Date of Submission


Date of Award


Institute Name (Publisher)

Indian Statistical Institute

Document Type

Doctoral Thesis

Degree Name

Doctor of Philosophy

Subject Name

Quantitative Economics


Economic Research Unit (ERU-Kolkata)


Bhattacharya, Nikhilesh (ERU-Kolkata; ISI)

Abstract (Summary of the Work)

The main motivation of the present dissertation is the estimation of engel elasticities of certain items of consumption after eliminating the possible effects of seasonal and other short-run fluctuations from household budget data. Indian budget data provided by the National Sample Survey Organization (NSSO), mainly relating to the 38th round (January - December, 1983), has been utilized for the study. Such data generally relate to a moving reference period of 'last 30 days' preceding the date of interview (also referred to as the last month reference period). The use of such a reference period introduces errors in observation in both the regressor and the regressand leading to biased estimates in the standard method of engel curve estimation. This problem has been largely ignored in the econometric literature.Some literature relevant to this analysis is surveyed in Chapter 1. NSS data utilized in this analysis is described in Chapter 2. In Chapters 3 and 4, first, a number of frequently applied engel functions were fitted to the data to find out, on the basis of certain statistical criteria, which engel curve forms fit the data best. Then the effect of the length of the reference period, used for collection of data, on the estimated elasticities has been examined in detail utilizing NSS data collected for last month and last year reference periods. Last year based estimates are found to be appreciably different from last month based estimates. The results highlight the need for alternative methods of estimating engel elasticities from budget data relating to short reference periods like last month.In Chapters 5 and 6 the problem is treated in an errors-in-variables (EIV) frame- work. However, the standard techniques of dealing with the EIV problem offer no easy solution in this case, especially because the errors in the regressor and the regressand are correlated. The possibilities of Instrumental Variable (IV) estimation and Method of Moments estimation have been tried out in Chapter 5 and Chapter 6, respectively. Section 6.6 of Chapter 6 concludes this dissertation with some remarks on the main findings and the shortcomings of this analysis.


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This work is licensed under a Creative Commons Attribution 4.0 International License.


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