An Error-Components Model for Prediction of County Crop Areas Using Survey and Satellite Data

Battese, George E.; Harter, Rachel; Fuller, Wayne A.

doi:10.1080/01621459.1988.10478561

Cited by 554 publications

(344 citation statements)

References 14 publications

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“…For comparability with the rest of the methods presented here, in the following we assume that the sampling clusters are the areas. In this case, the model becomes the nested error model of Battese, Harter and Fuller (1988) for the log-transformation of the welfare variables, that is, Y di = log(E di ) is assumed to be linearly related with a p-vector of auxiliary variables x di , which may include unit-specific and area-specific covariates, and includes random area effects u d as follows…”

Section: Disadvantagesmentioning

confidence: 99%

A Comparison of Small Area Estimation Methods for Poverty Mapping

Guadarrama

Molina

Rao

2016

Statistics in Transition New Series

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Poverty maps are an important source of information on the regional distribution of poverty and are currently used to support regional policy making and to allocate funds to local jurisdictions. But obtaining accurate poverty maps at low levels of disaggregation is not straightforward because of insufficient sample size of official surveys in some of the target regions. Direct estimates, obtained with the region-specific sample data, are unstable in the sense of having very large sampling errors for regions with small sample size. Very unstable poverty estimates might make the seemingly poorer regions in one period appear as the richer in the next period, which can be inconsistent. On the other hand, very stable but biased estimates (e.g., too homogeneous across regions) might make identification of the poorer regions difficult. Here we review the main small area estimation methods for poverty mapping. In particular, we consider direct estimation, the Fay-Herriot area level model, the method of Elbers, Lanjouw and Lanjouw (2003) used by the World Bank, the empirical Best/Bayes (EB) method of Molina and Rao (2010) and its extension, the Census EB, and finally the hierarchical Bayes proposal of Molina, Nandram and Rao (2014). We put ourselves in the point of view of a practitioner and discuss, as objectively as possible, the benefits and drawbacks of each method, illustrating some of them through simulation studies. Abstract: Poverty maps are an important source of information on the regional distribution of poverty and are currently used to support regional policy making and to allocate funds to local jurisdictions. But obtaining accurate poverty maps at low levels of disaggregation is not straightforward because of insufficient sample size of official surveys in some of the target regions. Direct estimates, obtained with the region-specific sample data, are unstable in the sense of having very large sampling errors for regions with small sample size. Very unstable poverty estimates might make the seemingly poorer regions in one period appear as the richer in the next period, which can be inconsistent. On the other hand, very stable but biased estimates (e.g., too homogeneous across regions) might make identification of the poorer regions difficult. Here we review the main small area estimation methods for poverty mapping. In particular, we consider direct estimation, the Fay-Herriot area level model, the method of Elbers, Lanjouw and Lanjouw (2003) used by the World Bank, the empirical Best/Bayes (EB) method of Molina and Rao (2010) and its extension, the Census EB, and finally the hierarchical Bayes proposal of Molina, Nandram and Rao (2014). We put ourselves in the point of view of a practitioner and discuss, as objectively as possible, the benefits and drawbacks of each method, illustrating some of them through simulation studies. Keywords

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Section: Disadvantagesmentioning

confidence: 99%

A Comparison of Small Area Estimation Methods for Poverty Mapping

Guadarrama

Molina

Rao

2016

Statistics in Transition New Series

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“…The quantity of interest is usually the population mean of y for the small area, which may be expressed as where and the population means of aj' and fc' for the small-area and where = FjFollowing Prasad and Rao (1986) and Battese and Fuller (1981), we assume that the population size for the area is large, in which case % 0 (i.e., the population is assumed to be infinite).…”

Section: Descriptionmentioning

confidence: 99%

“…Fay and Herriot (1979), Battese and Fuller (1981), Ericksen and Kadane (1985), Stroud (1987), Demp ster and Raghunathan (1987), Prasad and Rao (1986), and Casella and Strang (1987).…”

Section: Descriptionmentioning

confidence: 99%

“…When the sample information about a particular batch is meager, or nonexistent, small-area methodology may be useful. Battese, Harter, and Fuller (1988) discussed the application of small-area meth ods to the prediction of county crop areas using satellite information. Their data, which are given in Tables 1.2 Here we focus on the estimation of mean hectares of corn per segment for the 12 counties represented in the sample.…”

Section: Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Some Bayesian and Non-Bayesian Procedures for the Analysis of Comparative Experiments and for Small-Area Estimation: Computational Aspects, Frequentist Properties, and Relationships

Hulting

Harville

1991

Journal of the American Statistical Association

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Hulting, Frederick Landis, "Some Bayesian and non-Bayesian procedures for the analysis of comparative experiments and for smallarea estimation: computational aspects, frequentist properties, and relationships " (1989 INFORMATION TO USERSThe most advanced technology has been used to photo graph and reproduce this manuscript from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer.The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction.In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion.Oversize materials (e.g., maps, drawings, charts) are re produced by sectioning the original, beginning at the upper left-hand corner and continuing from left to right in equal sections with small overlaps. Each original is also photographed in one exposure and is included in reduced form at the back of the book. These are also available as one exposure on a standard 35mm slide or as a 17" x 23" black and white photographic print for an additional charge.Photographs included in the original manuscript have been reproduced xerographically in this copy. Higher quality 6" x 9" black and white photographic prints are available for any photographs or illustrations appearing in this copy for an additional charge. Contact UMI directly to order.

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“…When direct estimation is not possible, one has to rely upon alternative model-based methods for producing small area estimates. One popular approach uses mixed (random) effects models for small area estimation (Fay and Herriot, 1979;Battese et. al., 1988).…”

Section: Introductionmentioning

confidence: 99%

Small area estimation under spatial nonstationarity

Chandra

Salvati

Chambers

et al. 2012

Computational Statistics & Data Analysis

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In this paper a geographical weighted pseudo empirical best linear unbiased predictor (GWEBLUP) for small area averages is proposed, and two approaches for estimating its mean squared error (MSE), a conditional approach and an unconditional one, are developed. The popular empirical best linear unbiased predictor (EBLUP) under the linear mixed model and its associated MSE estimator are obtained as a special case of the GWEBLUP. Empirical results using both model-based and design-based simulations, with the latter based on two real data sets, show that the GWEBLUP predictor can lead to efficiency gains when spatial nonstationarity is present in the data. A practical gain from using the GWEBLUP is in small area estimation for out of sample areas. In this case the efficient use of geographical information can potentially improve upon conventional synthetic estimation.

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An Error-Components Model for Prediction of County Crop Areas Using Survey and Satellite Data

Cited by 554 publications

References 14 publications

A Comparison of Small Area Estimation Methods for Poverty Mapping

A Comparison of Small Area Estimation Methods for Poverty Mapping

Some Bayesian and Non-Bayesian Procedures for the Analysis of Comparative Experiments and for Small-Area Estimation: Computational Aspects, Frequentist Properties, and Relationships

Small area estimation under spatial nonstationarity

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