<abstract xmlns="http://www.w3.org/1999/xhtml"> Evidence-based policy making and economic decision making rely on accurate business information on a national level and increasingly also on smaller regions and business classes. In general, traditional design-based methods suffer from low accuracy in the case of very small sample sizes in certain subgroups, whereas model-based methods, such as small area techniques, heavily rely on strong statistical models.In small area applications in business statistics, two major issues may occur. First, in many countries business registers do not deliver strong auxiliary information for adequate model building. Second, sampling designs in business surveys are generally nonignorable and contain a large variation of survey weights.The present study focuses on the performance of small area point and accuracy estimates of business statistics under different sampling designs. Different strategies of including sampling design information in the models are discussed. A design-based Monte Carlo simulation study unveils the impact of the variability of design weights and different levels of aggregation on model- versus design-based estimation methods. This study is based on a close to reality data set generated from Italian business data.</abstract>

Evidence-based policy making and economic decision making rely on accurate business information on a national level and increasingly also on smaller regions and business classes. In general, traditional design-based methods suffer from low accuracy in the case of very small sample sizes in certain subgroups, whereas model-based methods, such as small area techniques, heavily rely on strong statistical models.In small area applications in business statistics, two major issues may occur. First, in many countries business registers do not deliver strong auxiliary information for adequate model building. Second, sampling designs in business surveys are generally nonignorable and contain a large variation of survey weights.The present study focuses on the performance of small area point and accuracy estimates of business statistics under different sampling designs. Different strategies of including sampling design information in the models are discussed. A design-based Monte Carlo simulation study unveils the impact of the variability of design weights and different levels of aggregation on model- versus design-based estimation methods. This study is based on a close to reality data set generated from Italian business data.

The Impact of Sampling Designs on Small Area Estimates for Business Data

University of Trier – Fachbereich IV, Lehrstuhl für Wirtschafts- und Sozialstatistik, Universitätsring 15 Trier D-54286, Germany.

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"The Impact of Sampling Designs on Small Area Estimates for Business Data"}

Evidence-based policy making and economic decision making rely on accurate business information on a national level and increasingly also on smaller regions...

The Impact of Sampling Designs on Small Area Estimates for Business Data

Online veröffentlicht: 11. Dez. 2014

Seitenbereich: 749 - 771

Eingereicht: 01. Jan. 2013

Akzeptiert: 01. Juni 2014

DOI: https://doi.org/10.2478/jos-2014-0046

Schlüsselwörter
Nonignorable sampling designs, MSE estimation, confidence interval coverage

© by Jan Pablo Burgard

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The Impact of Sampling Designs on Small Area Estimates for Business Data

Online veröffentlicht: 11. Dez. 2014

Seitenbereich: 749 - 771

Eingereicht: 01. Jan. 2013

Akzeptiert: 01. Juni 2014

DOI: https://doi.org/10.2478/jos-2014-0046

SchlüsselwörterNonignorable sampling designs, MSE estimation, confidence interval coverage

© by Jan Pablo Burgard

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Nonignorable sampling designs, MSE estimation, confidence interval coverage