On the choice of the number of Monte Carlo iterations and bootstrap replicates in Empirical Best Prediction

Empirical Best Predictors (EBPs) are widely used for small area estimation purposes. In the case of longitudinal surveys, this class of predictors can be used to predict any given population or subpopulation characteristic for any time period, including future periods. Generally, the value of an EBP...
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Autor*in:	Chwila, Adam [verfasserIn] Żądło, Tomasz [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Rechteinformationen:	Open Access Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International ; CC BY-NC-ND 4.0

Übergeordnetes Werk:	Enthalten in: Statistics in transition - Warszawa : GUS, 2000, 21(2020), 2 vom: Juni, Seite 35-60
Übergeordnetes Werk:	volume:21 ; year:2020 ; number:2 ; month:06 ; pages:35-60

Links:	Link aufrufen Link aufrufen Link aufrufen

DOI / URN:	10.21307/stattrans-2020-013

Katalog-ID:	1726661210

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allfields	10.21307/stattrans-2020-013 doi 10419/236763 hdl (DE-627)1726661210 (DE-599)KXP1726661210 DE-627 ger DE-627 rda eng Chwila, Adam verfasserin aut On the choice of the number of Monte Carlo iterations and bootstrap replicates in Empirical Best Prediction Adam Chwila, Tomasz Żądło 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Empirical Best Predictors (EBPs) are widely used for small area estimation purposes. In the case of longitudinal surveys, this class of predictors can be used to predict any given population or subpopulation characteristic for any time period, including future periods. Generally, the value of an EBP is computed by means of Monte Carlo algorithms, while its MSE is usually estimated using the parametric bootstrap method. Model-based simulation studies of the properties of the predictors require numerous repetitions of the random generation of population data. This leads to a question about the dependence between the number of iterations in all the procedures and the stability of the results. The aim of the paper is to show this dependence and to propose methods of choosing the appropriate number of iterations in practice, using a set of real economic longitudinal data available at the United States Census Bureau website. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ Żądło, Tomasz verfasserin aut Enthalten in Statistics in transition Warszawa : GUS, 2000 21(2020), 2 vom: Juni, Seite 35-60 Online-Ressource (DE-627)512298068 (DE-600)2235641-1 (DE-576)281309450 2450-0291 nnns volume:21 year:2020 number:2 month:06 pages:35-60 https://www.exeley.com/exeley/journals/statistics_in_transition/21/2/pdf/10.21307_stattrans-2020-013.pdf Verlag kostenfrei https://doi.org/10.21307/stattrans-2020-013 Resolving-System kostenfrei http://hdl.handle.net/10419/236763 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2863 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 21 2020 2 6 35-60 26 01 0206 373718237X x1z 10-08-20 2403 01 DE-LFER 3756573133 00 --%%-- --%%-- n --%%-- l01 16-09-20 2403 01 DE-LFER https://doi.org/10.21307/stattrans-2020-013 2403 01 DE-LFER https://www.exeley.com/exeley/journals/statistics_in_transition/21/2/pdf/10.21307_stattrans-2020-013.pdf 26 00 DE-206 56 survey sampling 26 00 DE-206 56 economic longitudinal data 26 00 DE-206 56 prediction for future periods
spelling	10.21307/stattrans-2020-013 doi 10419/236763 hdl (DE-627)1726661210 (DE-599)KXP1726661210 DE-627 ger DE-627 rda eng Chwila, Adam verfasserin aut On the choice of the number of Monte Carlo iterations and bootstrap replicates in Empirical Best Prediction Adam Chwila, Tomasz Żądło 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Empirical Best Predictors (EBPs) are widely used for small area estimation purposes. In the case of longitudinal surveys, this class of predictors can be used to predict any given population or subpopulation characteristic for any time period, including future periods. Generally, the value of an EBP is computed by means of Monte Carlo algorithms, while its MSE is usually estimated using the parametric bootstrap method. Model-based simulation studies of the properties of the predictors require numerous repetitions of the random generation of population data. This leads to a question about the dependence between the number of iterations in all the procedures and the stability of the results. The aim of the paper is to show this dependence and to propose methods of choosing the appropriate number of iterations in practice, using a set of real economic longitudinal data available at the United States Census Bureau website. