Online Program Home
  My Program

Abstract Details

Activity Number: 232 - From Ranch to Rain: Government Statistics on Agriculture and Weather
Type: Contributed
Date/Time: Monday, July 31, 2017 : 2:00 PM to 3:50 PM
Sponsor: Government Statistics Section
Abstract #322484 View Presentation
Title: Stochastic Seasonality, Contemporaneous Inference, and Forecasting in the Presence of More Volatile Weather
Author(s): William Wei-Choun Yu* and Jerry Nickelsburg
Companies: UCLA Anderson School of Management and UCLA Anderson School of Management
Keywords: seasonal adjustment ; stochastic seasonality ; forecasting ; inference ; temperature ; weather
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.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2017 program

 
 
Copyright © American Statistical Association