Elbers, Chrisvan der Weide, Roy2014-08-152014-08-152014-07https://hdl.handle.net/10986/19362This paper proposes a method for estimating distribution functions that are associated with the nested errors in linear mixed models. The estimator incorporates Empirical Bayes prediction while making minimal assumptions about the shape of the error distributions. The application presented in this paper is the small area estimation of poverty and inequality, although this denotes by no means the only application. Monte-Carlo simulations show that estimates of poverty and inequality can be severely biased when the non-normality of the errors is ignored. The bias can be as high as 2 to 3 percent on a poverty rate of 20 to 30 percent. Most of this bias is resolved when using the proposed estimator. The approach is applicable to both survey-to-census and survey-to-survey prediction.en-USCC BY 3.0 IGOANALYSIS OF VARIANCEASYMPTOTIC DISTRIBUTIONBENCHMARKBIASESBOOTSTRAPCENTRAL LIMIT THEOREMCOMMON VARIANCECOVARIANCEDEPENDENT VARIABLEDESCRIPTIVE STATISTICSDEVELOPED COUNTRIESDEVELOPING COUNTRIESDEVELOPMENT ECONOMICSDEVELOPMENT POLICYDEVELOPMENT RESEARCHDISTRIBUTION FUNCTIONDISTRIBUTION FUNCTIONSDISTRIBUTIONAL ASSUMPTIONSECONOMIC REVIEWECONOMICSEMPIRICAL APPLICATIONEMPIRICAL SUPPORTEQUATIONSERRORERROR TERMERROR TERMSESTIMATION METHODEXPECTED VALUEFINITE SAMPLEFUNCTIONAL FORMGINI INDEXGOODNESS-OF-FITHETEROSKEDASTICITYHOUSEHOLD DATAHOUSEHOLD INCOMEHOUSEHOLD MEMBERSHOUSEHOLD SIZEINCOME DATAINCOME DISTRIBUTIONINCOME INEQUALITYINDEPENDENT VARIABLESINEQUALITY MEASUREMENTINEQUALITY WILLLINEAR FUNCTIONLINEAR MODELSLOG INCOMELOG LIKELIHOOD FUNCTIONLOG-LIKELIHOOD FUNCTIONMATHEMATICSMATRIXMAXIMUM LIKELIHOODMAXIMUM LIKELIHOOD ESTIMATIONMEASUREMENT ERRORMOMENT CONDITIONMONTE CARLO SIMULATIONNON-LINEAR FUNCTIONNORMAL DENSITYNORMAL DISTRIBUTIONOPTIMIZATIONPARAMETER VECTORPER CAPITA INCOMEPER CAPITA INCOMESPOINT ESTIMATESPOLICY DISCUSSIONSPOLICY RESEARCHPOVERTY ALLEVIATIONPOVERTY LINEPOVERTY LINESPOVERTY RATEPOVERTY RATESPRECISIONPREDICTIONPREDICTIONSPROBABILITIESPROBABILITYPROBABILITY DENSITYPROBABILITY DENSITY FUNCTIONPROBABILITY DISTRIBUTIONPROBABILITY DISTRIBUTION FUNCTIONPUBLIC ECONOMICSPUBLIC GOODSRANDOM EFFECTSRANDOM VARIABLERANDOM VARIABLESREGRESSION MODELSAMPLE SIZESKEWNESSSTANDARD DEVIATIONSTANDARD ERRORSSTRUCTURAL MODEL10.1596/1813-9450-6962