Although a number of recent studies are closely related to our present research, our novel findings offer some important insights that foreshadow the empirical results. The current research addresses to answer the following questions, in sequence: (i) Is there any extreme value dependence between the crude oil and macroeconomic variables? If yes, (ii) is the dependence symmetric or asymmetric? Finally, (iii) can this dependence be driven by the phases of the economic cycle?

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Measuring business cycles: Empirical evidence based on an unobserved component approach

Alqaralleh

2019

Cogent Economics & Finance

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Monthly US business cycle indicators: a new multivariate approach based on a band-pass filter

Abstract: This article proposes a new multivariate method to construct business cycle indicators

Cited by 3 publications

References 33 publications

Financial cycle, business cycle, and policy uncertainty in India: An empirical investigation

Financial cycle, business cycle, and policy uncertainty in India: An empirical investigation

Are the macroeconomic effects on oil price asymmetric? An asymmetric quantile regression approach

Measuring business cycles: Empirical evidence based on an unobserved component approach

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