Small Area Prediction Under Alternative Model Specifications

Erciulescu Andreea L. and Fuller Wayne A.
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Erciulescu Andreea L.: National Institute of Statistical Sciences and USDA NASS, 1400 Independence Ave. SW, Room 6040 F, Washington, DC, 20250 United States
Fuller Wayne A.: Iowa State University, 214 Department of Statistics, Ames, IA, United States

Statistics in Transition New Series, 2016, vol. 17, issue 1, 9-24

Abstract: Construction of small area predictors and estimation of the prediction mean squared error, given different types of auxiliary information are illustrated for a unit level model. Of interest are situations where the mean and variance of an auxiliary variable are subject to estimation error. Fixed and random specifications for the auxiliary variables are considered. The efficiency gains associated with the random specification for the auxiliary variable measured with error are demonstrated. A parametric bootstrap procedure is proposed for the mean squared error of the predictor based on a logit model. The proposed bootstrap procedure has smaller bootstrap error than a classical double bootstrap procedure with the same number of samples.

Keywords: unit level model; parametric bootstrap; double bootstrap; measurement error; auxiliary information (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:stintr:v:17:y:2016:i:1:p:9-24:n:2

DOI: 10.21307/stattrans-2016-003

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