External Seminar by Dimitris Koribilis

04-10-2019 from 11:00 to 12:30
Faculty Council Room, Tweekerkenstraat 2, 2nd floor
Department of Economics
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Title: High-Dimensional Macroeconomic Forecasting Using Message Passing Algorithms

By Dimitris Koribilis (University of Glagow)

Please register by Monday 30 September 2019 at https://doodle.com/poll/e83tfrgz525wa6uu


This paper proposes two distinct contributions to econometric analysis of 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 Bayesian hierarchical priors that shrink the high-dimensional vector of coefficients either towards zero or time-invariance. Second, it introduces the frameworks of factor graphs and message passing as a means of designing efficient Bayesian estimation algorithms. In particular, a Generalized Approximate Message Passing (GAMP) algorithm is derived that has low algorithmic complexity and is 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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