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ Żądło, Tomasz verfasserin aut Enthalten in Statistics in transition Warszawa : GUS, 2000 21(2020), 2 vom: Juni, Seite 35-60 Online-Ressource (DE-627)512298068 (DE-600)2235641-1 (DE-576)281309450 2450-0291 nnns volume:21 year:2020 number:2 month:06 pages:35-60 https://www.exeley.com/exeley/journals/statistics_in_transition/21/2/pdf/10.21307_stattrans-2020-013.pdf Verlag kostenfrei https://doi.org/10.21307/stattrans-2020-013 Resolving-System kostenfrei http://hdl.handle.net/10419/236763 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2863 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 21 2020 2 6 35-60 26 01 0206 373718237X x1z 10-08-20 2403 01 DE-LFER 3756573133 00 --%%-- --%%-- n --%%-- l01 16-09-20 2403 01 DE-LFER https://doi.org/10.21307/stattrans-2020-013 2403 01 DE-LFER https://www.exeley.com/exeley/journals/statistics_in_transition/21/2/pdf/10.21307_stattrans-2020-013.pdf 26 00 DE-206 56 survey sampling 26 00 DE-206 56 economic longitudinal data 26 00 DE-206 56 prediction for future periods
allfields_unstemmed	10.21307/stattrans-2020-013 doi 10419/236763 hdl (DE-627)1726661210 (DE-599)KXP1726661210 DE-627 ger DE-627 rda eng Chwila, Adam verfasserin aut On the choice of the number of Monte Carlo iterations and bootstrap replicates in Empirical Best Prediction Adam Chwila, Tomasz Żądło 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Empirical Best Predictors (EBPs) are widely used for small area estimation purposes. In the case of longitudinal surveys, this class of predictors can be used to predict any given population or subpopulation characteristic for any time period, including future periods. Generally, the value of an EBP is computed by means of Monte Carlo algorithms, while its MSE is usually estimated using the parametric bootstrap method. Model-based simulation studies of the properties of the predictors require numerous repetitions of the random generation of population data. This leads to a question about the dependence between the number of iterations in all the procedures and the stability of the results. The aim of the paper is to show this dependence and to propose methods of choosing the appropriate number of iterations in practice, using a set of real economic longitudinal data available at the United States Census Bureau website. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ Żądło, Tomasz verfasserin aut Enthalten in Statistics in transition Warszawa : GUS, 2000 21(2020), 2 vom: Juni, Seite 35-60 Online-Ressource (DE-627)512298068 (DE-600)2235641-1 (DE-576)281309450 2450-0291 nnns volume:21 year:2020 number:2 month:06 pages:35-60 https://www.exeley.com/exeley/journals/statistics_in_transition/21/2/pdf/10.21307_stattrans-2020-013.pdf Verlag kostenfrei https://doi.org/10.21307/stattrans-2020-013 Resolving-System kostenfrei http://hdl.handle.net/10419/236763 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2863 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 21 2020 2 6 35-60 26 01 0206 373718237X x1z 10-08-20 2403 01 DE-LFER 3756573133 00 --%%-- --%%-- n --%%-- l01 16-09-20 2403 01 DE-LFER https://doi.org/10.21307/stattrans-2020-013 2403 01 DE-LFER https://www.exeley.com/exeley/journals/statistics_in_transition/21/2/pdf/10.21307_stattrans-2020-013.pdf 26 00 DE-206 56 survey sampling 26 00 DE-206 56 economic longitudinal data 26 00 DE-206 56 prediction for future periods
allfieldsGer	10.21307/stattrans-2020-013 doi 10419/236763 hdl (DE-627)1726661210 (DE-599)KXP1726661210 DE-627 ger DE-627 rda eng Chwila, Adam verfasserin aut On the choice of the number of Monte Carlo iterations and bootstrap replicates in Empirical Best Prediction Adam Chwila, Tomasz Żądło 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Empirical Best Predictors (EBPs) are widely used for small area estimation purposes. In the case of longitudinal surveys, this class of predictors can be used to predict any given population or subpopulation characteristic for any time period, including future periods. Generally, the value of an EBP is computed by means of Monte Carlo algorithms, while its MSE is usually estimated using the parametric bootstrap method. Model-based simulation studies of the properties of the predictors require numerous repetitions of the random generation of population data. This leads to a question about the dependence between the number of iterations in all the procedures and the stability of the results. The aim of the paper is to show this dependence and to propose methods of choosing the appropriate number of iterations in practice, using a set of real economic longitudinal data available at the United States Census Bureau website. