Stochastic Seasonality, Contemporaneous Inference, and Forecasting in the Presence of Volatile Weather

49 Pages Posted: 21 Jul 2014 Last revised: 2 Sep 2017

See all articles by Jerry Nickelsburg

Jerry Nickelsburg

University of California, Los Angeles (UCLA) - Anderson Forecast

William Yu

UCLA Anderson School of Management, Anderson Forecast

Date Written: July 30, 2017

Abstract

Contemporaneous inference from economic data releases for policy and business decisions has become increasingly relevant in the high pace of the information age. The released data are typically filtered to eliminate seasonal patterns to reveal underlying trends and cycles. The nature of economic seasonal behavior is such that average seasonality, not actual seasonality, is filtered from the data. First, the paper suggests adjustments of the inference accounts for the stochastic seasonality. We formalize the issue and present a simple method to the informal inferential practice. Second, we provide a data-based method that allows for temperature adjustment to improve forecasting outcomes. With the assumption of climate change taking place, these methods are particularly important as weather patterns become more volatile.

Keywords: Seasonal adjustment, Stochastic seasonality, Forecasting, Inference, Temperature, Weather

JEL Classification: C1, C53, E37

Suggested Citation

Nickelsburg, Jerry and Yu, Wei-Choun, Stochastic Seasonality, Contemporaneous Inference, and Forecasting in the Presence of Volatile Weather (July 30, 2017). Available at SSRN: https://ssrn.com/abstract=2468626 or http://dx.doi.org/10.2139/ssrn.2468626

Jerry Nickelsburg

University of California, Los Angeles (UCLA) - Anderson Forecast ( email )

110 Westwood Plaza, Suite C525
Los Angeles, CA 90095-1481
United States

Wei-Choun Yu (Contact Author)

UCLA Anderson School of Management, Anderson Forecast ( email )

110 Westwood Plaza
Los Angeles, CA 90095-1481
United States
310-825-7805 (Phone)

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