-
AEA 20192019We examine the impact of "big data" on firm performance in the context of forecast accuracy using proprietary retail sales data obtained from Amazon. We measure the accuracy of forecasts in two relevant dimensions: the number of products (N), and the number of time periods for which a product is available for sale (T). Theory suggests diminishing returns to larger N and T, with relative forecast errors
-
Games & Economic Behavior2018We propose to smooth out the calibration score, which measures how good a forecaster is, by combining nearby forecasts. While regular calibration can be guaranteed only by randomized forecasting procedures, we show that smooth calibration can be guaranteed by deterministic procedures. As a consequence, it does not matter if the forecasts are leaked, i.e., made known in advance: smooth calibration can nevertheless
Related content
-
October 10, 2023The system has expanded from generating peak computation-load forecasts one year in advance to a series of forecasts that include per-minute forecasts several months into the future. -
October 5, 2023Wharton professor Jessie Handbury lends her expertise to Amazon’s PXTCS Team as an Amazon Visiting Academic.
-
September 19, 2023The new Fulfillment by Amazon system empowers sellers to have more transparency and control over their capacity within Amazon’s fullfilment network by applying market-based principles.
-
June 22, 2023Kanoria and coauthors honored for their paper narrowing the gap between theoretical understanding and practical experience in matching markets.
-
June 2, 2023In a plenary talk, the Berkeley professor and Distinguished Amazon Scholar will argue that AI research should borrow concepts from economics and focus on social collectives.
-
April 14, 2023The requirement that at any given time, all customers see the same prices for the same products necessitates innovation in the design of A/B experiments.
