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

• Autorzy: Daniel Bonnéry , Yung Rheng , Neung Soo Ha , Partha Luhiri
• Języki publikacji: EN

Abstrakty

EN

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. (original abstract)

Słowa kluczowe

EN

Small area estimates; Markov chain; Monte Carlo simulation; Risk; Data analysis

PL

Statystyka małych obszarów; Łańcuch Markowa; Symulacja Monte Carlo; Ryzyko; Analiza danych

• Czasopismo: Statistics in Transition
• Rocznik: 2015
• Tom: 16
• Numer: nr 4 The Measurement of Subjective Well-Being in Survey Research
• Strony: 511--522

Twórcy

autor

Daniel Bonnéry

• University of Maryland

autor

Yung Rheng

• U.S. Census Bureau

autor

Neung Soo Ha

• Nielsen

autor

Partha Luhiri

• University of Maryland

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