Triple-goal Estimation of Unemployment Rates for U.S. States Using the U.S. Current Population Survey data Cover Image

Triple-goal Estimation of Unemployment Rates for U.S. States Using the U.S. Current Population Survey data
Triple-goal Estimation of Unemployment Rates for U.S. States Using the U.S. Current Population Survey data

Author(s): Daniel Bonnéry, Partha Lahiri, Yang Cheng, Neung Soo Ha
Subject(s): Economy
Published by: Główny Urząd Statystyczny
Keywords: complex survey data; empirical distribution function; Monte Carlo Markov Chain; rank; risk; small area estimation

Summary/Abstract: In this paper, we first develop a triple-goal small area estimation methodology for simultaneous estimation of unemployment rates for U.S. states using the Current Population Survey (CPS) data and a two-level random sampling variance normal model. The main goal of this paper is to illustrate the utility of the triple-goal methodology in generating a single series of unemployment rate estimates for three separate purposes: developing estimates for individual small area means, producing empirical distribution function (EDF) of true small area means, and the ranking of the small areas by true small area means. We achieve our goal using a Monte Carlo simulation experiment and a real data analysis.

  • Issue Year: 16/2015
  • Issue No: 4
  • Page Range: 511-522
  • Page Count: 11
  • Language: English