Evaluation of Statistical Matching and Selected SAE Methods

1. Front Matter

   Pages I-XIII

   PDF

2. Introduction

   • Verena Puchner

   Pages 1-2

3. Mathematical Background

   • Verena Puchner

   Pages 3-15

4. Design of the Simulation Study

   • Verena Puchner

   Pages 17-22

5. Application to Poverty Estimation Using EU-SILC and Micro Census Data

   • Verena Puchner

   Pages 23-62

6. Conclusion

   • Verena Puchner

   Pages 63-64

7. Back Matter

   Pages 65-101

   PDF

Verena Puchner evaluates and compares statistical matching and selected SAE methods. Due to the fact that poverty estimation at regional level based on EU-SILC samples is not of adequate accuracy, the quality of the estimations should be improved by additionally incorporating micro census data. The aim is to find the best method for the estimation of poverty in terms of small bias and small variance with the aid of a simulated artificial "close-to-reality" population. Variables of interest are imputed into the micro census data sets with the help of the EU-SILC samples through regression models including selected unit-level small area methods and statistical matching methods. Poverty indicators are then estimated. The author evaluates and compares the bias and variance for the direct estimator and the various methods. The variance is desired to be reduced by the larger sample size of the micro census.

• EU-SILC Samples
• Micro Census
• Poverty Estimation
• Regression Models
• Statistical Matching

• Tieto Austria GmbH, Vienna, Austria

  Verena Puchner

Verena Puchner obtained her master’s degree at Technical University of Vienna under the supervision of Priv.-Doz. Dipl.-Ing. Dr. techn. Matthias Templ. At present, she works as a data miner and consultant.

Policies and ethics