Quotation Kastner, Gregor. 2020. Efficient Bayesian Computing in Many Dimensions - Applications in Economics and Finance. BAYESCOMP 2020, Gainesville, Vereinigte Staaten/USA, 07.01.-10.01. Invited Talk


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Abstract

Statistical inference for dynamic models in high dimensions often comes along with a huge amount of parameters that need to be estimated. Thus, to handle the curse of dimensionality, suitable regularization methods are of prime importance, and efficient computational tools are required to make practical estimation feasible. In this talk, we exemplify how these two principles can be implemented for models of importance in macroeconomics and finance. First, we discuss a Bayesian vector autoregressive (VAR) model with time-varying contemporaneous correlations that is capable of handling vast dimensional information sets. Second, we propose a straightforward algorithm to carry out inference in large dynamic regression settings with mixture innovation components for each coefficient in the system.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Paper presented at an academic conference or symposium
Language English
Title Efficient Bayesian Computing in Many Dimensions - Applications in Economics and Finance
Event BAYESCOMP 2020
Year 2020
Date 07.01.-10.01.
Country United States/USA
Location Gainesville
URL http://users.stat.ufl.edu/~jhobert/BayesComp2020/Conf_Website/
Invited Talk Y

Associations

Projects
High-dimensional statistical learning: New methods to advance economic and sustainability policies
People
Kastner, Gregor (Details)
Organization
Institute for Statistics and Mathematics IN (Details)
Research areas (Ă–STAT Classification 'Statistik Austria')
1105 Computer software (Details)
1162 Statistics (Details)
5323 Econometrics (Details)
5701 Applied statistics (Details)
5707 Time series analysis (Details)
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