Econometric Methods for Statisticians, Data Scientists and Data Engineers
Swayam Prabha Course DTH Channel 16
Duration of the Course: 40 hours
Affiliation: Professor (Superannuated in June 2019), Department of Statistics, University of Allahabad,
Email: anoopchaturv@gmail.com
Mobile: +91 9415214134
Suggested Books:
Baltagi, Badi H. (2021): Econometric Analysis of Panel Data, Springer.
Gujarathi, D. (1979): Basic Econometrics, McGraw Hill.
Johnston, J. and J. Dinardo (1997): Econometric methods. Third edition, McGraw Hill.
Judge, G.G., W.E. Griffiths, R.C. Hill LĂĽtkepohl and T. C. Lee (1985). The theory and practice of econometrics, Wiley.
Koutsoyiannis, A. (1979): Theory of Econometrics, Macmillan Press.
Srivastava, V.K. and Giles D.A.E. (1987): Seemingly unrelated regression equations models, Marcel Dekker.
Ullah, A. and Vinod, H.D. (1981). Recent advances in Regression Methods, Marcel Dekker
YouTube link: The telecasted lectures are available at YouTube:Â
https://www.youtube.com/playlist?list=PLqMl6r3x6BURZXt383hjvoO9P-gx4__Ab
Slides and Videos used in the lectures:
Lecture videos download links Lecture slides download links Lecture Title
Click here Lecture 1 Click here Lecture 1 Introduction to Econometrics
Click here Lecture 2 Click here Lecture 2 Matrix Methods in Econometrics
Click here Lecture 3 Click here Lecture 3 Multivariate Normal Distribution
Click here Lecture 4 Click here Lecture 4 Simple Linear Regression Model
Click here Lecture 5 Click here Lecture 5 Multiple Linear Model and Least Squares Estimation
Click here Lecture 6 Click here Lecture 6 Properties of OLS and Maximum Likelihood Estimators
Click here Lecture 7 Click here Lecture 7 Analysis of Variance, Model Selection and Confidence Estimation
Click here Lecture 8 Click here Lecture 8 Restricted Regression Estimation
Click here Lecture 9 Click here Lecture 9 Testing Set of Linear and Nonlinear Hypothesis
Click here Lecture 10 Click here Lecture 10 Model with non-spherical disturbances
Click here Lecture 11 Click here Lecture 11 Model with Heteroscedastic disturbances
Click here Lecture 12 Click here Lecture 12 Model with autocorrelated disturbances
Click here Lecture 13 Click here Lecture 13 Testing and Estimation Under Stochastic Restrictions
Click here Lecture 14 Click here Lecture 14 Prediction in Regression Models
Click here Lecture 15 Click here Lecture 15 Specification analysis
Click here Lecture 16 Click here Lecture 16 Instrumental Variable Estimation
Click here Lecture 17 Click here Lecture 17 Measurement Error Model: Introduction
Click here Lecture 18 Click here Lecture 18 Measurement Error Model: Maximum Likelihood Estimation
Click here Lecture 19 Click here Lecture 19 Testing Structural break, parameter constancy and model stability
Click here Lecture 20 Click here Lecture 20 Multicollinearity problem, its Sources and Consequences
Click here Lecture 21 Click here Lecture 21 Detection and solutions to multicollinearity
Click here Lecture 22 Click here Lecture 22 Shrinkage Estimation and Penalized regression
Click here Lecture 23 Click here Lecture 23 Models with Dummy Explanatory Variables and Discrete Dependent Variables
Click here Lecture 24 Click here Lecture 24 LOGIT and PROBIT Models
Click here Lecture 25 Click here Lecture 25 TOBIT and Multinomial Choice Models
Click here Lecture 26 Click here Lecture 26 Distributed Lag Models
Click here Lecture 27 Click here Lecture 27 Nonlinear Regression Models
Click here Lecture 28 Click here Lecture 28 Seemingly Unrelated Regression Models: Introduction
Click here Lecture 29 Click here Lecture 29 SUR Models: MLE Estimation, Nonlinear SystemÂ
Click here Lecture 30 Click here Lecture 30 Simultaneous Equations Model: Introduction and Basic Concepts
Click here Lecture 31 Click here Lecture 31 General form of Simultaneous Equations Model and the identification problem
Click here Lecture 32 Click here Lecture 32 Identification problem and rank, order conditions
Click here Lecture 33 Click here Lecture 33 Identification for Reduced Form, Derivation of rank and order Conditions
Click here Lecture 34 Click here Lecture 34 Methods of Estimation: Limited Information Estimation Methods
Click here Lecture 35 Click here Lecture 35 Two Stage Least Squares and Limited Information Maximum Likelihood Estimators
Click here Lecture 36 Click here Lecture 36 Three Stage Least Squares and Full Information Maximum Likelihood Estimators
Click here Lecture 37 Click here Lecture 37 Panel Data Models: Basic concepts
Click here Lecture 38 Click here Lecture 38 One-Way Error Component Regression Model with fixed and random effects
Click here Lecture 39 Click here Lecture 39 Estimation of Random Effects Model
Click here Lecture 40 Click here Lecture 40 Two-Way Error Component Regression Model