This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 471
Type: Contributed
Date/Time: Wednesday, August 4, 2010 : 8:30 AM to 10:20 AM
Sponsor: Section on Government Statistics
Abstract - #308522
Title: Studying Simulated Mass Layoff Events and Employment/Unemployment Data with Factor Analysis, Multiple Regression, and Bayesian Methods
Author(s): Zhe (Jason) Liu*+ and Mack Shelley
Companies: Iowa State University and Iowa State University
Address: 3210 Lettie ST, Ames, IA, 50014,
Keywords: Factor Analysis ; Multiple Regression ; Bayesian Analysis ; MCMC
Abstract:

Mass Layoff Events are the total number of events that have at least 20 initial claims for unemployment insurance (UI) filed against a particular establishment during a 5-week period. Mass Layoff Statistics (MLS) are automatically collected and can be used to help policymakers get timely information of current employment/unemployment status. Since real MLS data were not allowed to be used here for reasons of confidentiality, simulated data were generated with Markov Chain Monte Carlo methods. Confirmatory and exploratory factor analysis was used to find latent factors throughout different industries. Multiple regression was used to estimate the relationship between MLS and employment/unemployment data. Bayesian methods were used to include the information of previous research and the experience of researchers.


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