Reliable detection of forecasting skill is slow and resource-intensive. Forecasters need to answer dozens of questions, which may take months or years to resolve. To accelerate this process, we propose the Full Accuracy Score (FAS), a measure that combines the strengths of ground-truth-based...

In revenue management, the customer discrete choice model is essential to describe customer purchase behavior. The multinomial logit (MNL) model is a classical random utility-based choice model that assumes that the consumer can purchase only one product from a set of substitute products. The...

The main aim of this paper is to forecast both in-sample and out-of-sample lithium prices. Specifically, we explore the empirical implications of the present value model for exchange rates, market indexes, mining company prices and related company prices in hi-tech, automotive, electric vehicle...

Many central banks and government agencies use nowcasting techniques to obtain policy relevant information about the business cycle. Existing nowcasting methods, however, have two critical shortcomings for this purpose. First, in contrast to machine-learning models, they do not provide much if...

I evaluate whether incorporating sub-national trends improves macroeconomic fore-casting accuracy in a deep machine learning framework. Specifically, I adopt a computer vision setting by transforming U.S. economic data into a ‘video’ series of geographic ‘images’ and utilizing a...

We propose a framework to study optimal trading policies in a one-tick pro-rata limit order book, as typically arises in short-term interest rate futures contracts. The high-frequency trader chooses to post either market orders or limit orders, which are represented respectively by impulse...

The Nowcasting Lab is an automated code-database-website environment for GDP forecasting. It generates nowcasts and one-quarter ahead forecasts for quarterly GDP growth of the United States, the euro area, and currently 14 other economies using several forecasting models and a large amount of...

This paper compares within-sample and out-of-sample fit of a DSGE model with rational expectations to a model with adaptive learning. The Galí, Smets and Wouters model is the chosen laboratory using quarterly real-time euro area data vintages, covering 2001Q1–2019Q4. The adaptive learning...