We argue that comprehensive out-of-sample (OOS) evaluation using statistical decision theory (SDT) should replace the current practice of K-fold and Common Task Framework validation in machine learning (ML) research. SDT provides a formal framework for performing comprehensive OOS evaluation...

This paper studies the predictability of ultra high-frequency stock returns and durations to relevant price, volume and transactions events, using machine learning methods. We find that, contrary to low frequency and long horizon returns, where predictability is rare and inconsistent,...

We propose a statistical model of differences in beliefs in which heterogeneous investors are represented as different machine learning model specifications. Each investor forms return forecasts from their own specific model using data inputs that are available to all investors. We measure...

The substantial fluctuations in oil prices in the wake of the COVID-19 pandemic and the Russian invasion of Ukraine have highlighted the importance of tail events in the global market for crude oil which call for careful risk assessment. In this paper we focus on forecasting tail risks in the...

We propose a new tool to filter non-linear dynamic models that does not require the researcher to specify the model fully and can be implemented without solving the model. If two conditions are satisfied, we can use a flexible statistical model and a known measurement equation to back out the...

Aggregate and sectoral comovement are central features of business cycle data. Therefore, the ability to generate comovement is a natural litmus test for macroeconomic models. But it is a test that most existing models fail. In this paper we propose a unified model that generates both aggregate...

Forecasts of the rate of price inflation play a central role in the formulation of monetary policy, and forecasting inflation is a key job for economists at the Federal Reserve Board. This paper examines whether this job has become harder and, to the extent that it has, what changes in the...

We develop equilibrium models of an exhaustible resource market where both prices and extraction choices are determined endogenously. Our analysis highlights a role for adjustment costs in generating price dynamics that are consistent with observed oil and gas forward prices as well as with the...

This paper provides a general framework for integration of high-frequency intraday data into the measurement forecasting of daily and lower frequency volatility and return distributions. Most procedures for modeling and forecasting financial asset return volatilities, correlations, and...

We look at the effects of rainfall forecasts and realized rainfall on equilibrium agricultural wages over the course of the agricultural production cycle. We show theoretically that a forecast of good weather can lower wages in the planting stage, by lowering ex ante out-migration, and can...