We develop empirical models of hedonic prices and derive hedonic indices for measuring changes in customer welfare based upon deep learning. We first generate abstract product attributes, or "features," from text descriptions and images using deep neural networks, and then use these attributes...

Accurate, real-time measurements of price index changes using electronic records are essential for tracking inflation and productivity in today's economic environment. We develop empirical hedonic models that can process large amounts of unstructured product data (text, images, prices,...

We show that hedonic price indexes may be biased when not all product characteristics are observed. We derive two primary sources of bias. The first is a classical selection problem that arises due to changes over time in the values of unobserved characteristics. The second comes from changes in...

We show that hedonic price indexes may be biased when not all product characteristics are observed. We derive two primary sources of bias. The first is a classical selection problem that arises due to changes over time in the values of unobserved characteristics. The second comes from changes in...

We show that hedonic price indexes may be biased when not all product characteristics are observed. We derive two primary sources of bias. The first is a classical selection problem that arises due to changes over time in the values of unobserved characteristics. The second comes from changes in...