000			02568nam a22002297a 4500
005			20250506161213.0
008			250429b |||||||| |||| 00| 0 eng d
020			_a9789391820312
040			_cAIMIT LIBRARY
082			_21 _a330.015195 _bSENJ
100			_aSengupta, Jhumur. _9209107
245			_aIntroduction to econometrics / _cBy Jhumur Sengupta.
250			_a1st ed.
260			_aNew Delhi : _bSultan Chand & Sons , _c2023.
300			_axvi,174p. ; _bPB _c22.3 cm
500			_aThe book is intended for the Core Course on “Introductory Econometrics” for Economics Honours students at the Undergraduate level according to the National Education Policy (NEP), 2020 and Choice Based Credit System syllabus. All the UGC-recognized Universities are the potential users of the book. In addition, the book covers a part of the UGC NET Syllabus. Students and researchers who want to learn basic Econometric theory will find the book very useful. The book addresses the basic theories of Econometrics in a clear and lucid manner. Salient Fetures The book covers topics including regression models, parameter estimation techniques, properties of the estimators, statistical testing and model specification problems in detail. Elementary concepts of statistics have been provided in Chapter 1 of the book. For ease of understanding, chapters on advanced topics are covered in the later part of the book. Statistical and mathematical derivations are used in the book in a thorough manner for the students and researchers who do not have any exposure to the course Econometrics. Each chapter contains several examples and exercise problems illustrating the applications of econometric theories. Some of the examples and exercise problems have been taken from the UGC NET Examination, Examinations at several Universities and Competitive Examinations. Every effort has been made to explain the basic theories in a simple way for easy understanding of the subject. A discussion on Computer Packages STATA and R is given in the Appendix Section.
505			_a Contents: Nature and Scope of Econometrics Estimation of Classical Linear Regression Model Properties of Least Square Estimators Statistical Inference in Linear Regression Model Data Problems & Violations of Classical Assumptions Specification Analysis
650			_aEstimation of classical linear regression model _9209108
650			_aSpecification analysis _9209109
650			_aStatistical inference in linear regression model _9209110
942			_2ddc _cBK _e1st _k330.015195 SENJ
999			_c234265 _d234265