We survey and apply several techniques from the statistical and computer science literature
to the problem of demand estimation. To improve out-of-sample prediction accuracy, we …

This paper describes the pricing strategy model deployed at Airbnb, an online marketplace
for sharing home and experience. The goal of price optimization is to help hosts who share …

Using methods from machine learning we show that fundamentals from simple exchange
rate models (PPP or UIRP) or Taylor-rule based models lead to improved exchange rate …

Abstract Most soils in Sub-Saharan Africa (SSA) are substantially degraded and are in need
of restoration to enhance sustainable food production. This is a harder problem given that …

We provide a microeconomic framework for decision trees: a popular machine learning
method. Specifically, we show how decision trees represent a non-compensatory decision …

Historically, exchange rate forecasting models have exhibited poor out-of-sample
performances and were inferior to the random walk model. Monthly panel data from 1973 to …

Many leading e-commerce retailers adopt a consistent pricing strategy to build customer
trust and promote just a small portion of their catalog each week. Promotion optimization for …

This paper presents a methodology for identifying groups of products that exhibit similar
patterns in demand and responsiveness to changes in price using store-level sales data. We …

Intermittent demand patterns are commonly present in business aircraft spare parts supply
chains. Because of the infrequent arrivals and large variations in demand, aircraft …

The proliferation of rich consumer-level datasets has led to the rise of the" algorithmic
modeling culture"[2] wherein analysts treat the statistical model as a" black box" and predict …