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Weather-adjusting employment data

Author

Listed:
  • Michael D. Boldin
  • Jonathan H. Wright

Abstract

First version: December 18, 2014. This version: January 12, 2015. This paper proposes and implements a statistical methodology for adjusting employment data for the effects of deviation in weather from seasonal norms. This is distinct from seasonal adjustment, which only controls for the normal variation in weather across the year. Unusual weather can distort both the data and the seasonal factors. We control for both of these effects by integrating a weather adjustment step in the seasonal adjustment process. We use several indicators of weather, including temperature, snowfall and hurricanes. Weather effects can be very important, shifting the monthly payrolls change number by more than 100,000 in either direction. The effects are largest in the winter and early spring months and in the construction sector.

Suggested Citation

  • Michael D. Boldin & Jonathan H. Wright, 2015. "Weather-adjusting employment data," Working Papers 15-5, Federal Reserve Bank of Philadelphia.
    • Handle: RePEc:fip:fedpwp:15-5
    as

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    File URL: https://www.philadelphiafed.org/-/media/frbp/assets/working-papers/2015/wp15-05.pdf
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    References listed on IDEAS

    as
    1. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2005. "There is a risk-return trade-off after all," Journal of Financial Economics, Elsevier, vol. 76(3), pages 509-548, June.
    2. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    3. Melissa Dell & Benjamin F. Jones & Benjamin A. Olken, 2012. "Temperature Shocks and Economic Growth: Evidence from the Last Half Century," American Economic Journal: Macroeconomics, American Economic Association, vol. 4(3), pages 66-95, July.
    4. Andreou, Elena & Ghysels, Eric & Kourtellos, Andros, 2010. "Regression models with mixed sampling frequencies," Journal of Econometrics, Elsevier, vol. 158(2), pages 246-261, October.
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    Cited by:

    1. Charles Fries & Francois Gourio, 2018. "Weather Shocks and Climate Change," 2018 Meeting Papers 1159, Society for Economic Dynamics.
    2. Hyunju Kang & Hyunduk Suh & Jongmin Yu, 2019. "Does Air Pollution Affect Consumption Behavior? Evidence from Korean Retail Sales," Asian Economic Journal, East Asian Economic Association, vol. 33(3), pages 235-251, September.
    3. Christopher L. Foote, 2015. "Did abnormal weather affect U.S. employment growth in early 2015?," Current Policy Perspectives 15-2, Federal Reserve Bank of Boston.
    4. Tom Stark, 2015. "First quarters in the national income and product accounts," Research Rap Special Report, Federal Reserve Bank of Philadelphia, issue May.
    5. Erik Haustein & Sven Schreiber, 2016. "Adjusting production indices for varying weather effects," IMK Working Paper 171-2016, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    6. Caglar Yunculer, 2015. "Estimating the Bridging Day Effect on Turkish Industrial Production," CBT Research Notes in Economics 1515, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.

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    More about this item

    Keywords

    Weather; employment data; seasonal adjustment; MIDAS;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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