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Bayesian Analysis of Coefficient Instability in Dynamic Regressions

Directorate General for Economics, Statistics and Research, Banca d’Italia, Via Nazionale 91, 00184 Roma, Italy
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Econometrics 2019, 7(3), 29; https://doi.org/10.3390/econometrics7030029
Received: 2 May 2019 / Revised: 21 June 2019 / Accepted: 25 June 2019 / Published: 28 June 2019
This paper deals with instability in regression coefficients. We propose a Bayesian regression model with time-varying coefficients (TVC) that allows to jointly estimate the degree of instability and the time-path of the coefficients. Thanks to the computational tractability of the model and to the fact that it is fully automatic, we are able to run Monte Carlo experiments and analyze its finite-sample properties. We find that the estimation precision and the forecasting accuracy of the TVC model compare favorably to those of other methods commonly employed to deal with parameter instability. A distinguishing feature of the TVC model is its robustness to mis-specification: Its performance is also satisfactory when regression coefficients are stable or when they experience discrete structural breaks. As a demonstrative application, we used our TVC model to estimate the exposures of S&P 500 stocks to market-wide risk factors: We found that a vast majority of stocks had time-varying exposures and the TVC model helped to better forecast these exposures. View Full-Text
Keywords: coefficients’ instability; TVC model; Bayesian regression; Monte Carlo experiments coefficients’ instability; TVC model; Bayesian regression; Monte Carlo experiments
MDPI and ACS Style

Ciapanna, E.; Taboga, M. Bayesian Analysis of Coefficient Instability in Dynamic Regressions. Econometrics 2019, 7, 29.

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