Time series generalization of Fay-Herriot estimation for small areas: CS11-613/91-18E-PDF
"In estimation for small areas it is common to borrow strength from other small areas since the direct survey estimates often have large sampling variability. A class of methods called composite estimation addresses the problem by using a linear combination of direct and synthetic estimators. The synthetic component is based on a model which connects small area means cross-sectionally (over areas) and/or over time. The Fay-Herriot estimator is a composite estimator which provides empirical best linear unbiased predictors for cross-sectional data under a linear regression model with uricorrelated small area effects. In this paper we consider three models to generalize Fay-Herriot estimation to more than one time point. In the first model, regression parameters are random and serially dependent but the small area effects are assumed to be independent over time. In the second model, regression parameters are nonrandom and may take common values over time but the small area effects are serially dependent. The third model is more general in that regression parameters and small area effects are assumed to be serially dependent"--Abstract.
|Department/Agency||Statistics Canada. Social Survey Methods Division.|
|Title||Time series generalization of Fay-Herriot estimation for small areas|
|Series Title||Working paper ;|
|Publication Type||Series - View Master Record|
|Electronic Document|| |
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|Note||"Working paper no. SSMD 91-018E." Digitized edition from print [produced by Statistics Canada].|
|Number of Pages||23 p. :|
|Departmental Catalogue Number||11-613E|
|Subject Terms||Surveys, Methodology|
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