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Author Pfeffermann, Danny ♦ Tiller, Richard
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Possible Model Breakdown ♦ State Level ♦ Traditional Direct Survey ♦ Reliable Estimate ♦ Benchmark Constraint ♦ Filtering Algorithm ♦ Small Area Estimation ♦ Model Dependent Estimate ♦ Unemployment Estimate ♦ Labor Statistic ♦ Benchmarking Process ♦ State-space Model ♦ Several State ♦ Survey Estimate ♦ Correlated Measurement ♦ Correlated Measurement Error ♦ Benchmarked Estimator ♦ Previous Survey ♦ Valid Estimator ♦ Direct Estimator ♦ State-space Modelling ♦ Monthly Employment ♦ Joint Modeling ♦ Appropriate Time Series Cross-sectional Model ♦ Direct Survey ♦ Real Unemployment Series
Description The problem of Small Area Estimation is how to produce reliable estimates of area (domain) characteristics, when the sizes within the areas are too small to warrant the use of traditional direct survey estimates. This problem is commonly tackled by borrowing information from either neighboring areas and/or from previous surveys, using appropriate time series/cross-sectional models. In order to protect against possible model breakdowns and for other reasons, it is often required to benchmark the model dependent estimates to the corresponding direct survey estimates in larger areas, for which the survey estimates are sufficiently accurate. The benchmarking process defines another way of borrowing information across the areas. This article shows how benchmarking can be implemented with the state-space models used by the Bureau of Labor Statistics in the U.S. for the production of the monthly employment and unemployment estimates at the state level. The computation of valid estimators for the variances of the benchmarked estimators requires joint modeling of the direct estimators in several states, which in turn requires the development of a filtering algorithm for state-space models with correlated measurement errors. No such algorithm has been developed so far. The application of the proposed procedure is illustrated using real unemployment series.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2003-01-01
Publisher Institution Institute, University of Southampton