Abstract
To put it in a nutshell, the subject matter of this PhD thesis is improving accuracy and comparability of official statistical figures.
Statistics are usually compiled based on (random) samples. The bigger the sample size, the closer to the true value the estimate (e.g., the average) is expected to be. Sometimes, the sample size is very small, such that the estimate becomes useless (think of an estimated unemployment rate of 3%±4% at a 95% confidence level). Apart from that, estimates become incomparable before and after the survey redesign. Time series models can be used to solve both problems without resorting to (expensive) additional interviewing. The gained efficiency may allow us to reduce the sample size twice or even thrice.
Statistics are usually compiled based on (random) samples. The bigger the sample size, the closer to the true value the estimate (e.g., the average) is expected to be. Sometimes, the sample size is very small, such that the estimate becomes useless (think of an estimated unemployment rate of 3%±4% at a 95% confidence level). Apart from that, estimates become incomparable before and after the survey redesign. Time series models can be used to solve both problems without resorting to (expensive) additional interviewing. The gained efficiency may allow us to reduce the sample size twice or even thrice.
Original language  English 

Awarding Institution 

Supervisors/Advisors 

Award date  11 May 2016 
Place of Publication  Maastricht 
Publisher  
Print ISBNs  9789461595508 
Publication status  Published  2016 
Keywords
 hierarchical Bayesian approach
 Kalman filter
 multilevel models
 small area estimation
 state space models
 variance reduction