Shalabh
shalab@iitk.ac.in
shalabh1@yahoo.com
Department of Mathematics & Statistics
Indian Institute of Technology Kanpur, Kanpur - 208016 (India)

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Econometric Methods for Statisticians, Data Scientists and Data Engineers

by

Professor Anoop Chaturvedi

Allahabad University

Swayam Prabha Course

The forty hours course is for the students in Bachelor's and Master's programmes and covers the topics of Econometrics from Statistics perspective.

Suggested books:

1. Baltagi, Badi H. (2021): Econometric Analysis of Panel Data, Springer.

2. Gujarathi, D. (1979): Basic Econometrics, McGraw Hill.

3. Johnston, J. and J. Dinardo (1997): Econometric methods. Third edition, McGraw Hill.

4. Judge, G.G., W.E. Griffiths, R.C. Hill Luetkepohl and T. C. Lee (1985). The theory and practice of econometrics, Wiley.

5. Koutsoyiannis, A. (1979): Theory of Econometrics, Macmillan Press.

6. Srivastava, V.K. and Giles D.A.E. (1987): Seemingly unrelated regression equations models, Marcel Dekker.

7. Ullah, A. and Vinod, H.D. (1981). Recent advances in Regression Methods, Marcel Dekker

Language of the course: English

Duration of the course: 40 Hours

Swayam Prabha DTH Channel 16 Youtube link: The telecasted lectures are available at  YouTube (Click here). 

Slides and Videos used in the lectures: 

