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Construction of Composite Index under Complex Surveys

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Field Value
 
Title Construction of Composite Index under Complex Surveys
PROJECT REPORT
 
Creator Deepak Singh
Pradip Basak
Raju Kumar
 
Subject Multicollinearity
Survey weight
Food consumption index
Household consumer expenditure surveys
Principal component
Index
 
Description Not Available
Principal component (PC) based index accounts for the effect of multicollinearity among the indicator variables through the eigen values and eigen vectors derived from the variance-covariance matrix using maximum likelihood (ML)/ordinary least squares (OLS) methods of estimation. However, these methods of estimation of variance covariance matrix are based on the assumption that sample elements, on which the indicator variables are measured, are independent and identically distributed. In complex survey designs, the independence assumption of units does not hold that leads to erroneous estimation of variance covariance matrix under OLS methods. Therefore, in case of survey data there is a need to develop PC based index using survey weights and auxiliary information which excludes the effect of multicollinearity among the indicator variables as well as accounts for the effect of complex survey designs through which the sample data is collected. Therefore under this study different methods of indices development are proposed which are capable to incorporate the survey weights and auxiliary information available in the data.
Not Available
 
Date 2022-07-16T05:22:45Z
2022-07-16T05:22:45Z
2020-12-21
 
Type Project Report
 
Identifier Singh, D., Basak, P. and Kumar, R. (2020). Construction of Composite Index under Complex Surveys. Project report, ICAR-IASRI, New Delhi, India
Not Available
http://krishi.icar.gov.in/jspui/handle/123456789/73607
 
Language English
 
Publisher ICAR-IASRI