Data, data, data…. Economists know their importance well, especially when it comes to
monitoring macroeconomic conditions—the basis for making informed economic and policy …

We show that machine learning methods, in particular, extreme trees and neural networks
(NNs), provide strong statistical evidence in favor of bond return predictability. NN forecasts …

Research into predictive accuracy testing remains at the forefront of the forecasting field.
One reason for this is that rankings of predictive accuracy across alternative models, which …

In this paper, we extract latent factors from a large cross‐section of commodity prices,
including fuel and non‐fuel commodities. We decompose each commodity price series into …

Most existing macro-finance term structure models (MTSMs) appear incompatible with
regression evidence of unspanned macro risk. This “spanning puzzle” appears to invalidate …

This paper studies Quasi Maximum Likelihood estimation of Dynamic Factor Models for
large panels of time series. Specifically, we consider the case in which the autocorrelation of …

Dynamic factor models have been the main “big data” tool used by empirical
macroeconomists during the last 30 years. In this context, Kalman filter and smoothing (KFS) …

Are macroeconomic releases important drivers of Treasury bond yields? We develop a two-
step regression strategy that fully exploits the available high-frequency market reaction data …

The dynamic behavior of the term structure of interest rates is difficult to replicate with
models, and even models with a proven track record of empirical performance have …

This survey looks at the literature on factor extraction in the context of Dynamic Factor
Models (DFMs) fitted to multivariate systems of economic and financial variables. Many of …