EconPapers    
Economics at your fingertips  
 

Restricted calibration and weight trimming approaches for estimation of the population total in business statistics

Cenker Burak Metin, Sinem Tuğba Şahin Tekin and Yaprak Arzu Özdemir

Journal of Applied Statistics, 2021, vol. 48, issue 13-15, 2658-2672

Abstract: Some adjustments are made to design weights to reduce the negative effects of non-response and out-of-scope problems. The calibration approach is a weighting process that agrees with the known population values by using auxiliary information. In this study, alternative calibration approaches and weight trimming process that can be used in large data sets with extreme weights and different correlation structures were analysed. In addition, the effect of the correlation structure of auxiliary variables on the efficiency of the calibration estimators was investigated by a simulation study. The 2017 Annual Industry and Service Statistics data were used in the simulation study and it was seen that restricted calibration estimators were more efficient than the generalized regression estimator in estimating the variables with a high variance such as turnover. Especially in small sample fractions, we recommend the application of restricted calibration estimators, as they are more efficient than the weight trimming in solving the negative and less than one weights problem encountered after the calibration process.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2020.1869703 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2658-2672

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664763.2020.1869703

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-20
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2658-2672