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

Daniel Wilson ()

No 2017-13, Working Paper Series from Federal Reserve Bank of San Francisco

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: R11 Q52 J21 G17 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2017-06-14, Revised 2017-06-14
New Economics Papers: this item is included in nep-lma and nep-ure
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Journal Article: Clearing the Fog: The Predictive Power of Weather for Employment Reports and Their Asset Price Responses (2019) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedfwp:2017-13

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DOI: 10.24148/wp2017-13

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