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ Żądło, Tomasz verfasserin aut Enthalten in Statistics in transition Warszawa : GUS, 2000 21(2020), 2 vom: Juni, Seite 35-60 Online-Ressource (DE-627)512298068 (DE-600)2235641-1 (DE-576)281309450 2450-0291 nnns volume:21 year:2020 number:2 month:06 pages:35-60 https://www.exeley.com/exeley/journals/statistics_in_transition/21/2/pdf/10.21307_stattrans-2020-013.pdf Verlag kostenfrei https://doi.org/10.21307/stattrans-2020-013 Resolving-System kostenfrei http://hdl.handle.net/10419/236763 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2863 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 21 2020 2 6 35-60 26 01 0206 373718237X x1z 10-08-20 2403 01 DE-LFER 3756573133 00 --%%-- --%%-- n --%%-- l01 16-09-20 2403 01 DE-LFER https://doi.org/10.21307/stattrans-2020-013 2403 01 DE-LFER https://www.exeley.com/exeley/journals/statistics_in_transition/21/2/pdf/10.21307_stattrans-2020-013.pdf 26 00 DE-206 56 survey sampling 26 00 DE-206 56 economic longitudinal data 26 00 DE-206 56 prediction for future periods
allfieldsSound	10.21307/stattrans-2020-013 doi 10419/236763 hdl (DE-627)1726661210 (DE-599)KXP1726661210 DE-627 ger DE-627 rda eng Chwila, Adam verfasserin aut On the choice of the number of Monte Carlo iterations and bootstrap replicates in Empirical Best Prediction Adam Chwila, Tomasz Żądło 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Empirical Best Predictors (EBPs) are widely used for small area estimation purposes. In the case of longitudinal surveys, this class of predictors can be used to predict any given population or subpopulation characteristic for any time period, including future periods. Generally, the value of an EBP is computed by means of Monte Carlo algorithms, while its MSE is usually estimated using the parametric bootstrap method. Model-based simulation studies of the properties of the predictors require numerous repetitions of the random generation of population data. This leads to a question about the dependence between the number of iterations in all the procedures and the stability of the results. The aim of the paper is to show this dependence and to propose methods of choosing the appropriate number of iterations in practice, using a set of real economic longitudinal data available at the United States Census Bureau website. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ Żądło, Tomasz verfasserin aut Enthalten in Statistics in transition Warszawa : GUS, 2000 21(2020), 2 vom: Juni, Seite 35-60 Online-Ressource (DE-627)512298068 (DE-600)2235641-1 (DE-576)281309450 2450-0291 nnns volume:21 year:2020 number:2 month:06 pages:35-60 https://www.exeley.com/exeley/journals/statistics_in_transition/21/2/pdf/10.21307_stattrans-2020-013.pdf Verlag kostenfrei https://doi.org/10.21307/stattrans-2020-013 Resolving-System kostenfrei http://hdl.handle.net/10419/236763 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2863 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 21 2020 2 6 35-60 26 01 0206 373718237X x1z 10-08-20 2403 01 DE-LFER 3756573133 00 --%%-- --%%-- n --%%-- l01 16-09-20 2403 01 DE-LFER https://doi.org/10.21307/stattrans-2020-013 2403 01 DE-LFER https://www.exeley.com/exeley/journals/statistics_in_transition/21/2/pdf/10.21307_stattrans-2020-013.pdf 26 00 DE-206 56 survey sampling 26 00 DE-206 56 economic longitudinal data 26 00 DE-206 56 prediction for future periods
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source	Enthalten in Statistics in transition 21(2020), 2 vom: Juni, Seite 35-60 volume:21 year:2020 number:2 month:06 pages:35-60
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authorswithroles_txt_mv	Chwila, Adam @@aut@@ Żądło, Tomasz @@aut@@
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spellingShingle	Chwila, Adam 26 survey sampling 26 economic longitudinal data 26 prediction for future periods On the choice of the number of Monte Carlo iterations and bootstrap replicates in Empirical Best Prediction
