Taming Thor: A New Approach to Modeling Weather Effects

38 Pages Posted: 7 Oct 2019

See all articles by Brigitte Roth Tran

Brigitte Roth Tran

Federal Reserve Bank of San Francisco

Date Written: January 30, 2018

Abstract

Weather effects are large, non-linear, and heterogeneous. Standard storm and element-based analyses are not designed to study weather effects on the whole. I use the LASSO machine learning method to develop a one-dimensional weather index reflecting how favorable weather conditions on the whole are for specific outcomes and contexts, capturing complex effects without over-fitting data. I illustrate the index with national daily store-level apparel and sporting goods sales. The worst five percent of weather days decrease outdoor store sales 22 percent, compared to 12 percent reductions at indoor malls on their bottom fifth percentile days. A bootstrap with historical weather data indicates that up to one-third of variability in same-store sales growth may be due to weather.

Keywords: LASSO, Machine Learning, Weather, Retail, Variation

JEL Classification: C4, Q54, L8

Suggested Citation

Roth Tran, Brigitte, Taming Thor: A New Approach to Modeling Weather Effects (January 30, 2018). Available at SSRN: https://ssrn.com/abstract=3464678 or http://dx.doi.org/10.2139/ssrn.3464678

Brigitte Roth Tran (Contact Author)

Federal Reserve Bank of San Francisco ( email )

101 Market Street
San Francisco, CA 94105
United States

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