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Economic Regionalization Of India 1960-61 And 1970-71., Rabindranath De Dr.
Economic Regionalization Of India 1960-61 And 1970-71., Rabindranath De Dr.
Doctoral Theses
A massive investment programme has been undertaken in India through the process of planning. But the effects of such investment on economic growth are worked out only at the aggregate level for the country as a whole; its contribution to the growth of the different regions in the country is yet to be adequately understood. Now, for a large country like India with significant regional disparities in the level of living, an essential objective of development policy should be to reduce the existing economic inequality between the regions | in particular, the inequality in the level of living. The priority …
On Specification And Statistical Inference In Single Equation Regression Models., Nityananda Sarkar Dr.
On Specification And Statistical Inference In Single Equation Regression Models., Nityananda Sarkar Dr.
Doctoral Theses
This thesis attempta to cansider and provids solutions to viat nay be broadly decribed na Bone problems of speciification and satatistical inforence in single equation lineur regression models. It co sists of three parte, each having several chepters. The firat part in devoted to sme problems connec ted vith the use of the wall-imown Bor-Cox (BC) tranaformation of variablee in single equetion ragreanion nodels. In the other two parts we exanine an autocorrelated linear regreasion nodel from a rather unconventional angle. Precisely, we consider the problema which arise when the error tem in an autocorre lated linear regression nodel is …
Estimation In Errors-In-Variables Models., Manoranjan Pal Dr.
Estimation In Errors-In-Variables Models., Manoranjan Pal Dr.
Doctoral Theses
In many econometric investigations, the 'errors-in variables' (EIVs) are not negligible (Morgenstern, 1963). Examination of 25 series relating to national accounts by Langaskens and Rijekeghan (1974) showed that the standard deviations of the errors ranged from 5 to 77 per cent of the average value of the corresponding variable. Such errors may vitiate least-squares (LS) estimation of regression coefficients (Johnaton, 1972). The well-known methods (ML; IV, including grouping method) proposed for handling classical EIV model (EMM) in regression analysis muffer from serious linitations. Same of them make strong distributional assumptions about the errors (and the regressors) and/or assume prier knowledge …