Abstract
This paper proposes a Bayesian regression model with time-varying coefficients (TVC) that makes it possible to estimate jointly the degree of instability and the time-path of regression coefficients. Thanks to its computational tractability, the model proves suitable to perform the first (to our knowledge) Monte Carlo study of the finite-sample properties of a TVC model. Under several specifications of the data generating process, the proposed model’s estimation precision and forecasting accuracy compare favourably with those of other methods commonly used to deal with parameter instability. Furthermore, the TVC model leads to small losses of efficiency under the null of stability and it is robust to mis-specification, providing a satisfactory performance also when regression coefficients experience discrete structural breaks. As a demonstrative application, we use our TVC model to estimate the exposures of S&P 500 stocks to market-wide risk factors: we find that a vast majority of stocks have time-varying risk exposures and that the TVC model helps to forecast these exposures more accurately.
Highlights
Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte
In this subsection we present the results of a set of Monte Carlo simulations aimed at understanding how our time-varying coefficients (TVC) model performs when regression coefficients experience a single discrete structural break
We have proposed a Bayesian regression model with time-varying coefficients (TVC) that has low computational requirements because it allows one to derive analytically the posterior distribution of coefficients, as well as the posterior probability that they are stable
Summary
Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel. Suggested Citation: Ciapanna, Emanuela; Taboga, Marco (2019) : Bayesian analysis of coefficient instability in dynamic regressions, Econometrics, ISSN 2225-1146, MDPI, Basel, Vol. Standard-Nutzungsbedingungen: Terms of use: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen. Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. Directorate General for Economics, Statistics and Research, Banca d’Italia, Via Nazionale 91, 00184 Roma, Italy
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