Basic Econometrics

Por: Swayam . en: , ,


This course is framed in such a manner that the student gets familiar with a basic course content which includes first of all some basic requirements for statistical requisites such as sampling methods, large and small sample tests, estimation theory, testing of hypothesis etc. Next the focus is on basics with linear regression and prediction problems. Multiple regression is studied in sufficient details. The approaches when the basic assumptions are violated one by one are studied by Multicollinearity, Heteroscadasticity, auto correlation phenomena. Problems with specifications errors, lagged variables model, simultaneous equations system model with identification problem are discussed. Time series analysis and models are introduced. Some detailed study about dummy variables is presented Panel data analysis is also presented for application purpose.



UNIT 1: Classical Linear Regression ModelLecture 1: Sampling Techniques
Lecture 2: Sampling
UNIT 1:Lecture 3 : Introduction to Statistical Inference(Estimation Theory)
Lecture -4: Testing of Hypothesis
Lecture -5: Hypothesis-Large Sample test & Chi square test
Lecture- 6 : Hypothesis - small sample tests t Tests and F Tests
UNIT -2 Multiple Regression ModelLecture – 7 :Preview of Econometric Methods
Lecture – 8 :Linear Regression Model
Lecture – 9: Tests for Linear Regression Model
Unit – 2:Lecture- 10 :Multiple Regression Model and ExtensionsLecture- 11 :Multiple Regression ModelLecture- 12 :Other functional forms
Unit 3 Functional Forms and Dummy VariablesLecture- 13 :Dummy variables
Lecture- 14 :Regression Analysis for Qualitative Variables
Lecture- 15 :Regression Analysis for Dummy Dependent Variables

Unit -4 Relaxing the AssumptionsLecture- 16 :Prediction in Linear Models and Multicollinearity
Lecture- 17 :Generalised Least Squares

UNIT 4:Lecture- 18 :Autocorrelation
Lecture- 19 :Specification Errors

UNIT 4:Lecture- 20 :Autoregressive and Distributed Lag Models
Lecture- 21 :Simultaneous Equations Model

UNIT 4:Lecture- 22 :Time Series Models
Lecture- 23 :Panel data analysis