Adjusting economic estimates in business surveys

Ugarte, M. D.; Militino, Ana F.; Goicoa, T.

doi:10.1080/02664760802319709

Cited by 2 publications

(2 citation statements)

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“…For example, Ugarte et al . (, ) used bootstrapping to estimate the MSE of benchmarked estimators, derived from linear mixed models with restrictions. González‐Manteiga et al .…”

Section: Mean‐squared Errormentioning

confidence: 99%

“…In this context, bootstrap methods are an attractive alternative that is already used in small area estimation. For example, Ugarte et al (2008Ugarte et al ( , 2009a used bootstrapping to estimate the MSE of benchmarked estimators, derived from linear mixed models with restrictions. González-Manteiga et al (2007) proposed bootstrap methods to estimate the MSE of a small area estimator derived from a logistic mixed model, and Molina et al (2007) adapted the bootstrap to a multinomial mixed model.…”

Section: Mean-squared Errormentioning

confidence: 99%

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Deriving Small Area Estimates from Information Technology Business Surveys

Militino

Ugarte

Goicoa

2015

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary Knowledge of the current state of the art in information and communication technology of businesses (ICTB) is an important issue for governments, markets and policy makers, because information technology improves access to information and plays an important role in firms' competitiveness. Statistical agencies use normalized surveys to provide harmonized statistics about the use of technology in enterprises. Classical design‐based estimators are appropriate for large domains, because direct estimates are consistent and easy to obtain by using sampling weights. However, to supply estimates for unplanned domains, where the sample size is random, model‐based estimators are usually required. In this paper, alternative logistic model‐based estimators are suggested to derive small area estimates from ICTB surveys. Final estimates are benchmarked to achieve coherence with direct estimates in larger domains, and standard errors are given by using bootstrap techniques. A Monte Carlo simulation study is conducted to compare the performance of the small area estimators proposed and to evaluate the behaviour of the mean‐squared error estimator. Results are illustrated with the 2010 ICTB survey of the Basque country (Spain).

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“…For example, Ugarte et al . (, ) used bootstrapping to estimate the MSE of benchmarked estimators, derived from linear mixed models with restrictions. González‐Manteiga et al .…”

Section: Mean‐squared Errormentioning

confidence: 99%