Professor Davide Pettenuzzo, Brandeis University

"Conditional Forecasts in Large Bayesian VARs with Multiple Soft and Hard Constraints"
Friday, 5 May. 3 pm
Room 305, Main Building.

Abstract

Conditional forecasts, i.e. projections of a set of variables of interest on the future paths of some other variables, are used routinely by empirical macroeconomists in a number of applied settings. In spite of this, the existing algorithms used to generate conditional forecasts tend to be very computationally intensive, especially when working with large Vector Autoregressions or when multiple soft and hard constraints are imposed at once. We introduce a novel precision-based sampler that is fast, scales well, and yields conditional forecasts from both hard and soft constraints. We show in a simulation study that the proposed method produces forecasts that are identical to those from the existing algorithms but in a fraction of the time. We then illustrate the performance of our method in a large Bayesian Vector Autoregression where we simultaneously impose a mix of soft and hard constraints on the future trajectories of key US macroeconomic indicators over the 2020–2022 period.

Bio

Davide Pettenuzzo is a Professor of Financial Econometrics at the Brandeis International Business School. His research interests include time-series econometrics, Bayesian econometrics, asset allocation, portfolio optimization, and econometric methods in finance. He serves as an associate editor for the Journal of Financial Econometrics.

For further information, please get in touch with business-school-research@glasgow.ac.uk. 

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First published: 27 March 2023

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