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

Bonnéry Daniel (), Cheng Yang (), Ha Neung Soo () and Lahiri Partha ()
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Bonnéry Daniel: Joint Program in Survey Methodology, University of Maryland, Maryland, ; United States
Cheng Yang: U.S. Census Bureau, Maryland, ; United States
Ha Neung Soo: Nielsen, Maryland, ; United States
Lahiri Partha: Joint Program in Survey Methodology, University of Maryland, Maryland, ; United States

Statistics in Transition New Series, 2015, vol. 16, issue 4, 511-522

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.

Keywords: complex survey data; empirical distribution function; Monte Carlo Markov Chain; rank; risk; small area estimation (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:stintr:v:16:y:2015:i:4:p:511-522:n:8

DOI: 10.21307/stattrans-2015-030

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