Lecture No.	Lecture videos download links	Lecture slides download links	Lecture Title	Brief Description
1	Click here Lecture 1	Click here Lecture 1	Introduction to Econometrics	Introduction to Econometrics, its various applications, Outline of the Course
2	Click here Lecture 2	Click here Lecture 2	Matrix Methods in Econometrics	Some basic results of matrix algebra and regression analysis required for the course
3	Click here Lecture 3	Click here Lecture 3	Multivariate Normal Distribution	Multivariate normal distribution, marginal and conditional distributions, distribution of quadratic forms of multivariate random vectors,
4	Click here Lecture 4	Click here Lecture 4	Simple Linear Regression Model	Two variable linear model, estimation of parameters and the properties of estimators, analysis of variance, testing the significance of regression coefficient
5	Click here Lecture 5	Click here Lecture 5	Multiple Linear Model and Least Squares Estimation	Multiple Linear Model: Motivation and Assumptions, Method of least squares for the estimation of parameters
6	Click here Lecture 6	Click here Lecture 6	Properties of OLS and Maximum Likelihood Estimators	Finite sample and large sample properties of OLS estimators, BLUE property of OLS, Method of maximum likelihood
7	Click here Lecture 7	Click here Lecture 7	Analysis of Variance, Model Selection and Confidence Estimation	Analysis of Variance and Model, Selection criterion, confidence estimation of regression parameters
8	Click here Lecture 8	Click here Lecture 8	Restricted Regression Estimation	Restricted Regression Estimation of parameters of the model and properties of restricted regression estimators
9	Click here Lecture 9	Click here Lecture 9	Testing Set of Linear and Nonlinear Hypothesis	Model with set of linear restrictions binding the coefficients, restricted regression estimator, Set of linear hypotheses, likelihood ratio test for testing the set of linear restrictions, testing the set of non-linear hypothesis.
10	Click here Lecture 10	Click here Lecture 10	Model with non-spherical disturbances	Model with non-spherical disturbances, generalized least squares estimator and its properties
11	Click here Lecture 11	Click here Lecture 11	Model with Heteroscedastic disturbances	Effect of Heteroscedasticity, Estimation of model with heteroscedastic disturbances, Tests for heteroscedasticity
12	Click here Lecture 12	Click here Lecture 12	Model with autocorrelated disturbances	Effect of Autocorrelation, Estimation of model with autocorrelated disturbances, tests for autocorrelation
13	Click here Lecture 13	Click here Lecture 13	Testing and Estimation Under Stochastic Restrictions	Mixed regression estimation in the presence of linear stochastic restrictions, tests for stochastic linear restrictions
14	Click here Lecture 14	Click here Lecture 14	Prediction in Regression Models	Prediction and forecasting in linear regression models when explanatory variables are uncertain. Forecasting for models with non-spherical disturbances. Forecasting for model with disturbances following stationary AR(1) process.
15	Click here Lecture 15	Click here Lecture 15	Specification analysis	Effect of omission of relevant variables, effect of inclusion of irrelevant variables, Effect on predictions
16	Click here Lecture 16	Click here Lecture 16	Instrumental Variable Estimation	Instrumental variables estimation, properties of IV estimators, choice of instrumental variables
17	Click here Lecture 17	Click here Lecture 17	Measurement Error Model: Introduction	Introduction to measurement errors, causes and consequence of measurement errors, functional, structural, and ultra structural forms of measurement error models, choice of instrumental variables for measurement errors model
18	Click here Lecture 18	Click here Lecture 18	Measurement Error Model: Maximum Likelihood Estimation	Maximum likelihood estimation of parameters of , functional, structural, and ultra structural forms of measurement error models
19	Click here Lecture 19	Click here Lecture 19	Testing Structural break, parameter constancy and model stability	Tests for Structural break with equal and unequal variances, Tests for model stability, Tests for Parameter Constancy
20	Click here Lecture 20	Click here Lecture 20	Multicollinearity problem, its Sources and Consequences	Definition of multicollinearity, its sources, consequences of multicollinearity
21	Click here Lecture 21	Click here Lecture 21	Detection and solutions to multicollinearity	Different measures of multicollinearity, Solutions to multicollinearity, principal component estimator, ordinary and generalized ridge regression estimators
22	Click here Lecture 22	Click here Lecture 22	Shrinkage Estimation and Penalized regression	Penalized regression estimation including LASSO, Group LASSO, Stein-rule estimation, and its dominance condition over OLS estimator under quadratic loss function.
23	Click here Lecture 23	Click here Lecture 23	Models with Dummy Explanatory Variables and Models with Discrete Dependent Variables	Definition of dummy variables, incorporating dummy variables in linear models, ANOVA and ANCOVA for models with dummy variables, model with interactions among dummy variables
24	Click here Lecture 24	Click here Lecture 24	LOGIT and PROBIT Models	Dummy dependent variables, linear probability model and its shortcomings, LOGIT models and its estimation, PROBIT model and its estimation
25	Click here Lecture 25	Click here Lecture 25	TOBIT and Multinomial Choice Models	TOBIT Model for censored dependent variables and estimation of parameters, Basic framework of multinomial choice models, Multinomial Logit (MNL) Model, its identification and estimation, Nested Logit Model
26	Click here Lecture 26	Click here Lecture 26	Distributed Lag Models	Finite and infinite distributed lag models, autoregressive distributed lag (ARDL) models, choice of lag length and estimation of parameters
27	Click here Lecture 27	Click here Lecture 27	Nonlinear Regression Models	Nonlinear regression models, estimation for nonlinear regression models, non-linear least squares, testing for parameter restrictions, confidence intervals
28	Click here Lecture 28	Click here Lecture 28	Seemingly Unrelated Regression Models: Introduction	Two equations and M equations SUR models, Generalized least squares
29	Click here Lecture 29	Click here Lecture 29	Seemingly Unrelated Regression Models: MLE Estimation, Nonlinear System and GMM Estimation	Feasible GLS estimation, restricted and unrestricted residuals estimation, Maximum likelihood Estimation of parameters, , nonlinear system and GMM estimation
30	Click here Lecture 30	Click here Lecture 30	Simultaneous Equations Model: Introduction and Basic Concepts	Basic concepts and fundamental issues, illustrating issues in estimation of parameters using examples.
31	Click here Lecture 31	Click here Lecture 31	General form of Simultaneous Equations Model and the identification problem	Introduce the general simultaneous equations model, explain the problem of identification with the help of few examples.
32	Click here Lecture 32	Click here Lecture 32	Identification problem and rank, order conditions	Explaining identification problem for general simultaneous equations model using likelihood function, rank and order conditions
33	Click here Lecture 33	Click here Lecture 33	Identification for Reduced Form, Derivation of rank and order Conditions	Identification for the reduced form of the simultaneous equations model, Derivation of rank and order conditions of identification
34	Click here Lecture 34	Click here Lecture 34	Methods of Estimation: Limited Information Estimation Methods	Recursive models, Indirect least squares, Instrumental Variable estimation,
35	Click here Lecture 35	Click here Lecture 35	Two Stage Least Squares and Limited Information Maximum Likelihood Estimators	Two-stage least squares estimation, k-class estimator, Limited Information Maximum Likelihood Estimation
36	Click here Lecture 36	Click here Lecture 36	Three Stage Least Squares and Full Information Maximum Likelihood Estimators	Three-stage least squares estimation, Full-Information Maximum Likelihood estimation
37	Click here Lecture 37	Click here Lecture 37	Panel Data Models: Basic concepts	Introduction to panel data, its advantages, balanced and unbalanced panel data, issues involved in panel data, heterogeneity across individuals and time
38	Click here Lecture 38	Click here Lecture 38	One-Way Error Component Regression Model with fixed and random effects	One-Way Error Component Regression Model with fixed and random effects, pooled estimators, Estimation of fixed effects model with two time periods
39	Click here Lecture 39	Click here Lecture 39	Estimation of Random Effects Model	Estimation of fixed effects model with more than two time periods, within and between estimators, testing for fixed effects, Estimation of random effects model, GLS estimation
40	Click here Lecture 40	Click here Lecture 40	Two-Way Error Component Regression Model	Estimation of two way error component models with fixed and random effects, Wu-Hausman Test for Fixed Effects against Random Effects