Forecast combinations have flourished remarkably in the forecasting community and, in
recent years, have become part of mainstream forecasting research and activities …

The Bayesian statistical paradigm provides a principled and coherent approach to
probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting …

Economists both failed to predict the global financial crisis and underestimated its
consequences for the broader economy. Focusing on the second of these failures, this …

This chapter provides an overview of solution and estimation techniques for dynamic
stochastic general equilibrium models. We cover the foundations of numerical …

We compute a sunspot equilibrium in an estimated small-scale New Keynesian model with a
zero lower bound (ZLB) constraint on nominal interest rates and a full set of stochastic …

In this chapter, we discuss a number of challenges for structural macroeconomic models in
the light of the Great Recession and its aftermath. It shows that a benchmark DSGE model …

We discuss model and forecast combination in time series forecasting. A foundational
Bayesian perspective based on agent opinion analysis theory defines a new framework for …

We develop the first agent-based model (ABM) that can compete with benchmark VAR and
DSGE models in out-of-sample forecasting of macro variables. Our ABM for a small open …

If the links between credit markets and real economy tighten in a crisis, financial indicators
might be particularly useful in forecasting the macroeconomic outcomes associated with …

We use machine learning to uncover regularities in the initial play of matrix games. We first
train a prediction algorithm on data from past experiments. Examining the games where our …