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Clearing the Fog: The Predictive Power of Weather for Employment Reports and Their Asset Price Responses

Daniel Wilson ()

American Economic Review: Insights, 2019, vol. 1, issue 3, 373-88

Abstract: This paper exploits vast granular data—with over one million county-month observations—to estimate a dynamic panel data model of weather's local employment effects. The fitted county model is then aggregated and used to generate in-sample and rolling out-of-sample (nowcast) estimates of the weather effect on national monthly employment. These nowcasts, which use only employment and weather data available prior to a given employment report, are significantly predictive not only of the surprise component of employment reports but also of stock and bond market returns on the days of employment reports.

JEL-codes: C53 G12 G17 H63 Q54 R23 (search for similar items in EconPapers)
Date: 2019
Note: DOI: 10.1257/aeri.20180432
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Handle: RePEc:aea:aerins:v:1:y:2019:i:3:p:373-88