A Bayesian Signals Approach for the Detection of Crises
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A Bayesian Signals Approach for the Detection of Crises Panayotis Michaelides1 · Mike Tsionas2 · Panos Xidonas3
© The Indian Econometric Society 2019
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 Predicting crises · Early warning system · Bayesian analysis · Leading indicators
* Panos Xidonas [email protected] Panayotis Michaelides [email protected] Mike Tsionas [email protected] 1
National Technical University of Athens, Athens, Greece
2
Lancaster University, Lancaster, UK
3
ESSCA Business School, Paris, France
13
Vol.:(0123456789)
Journal of Quantitative Economics
Introduction Lo Duca (2017) estimate that output losses from financial crises in the European Union amount, on average, to 8% of GDP, and Laeven (2012) estimate that during banking crises across a large sample of countries worldwide output losses amounted on average to 23% of GDP. Caprio and Klingebiel (1996) estimated that average bailout amounts to 10% of GDP and cumulative losses in output are, approximately, 5.6% of GDP (Hoggarth et al. 2002). In this work, we adopt the so-called “signals approach” as an Eearly Warning Mechnaism (EWM) to detect such crises and provide a novel Bayesian model which has numerous advantages over the traditional approaches. The signals approach, as an EWM for crises, goes back to the path-breaking paper of Kaminsky et al. (1998), followed by Kaminsky and Reinhart (1999). Its applications include EWMs for: (1) debt crises (see Knedlik and von Schweinitz 2012), (2) asset price bubbles (see Alessi and Detken 2011), (3) banking crises (see Borio and Drehmann 2009), and even (4) currency crises (see Edison 2003). Meanwhile, binary choice models predict a binary crisis variable in the spirit of Frankel and Rose (1996), Berg and Pattillo (1999), Kamin et al. (2001) and Bu
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