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AUEB STATS SEMINARS 21/2/2019:  High-Dimensional Macroeconomic Forecasting Using Message Passing Algorithms by Dimitris Korobilis Empty AUEB STATS SEMINARS 21/2/2019: High-Dimensional Macroeconomic Forecasting Using Message Passing Algorithms by Dimitris Korobilis

Sun 17 Feb 2019 - 10:54
ΚΥΚΛΟΣ ΣΕΜΙΝΑΡΙΩΝ ΣΤΑΤΙΣΤΙΚΗΣ ΦΕΒΡΟΥΑΡΙΟΣ 2019

AUEB STATS SEMINARS 21/2/2019:  High-Dimensional Macroeconomic Forecasting Using Message Passing Algorithms by Dimitris Korobilis Korobi11



Dimitris Korobilis
Essex Business School - University of Essex

High-Dimensional Macroeconomic Forecasting Using Message Passing Algorithms

ΠΕΜΠΤΗ 21/2/2019
13:00 (ακριβώς)

Νέο Κτίριο ΟΠΑ
Τροίας 2, Αίθουσα Τ103

ΠΕΡΙΛΗΨΗ

This paper proposes two distinct contributions to econometric analysis with large information sets and structural instabilities. First, it treats a regression model with time-varying coefficients, stochastic volatility and exogenous predictors, as an equivalent high-dimensional static regression problem with thousands of covariates. Inference in this specification proceeds using standard regression tools such as Bayesian hierarchical priors that shrink many irrelevant coefficients towards either zero or time-invariance. Second, it introduces the framework of factor graphs and message passing inference as a means of designing efficient posterior estimation algorithms. In particular, a Generalized Approximate Message Passing (GAMP) algorithm is derived, and is shown to have very low algorithmic complexity and to be trivially parallelizable. The result is a comprehensive methodology that can be used to estimate time-varying parameter regressions with arbitrarily large number of exogenous predictors. In a forecasting exercise for U.S. price inflation this methodology is shown to work very well.

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