CIRM - Videos & books Library - Bayesian econometrics in the Big Data Era

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Auteurs : Frühwirth-Schnatter, Sylvia (Auteur de la conférence)
CIRM (Editeur )

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Bayesian econometrics model-based clustering Markov chain models mixtures-of-experts model time series finite mixture models transition matrix treatment effects prior distribution identifiability questions from the audience

Résumé : Data mining methods based on finite mixture models are quite common in many areas of applied science, such as marketing, to segment data and to identify subgroups with specific features. Recent work shows that these methods are also useful in micro econometrics to analyze the behavior of workers in labor markets. Since these data are typically available as time series with discrete states, clustering kernels based on Markov chains with group-specific transition matrices are applied to capture both persistence in the individual time series as well as cross-sectional unobserved heterogeneity. Markov chains clustering has been applied to data from the Austrian labor market, (a) to understanding the effect of labor market entry conditions on long-run career developments for male workers (Frühwirth-Schnatter et al., 2012), (b) to study mothers' long-run career patterns after first birth (Frühwirth-Schnatter et al., 2016), and (c) to study the effects of a plant closure on future career developments for male worker (Frühwirth-Schnatter et al., 2018). To capture non- stationary effects for the later study, time-inhomogeneous Markov chains based on time-varying group specific transition matrices are introduced as clustering kernels. For all applications, a mixture-of-experts formulation helps to understand which workers are likely to belong to a particular group. Finally, it will be shown that Markov chain clustering is also useful in a business application in marketing and helps to identify loyal consumers within a customer relationship management (CRM) program.

Codes MSC :
62C10 - Bayesian problems; characterization of Bayes procedures
62F15 - Bayesian inference
62H30 - Classification and discrimination; cluster analysis
62M05 - Markov processes: estimation
62M10 - Time series, auto-correlation, regression, etc.
62P20 - Applications of statistics to economics

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Hennenfent, Guillaume

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https://videos.cirm-math.fr/2018-11-28_Fruhwirth-Schnatter.mp4

Informations sur la Rencontre

Nom de la Rencontre : Jean-Morlet chair: Bayesian statistics in the big data era / Chaire Jean-Morlet : Statistiques bayésiennes à l'ère du big data
Organisateurs de la Rencontre : Freyemurth, Jean-Marc ; Marin, Jean-Michel ; Mengersen, Kerrie ; Pommeret, Denys ; Pudlo, Pierre
Dates : 26/11/2018 - 30/11/2018
Année de la rencontre : 2018
URL de la Rencontre : https://www.chairejeanmorlet.com/1912.html

Données de citation

DOI : 10.24350/CIRM.V.19477603
Citer cette vidéo: Frühwirth-Schnatter, Sylvia (2018). Bayesian econometrics in the Big Data Era. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19477603
URI : http://dx.doi.org/10.24350/CIRM.V.19477603

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Bibliographie

Frühwirth-Schnatter, S., Celeux, G., & Robert, C.P. (Eds.). (2018). Chapman and Hall/CRC - https://www.crcpress.com/Handbook-of-Mixture-Analysis/Fruhwirth-Schnatter-Celeux-Robert/p/book/9781498763813

Frühwirth-Schnatter, S., Pittner, S., Weber, A., & Winter-Ebmer, R. (2018). Analysing plant closure effects using time-varying mixture-of-experts Markov chain clustering. The Annals of Applied Statistics, 12(3), 1796-1830 - https://doi.org/10.1214/17-AOAS1132

Frühwirth-Schnatter, S., Pamminger, C., Weber, A. and Winter-Ebmer, R. (2012). Labor market entry and earnings dynamics: Bayesian inference using mixtures‐of‐experts Markov chain clustering. Journal of Applied Econometrics, 27, 1116–1137 - https://doi.org/10.1002/jae.1249

Frühwirth-Schnatter, S. (2006). Finite mixture and Markov switching models. Berlin: Springer - https://doi.org/10.1007/978-0-387-35768-3

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