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Bayesian Bi-level Sparse Group Regressions for Macroeconomics Forecasting


Speaker:Anna Simoni, CNRS/CREST-ENSAE
Date: Tuesday 28 March 2023
Time: 13.30-15.00
Location: Queens Digital Humanities Seminar Room 2

Further details

This paper considers forecasting models for macroeconomic aggregates in a data rich environment where predictors are organized in groups. Variables in each group are characterized by strong covariation. Due to the large dimension, some groups and some predictors in a group might be not relevant to forecast the target variable, conditional on the remaining groups and predictors in the same group. In this paper we take advantage of this group structure by constructing a hierarchical prior distribution that treats the coefficients of each block independently, imposes correlation among the coefficients in each block and induces bi-level sparsity. We show that this prior is computationally convenient. We demonstrate the theoretical validity of our Bayesian procedure from a frequentist point of view and show that it attains the optimal rate of convergence. The setting is extended further to take into account stochastic volatility. Finally, we illustrate finite sample properties of our procedure through Monte Carlo experiments and analyse a real data forecasting problem.