DEMYSTIFYING OPTIMAL WELFARE WEIGHTS CONTROVERSY FROM A SOCIAL STRATEGIST PERSPECTIVE

Rohit Malhorta ()
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Rohit Malhorta: Auro University, School of Management & Hospitality, India

Journal of Social and Economic Statistics, 2016, vol. 5, issue 2, 33-48

Abstract: What are “welfare weights”, here in terms of welfare economics; a welfare weight strategy can be understood as using portfolio simulation to generate mathematical optimal weights to distribute the risks of incomplete market available through restricted financial information, i.e. the use of constrained optimal weights. Author compared how ex-post optimal weights in terms of wage regression residuals matrix reduced to selective portfolio sets behave while implementing different optimization frameworks. For this, use of OLS, LAD and Quantile regression methods were used across Cement sector companies’ financial data. Now, the motivation of an author is not to mere building a mathematical premise, but try to justify through supporting literature that unless a cultural transformation with subject to optimization is not respected, the phenomena will be difficult to understand. The statistical outcomes revealed that all the three methods , in terms of “post optimized percent change of weights” does not show significant difference when using impure combinations (non-normal and normal residual series portfolio) but the results strongly conceived towards LAD regression while using pure combinations (only normally distributed residual series portfolio), and hence, it is therefore, possible that a welfare strategy maintaining the thought of percent change of welfare weights can be considered as a viable tool for policy makers in future.

Keywords: OLS; LAD; Quantile regression; Risk optimization (search for similar items in EconPapers)
JEL-codes: C52 C54 C61 (search for similar items in EconPapers)
Date: 2016
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