Department of Economics and Business Economics

Forecasting Macroeconomic Variables Under Model Instability

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

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Forecasting Macroeconomic Variables Under Model Instability. / Pettenuzzo, Davide; Timmermann, Allan.

In: Journal of Business and Economic Statistics, Vol. 35, No. 2, 03.04.2017, p. 183-201.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Harvard

Pettenuzzo, D & Timmermann, A 2017, 'Forecasting Macroeconomic Variables Under Model Instability', Journal of Business and Economic Statistics, vol. 35, no. 2, pp. 183-201. https://doi.org/10.1080/07350015.2015.1051183

APA

Pettenuzzo, D., & Timmermann, A. (2017). Forecasting Macroeconomic Variables Under Model Instability. Journal of Business and Economic Statistics, 35(2), 183-201. https://doi.org/10.1080/07350015.2015.1051183

CBE

Pettenuzzo D, Timmermann A. 2017. Forecasting Macroeconomic Variables Under Model Instability. Journal of Business and Economic Statistics. 35(2):183-201. https://doi.org/10.1080/07350015.2015.1051183

MLA

Pettenuzzo, Davide and Allan Timmermann. "Forecasting Macroeconomic Variables Under Model Instability". Journal of Business and Economic Statistics. 2017, 35(2). 183-201. https://doi.org/10.1080/07350015.2015.1051183

Vancouver

Pettenuzzo D, Timmermann A. Forecasting Macroeconomic Variables Under Model Instability. Journal of Business and Economic Statistics. 2017 Apr 3;35(2):183-201. https://doi.org/10.1080/07350015.2015.1051183

Author

Pettenuzzo, Davide ; Timmermann, Allan. / Forecasting Macroeconomic Variables Under Model Instability. In: Journal of Business and Economic Statistics. 2017 ; Vol. 35, No. 2. pp. 183-201.

Bibtex

@article{5da1bac2e7aa4f6e9d980351b6456ab9,
title = "Forecasting Macroeconomic Variables Under Model Instability",
abstract = "We compare different approaches to accounting for parameter instability in the context of macroeconomic forecasting models that assume either small, frequent changes versus models whose parameters exhibit large, rare changes. An empirical out-of-sample forecasting exercise for U.S. gross domestic product (GDP) growth and inflation suggests that models that allow for parameter instability generate more accurate density forecasts than constant-parameter models although they fail to produce better point forecasts. Model combinations deliver similar gains in predictive performance although they fail to improve on the predictive accuracy of the single best model, which is a specification that allows for time-varying parameters and stochastic volatility. Supplementary materials for this article are available online.",
keywords = "Change-point models, GDP growth forecasts, Inflation forecasts, Regime switching, Stochastic volatility, Time-varying parameters",
author = "Davide Pettenuzzo and Allan Timmermann",
year = "2017",
month = apr,
day = "3",
doi = "10.1080/07350015.2015.1051183",
language = "English",
volume = "35",
pages = "183--201",
journal = "Journal of Business and Economic Statistics",
issn = "0735-0015",
publisher = "Taylor & Francis Inc.",
number = "2",

}

RIS

TY - JOUR

T1 - Forecasting Macroeconomic Variables Under Model Instability

AU - Pettenuzzo, Davide

AU - Timmermann, Allan

PY - 2017/4/3

Y1 - 2017/4/3

N2 - We compare different approaches to accounting for parameter instability in the context of macroeconomic forecasting models that assume either small, frequent changes versus models whose parameters exhibit large, rare changes. An empirical out-of-sample forecasting exercise for U.S. gross domestic product (GDP) growth and inflation suggests that models that allow for parameter instability generate more accurate density forecasts than constant-parameter models although they fail to produce better point forecasts. Model combinations deliver similar gains in predictive performance although they fail to improve on the predictive accuracy of the single best model, which is a specification that allows for time-varying parameters and stochastic volatility. Supplementary materials for this article are available online.

AB - We compare different approaches to accounting for parameter instability in the context of macroeconomic forecasting models that assume either small, frequent changes versus models whose parameters exhibit large, rare changes. An empirical out-of-sample forecasting exercise for U.S. gross domestic product (GDP) growth and inflation suggests that models that allow for parameter instability generate more accurate density forecasts than constant-parameter models although they fail to produce better point forecasts. Model combinations deliver similar gains in predictive performance although they fail to improve on the predictive accuracy of the single best model, which is a specification that allows for time-varying parameters and stochastic volatility. Supplementary materials for this article are available online.

KW - Change-point models

KW - GDP growth forecasts

KW - Inflation forecasts

KW - Regime switching

KW - Stochastic volatility

KW - Time-varying parameters

U2 - 10.1080/07350015.2015.1051183

DO - 10.1080/07350015.2015.1051183

M3 - Journal article

AN - SCOPUS:85014996668

VL - 35

SP - 183

EP - 201

JO - Journal of Business and Economic Statistics

JF - Journal of Business and Economic Statistics

SN - 0735-0015

IS - 2

ER -