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topic_title	26 00 DE-206 56 survey sampling 26 00 DE-206 56 economic longitudinal data 26 00 DE-206 56 prediction for future periods On the choice of the number of Monte Carlo iterations and bootstrap replicates in Empirical Best Prediction Adam Chwila, Tomasz Żądło
topic	26 survey sampling 26 economic longitudinal data 26 prediction for future periods
topic_unstemmed	26 survey sampling 26 economic longitudinal data 26 prediction for future periods
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title	On the choice of the number of Monte Carlo iterations and bootstrap replicates in Empirical Best Prediction
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title_full	On the choice of the number of Monte Carlo iterations and bootstrap replicates in Empirical Best Prediction Adam Chwila, Tomasz Żądło
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title_sort	on the choice of the number of monte carlo iterations and bootstrap replicates in empirical best prediction
title_auth	On the choice of the number of Monte Carlo iterations and bootstrap replicates in Empirical Best Prediction
abstract	Empirical Best Predictors (EBPs) are widely used for small area estimation purposes. In the case of longitudinal surveys, this class of predictors can be used to predict any given population or subpopulation characteristic for any time period, including future periods. Generally, the value of an EBP is computed by means of Monte Carlo algorithms, while its MSE is usually estimated using the parametric bootstrap method. Model-based simulation studies of the properties of the predictors require numerous repetitions of the random generation of population data. This leads to a question about the dependence between the number of iterations in all the procedures and the stability of the results. The aim of the paper is to show this dependence and to propose methods of choosing the appropriate number of iterations in practice, using a set of real economic longitudinal data available at the United States Census Bureau website.
abstractGer	Empirical Best Predictors (EBPs) are widely used for small area estimation purposes. In the case of longitudinal surveys, this class of predictors can be used to predict any given population or subpopulation characteristic for any time period, including future periods. Generally, the value of an EBP is computed by means of Monte Carlo algorithms, while its MSE is usually estimated using the parametric bootstrap method. Model-based simulation studies of the properties of the predictors require numerous repetitions of the random generation of population data. This leads to a question about the dependence between the number of iterations in all the procedures and the stability of the results. The aim of the paper is to show this dependence and to propose methods of choosing the appropriate number of iterations in practice, using a set of real economic longitudinal data available at the United States Census Bureau website.
abstract_unstemmed	Empirical Best Predictors (EBPs) are widely used for small area estimation purposes. In the case of longitudinal surveys, this class of predictors can be used to predict any given population or subpopulation characteristic for any time period, including future periods. Generally, the value of an EBP is computed by means of Monte Carlo algorithms, while its MSE is usually estimated using the parametric bootstrap method. Model-based simulation studies of the properties of the predictors require numerous repetitions of the random generation of population data. This leads to a question about the dependence between the number of iterations in all the procedures and the stability of the results. The aim of the paper is to show this dependence and to propose methods of choosing the appropriate number of iterations in practice, using a set of real economic longitudinal data available at the United States Census Bureau website.
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title_short	On the choice of the number of Monte Carlo iterations and bootstrap replicates in Empirical Best Prediction
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On the choice of the number of Monte Carlo iterations and bootstrap replicates in Empirical Best Prediction

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