A Bayesian Model for CPI Estimation from Proxy Measurements

Shay Rozen, M.Sc. student at the Department of Industrial Engineering

17 May 2022, 14:30 PM, Room 206& via zoom

Abstract:
We propose a novel Bayesian model to estimate the Consumer Price Index (CPI) using online price measurements. A collection of online prices from Israeli grocery chains and from the Israeli wholesale market for fresh produce datasets has been assembled. We designed a Bayesian model for predicting the price change of each CPI item separately as well as the entire aggregated index. Our model is optimized using Variational Bayes through an iterative process. We compared our model to various models showing its higher accuracy with respect to classical approaches for CPI forecasting.

Bio:
Shay Rozen, M.Sc. student at the department of Industrial Engineering in Tel Aviv University, specializing in Data Science. Shay holds a B.Sc degree in Industrial Engineering from Tel Aviv University, and serves as head of data analysis team at the Technological and Logistic Directorate in the IDF. The research has been supervised by Dr. Noam Koenigshtein.