A MIDAS approach to modeling first and second moment dynamics

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A MIDAS approach to modeling first and second moment dynamics. / Pettenuzzo, Davide; Timmermann, Allan; Valkanov, Rossen.

I: Journal of Econometrics, Bind 193, Nr. 2, 01.08.2016, s. 315-334.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Harvard

Pettenuzzo, D, Timmermann, A & Valkanov, R 2016, 'A MIDAS approach to modeling first and second moment dynamics', Journal of Econometrics, bind 193, nr. 2, s. 315-334. https://doi.org/10.1016/j.jeconom.2016.04.009

APA

Pettenuzzo, D., Timmermann, A., & Valkanov, R. (2016). A MIDAS approach to modeling first and second moment dynamics. Journal of Econometrics, 193(2), 315-334. https://doi.org/10.1016/j.jeconom.2016.04.009

CBE

MLA

Pettenuzzo, Davide, Allan Timmermann og Rossen Valkanov. "A MIDAS approach to modeling first and second moment dynamics". Journal of Econometrics. 2016, 193(2). 315-334. https://doi.org/10.1016/j.jeconom.2016.04.009

Vancouver

Pettenuzzo D, Timmermann A, Valkanov R. A MIDAS approach to modeling first and second moment dynamics. Journal of Econometrics. 2016 aug 1;193(2):315-334. https://doi.org/10.1016/j.jeconom.2016.04.009

Author

Pettenuzzo, Davide ; Timmermann, Allan ; Valkanov, Rossen. / A MIDAS approach to modeling first and second moment dynamics. I: Journal of Econometrics. 2016 ; Bind 193, Nr. 2. s. 315-334.

Bibtex

@article{cd54560924b3492b8227cec188bac6e4,
title = "A MIDAS approach to modeling first and second moment dynamics",
abstract = "We propose a new approach to predictive density modeling that allows for MIDAS effects in both the first and second moments of the outcome. Specifically, our modeling approach allows for MIDAS stochastic volatility dynamics, generalizing a large literature focusing on MIDAS effects in the conditional mean, and allows the models to be estimated by means of standard Gibbs sampling methods. When applied to monthly time series on growth in industrial production and inflation, we find strong evidence that the introduction of MIDAS effects in the volatility equation leads to improved in-sample and out-of-sample density forecasts. Our results also suggest that model combination schemes assign high weight to MIDAS-in-volatility models and produce consistent gains in out-of-sample predictive performance.",
keywords = "Bayesian estimation, Industrial production, Inflation forecasts, MIDAS regressions, Out-of-sample forecasts, Stochastic volatility",
author = "Davide Pettenuzzo and Allan Timmermann and Rossen Valkanov",
year = "2016",
month = aug,
day = "1",
doi = "10.1016/j.jeconom.2016.04.009",
language = "English",
volume = "193",
pages = "315--334",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier BV",
number = "2",

}

RIS

TY - JOUR

T1 - A MIDAS approach to modeling first and second moment dynamics

AU - Pettenuzzo, Davide

AU - Timmermann, Allan

AU - Valkanov, Rossen

PY - 2016/8/1

Y1 - 2016/8/1

N2 - We propose a new approach to predictive density modeling that allows for MIDAS effects in both the first and second moments of the outcome. Specifically, our modeling approach allows for MIDAS stochastic volatility dynamics, generalizing a large literature focusing on MIDAS effects in the conditional mean, and allows the models to be estimated by means of standard Gibbs sampling methods. When applied to monthly time series on growth in industrial production and inflation, we find strong evidence that the introduction of MIDAS effects in the volatility equation leads to improved in-sample and out-of-sample density forecasts. Our results also suggest that model combination schemes assign high weight to MIDAS-in-volatility models and produce consistent gains in out-of-sample predictive performance.

AB - We propose a new approach to predictive density modeling that allows for MIDAS effects in both the first and second moments of the outcome. Specifically, our modeling approach allows for MIDAS stochastic volatility dynamics, generalizing a large literature focusing on MIDAS effects in the conditional mean, and allows the models to be estimated by means of standard Gibbs sampling methods. When applied to monthly time series on growth in industrial production and inflation, we find strong evidence that the introduction of MIDAS effects in the volatility equation leads to improved in-sample and out-of-sample density forecasts. Our results also suggest that model combination schemes assign high weight to MIDAS-in-volatility models and produce consistent gains in out-of-sample predictive performance.

KW - Bayesian estimation

KW - Industrial production

KW - Inflation forecasts

KW - MIDAS regressions

KW - Out-of-sample forecasts

KW - Stochastic volatility

UR - http://www.scopus.com/inward/record.url?scp=84976540202&partnerID=8YFLogxK

U2 - 10.1016/j.jeconom.2016.04.009

DO - 10.1016/j.jeconom.2016.04.009

M3 - Journal article

AN - SCOPUS:84976540202

VL - 193

SP - 315

EP - 334

JO - Journal of Econometrics

JF - Journal of Econometrics

SN - 0304-4076

IS - 2

ER -