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A Bayesian Signals Approach for the Detection of Crises

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A Bayesian Signals Approach for the Detection of Crises. / Michaelides, P.; Tsionas, M.; Xidonas, P.
In: Journal of Quantitative Economics, Vol. 18, No. 3, 30.09.2020, p. 551-585.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Michaelides, P, Tsionas, M & Xidonas, P 2020, 'A Bayesian Signals Approach for the Detection of Crises', Journal of Quantitative Economics, vol. 18, no. 3, pp. 551-585. https://doi.org/10.1007/s40953-019-00186-8

APA

Michaelides, P., Tsionas, M., & Xidonas, P. (2020). A Bayesian Signals Approach for the Detection of Crises. Journal of Quantitative Economics, 18(3), 551-585. https://doi.org/10.1007/s40953-019-00186-8

Vancouver

Michaelides P, Tsionas M, Xidonas P. A Bayesian Signals Approach for the Detection of Crises. Journal of Quantitative Economics. 2020 Sept 30;18(3):551-585. Epub 2019 Nov 1. doi: 10.1007/s40953-019-00186-8

Author

Michaelides, P. ; Tsionas, M. ; Xidonas, P. / A Bayesian Signals Approach for the Detection of Crises. In: Journal of Quantitative Economics. 2020 ; Vol. 18, No. 3. pp. 551-585.

Bibtex

@article{f3c586c2b6634427a850502d8c0f42d4,
title = "A Bayesian Signals Approach for the Detection of Crises",
abstract = "In this paper, we consider the signals approach as an early-warning-system to detect crises. Crisis detection from a signals approach involves Type I and II errors which are handled through a utility function. We provide a Bayesian model and we test the effectiveness of the signals approach in three data sets: (1) Currency and banking crises for 76 currency and 26 banking crises in 15 developing and 5 industrial countries between 1970 and 1995, (2) costly asset price booms using quarterly data ranging from 1970 to 2007, and (3) public debt crises in Europe in 11 countries in the European Monetary Union from the introduction of the Euro until November 2011. The Bayesian model relies on a vector autoregression for indicator variables, and incorporates dynamic factors, time-varying weights in the latent composite indicator and special priors to avoid the proliferation of parameters. The Bayesian vector autoregressions are extended to a semi-parametric context to capture non-linearities. Our evidence reveals that our approach is successful as an early-warning mechanism after allowing for breaks and nonlinearities and, perhaps more importantly, the composite indicator is better represented as a flexible nonlinear function of the underlying indicators.",
keywords = "Bayesian analysis, Early warning system, Leading indicators, Predicting crises",
author = "P. Michaelides and M. Tsionas and P. Xidonas",
year = "2020",
month = sep,
day = "30",
doi = "10.1007/s40953-019-00186-8",
language = "English",
volume = "18",
pages = "551--585",
journal = "Journal of Quantitative Economics",
number = "3",

}

RIS

TY - JOUR

T1 - A Bayesian Signals Approach for the Detection of Crises

AU - Michaelides, P.

AU - Tsionas, M.

AU - Xidonas, P.

PY - 2020/9/30

Y1 - 2020/9/30

N2 - In this paper, we consider the signals approach as an early-warning-system to detect crises. Crisis detection from a signals approach involves Type I and II errors which are handled through a utility function. We provide a Bayesian model and we test the effectiveness of the signals approach in three data sets: (1) Currency and banking crises for 76 currency and 26 banking crises in 15 developing and 5 industrial countries between 1970 and 1995, (2) costly asset price booms using quarterly data ranging from 1970 to 2007, and (3) public debt crises in Europe in 11 countries in the European Monetary Union from the introduction of the Euro until November 2011. The Bayesian model relies on a vector autoregression for indicator variables, and incorporates dynamic factors, time-varying weights in the latent composite indicator and special priors to avoid the proliferation of parameters. The Bayesian vector autoregressions are extended to a semi-parametric context to capture non-linearities. Our evidence reveals that our approach is successful as an early-warning mechanism after allowing for breaks and nonlinearities and, perhaps more importantly, the composite indicator is better represented as a flexible nonlinear function of the underlying indicators.

AB - In this paper, we consider the signals approach as an early-warning-system to detect crises. Crisis detection from a signals approach involves Type I and II errors which are handled through a utility function. We provide a Bayesian model and we test the effectiveness of the signals approach in three data sets: (1) Currency and banking crises for 76 currency and 26 banking crises in 15 developing and 5 industrial countries between 1970 and 1995, (2) costly asset price booms using quarterly data ranging from 1970 to 2007, and (3) public debt crises in Europe in 11 countries in the European Monetary Union from the introduction of the Euro until November 2011. The Bayesian model relies on a vector autoregression for indicator variables, and incorporates dynamic factors, time-varying weights in the latent composite indicator and special priors to avoid the proliferation of parameters. The Bayesian vector autoregressions are extended to a semi-parametric context to capture non-linearities. Our evidence reveals that our approach is successful as an early-warning mechanism after allowing for breaks and nonlinearities and, perhaps more importantly, the composite indicator is better represented as a flexible nonlinear function of the underlying indicators.

KW - Bayesian analysis

KW - Early warning system

KW - Leading indicators

KW - Predicting crises

U2 - 10.1007/s40953-019-00186-8

DO - 10.1007/s40953-019-00186-8

M3 - Journal article

VL - 18

SP - 551

EP - 585

JO - Journal of Quantitative Economics

JF - Journal of Quantitative Economics

IS - 3

ER -