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Course 003: Econometric Methods

Department of Economics

Part A: Basic Statistics: Rohini Somanathan

Courses:

1. Probalility Basics

2. Random Variables

3. Expectation

4. Special Distributions

5. Convergence

6. Estimation

7. Sampling distribution

8. Hypothesis testing

Under Special distribution, there is perhaps Binomial, Poisson, Normal.

SPECIAL NOTE: THIS TIME INSTRUCTOR HAS BEEN CHANGED. ROHINI IS

NOT

TAKING THIS PAPER ANYMORE. INSTEAD, DEEPTI GOYAL WILL BE

TAKING THIS

COURSE.

Part B: Econometrics

Instructor:Kanwar

This half of the course covers basic econometric techniques commonly

used in the empirical analysis of economic (and non-economic)

relationships. The emphasis is on both theoretical rigour, as well as

hands-on training using STATA.

Text

D.N. Gujarati, Basic Econometrics, McGraw-Hill, New York, 2003,

chapters 1-14 (except 5.10, 7.11, and 13.6-13.10), and Appendix C. You

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could purchase the Indian edition.

Other texts which should serve you equally well are:

James H. Stock and Mark W. Watson, Introduction to Econometrics,

Pearson Education Inc.

Ramu Ramanathan, Introductory Econometrics with Applications.

South-Western

Jeffrey Wooldridge, Introductory Econometrics: A Modern Approach,

South-Western

J. Kmenta, Elements of Econometrics, Macmillan

R.C. Hill, W.E. Griffiths, and G.G. Judge, Undergraduate Econometrics.

John Wiley & Sons

J. Johnston and J. Dinardo, Econometric Methods, McGraw Hill.

Problem sets

Problem sets will be handed out (posted on the course website), and

discussed in the tutorials, but will not be graded.

Internal Assessment

Internal assessment for part B will comprise a mid-term (20%), and one

lab assignment (5%). The mid-term will be held in-class, on February25, 2010 (Thursday).

Course Outline

Introduction to techniques used in estimating economic relationships.

Topics include estimation and testing of hypotheses, forecasting and

construction of prediction intervals, use of appropriate functional

forms, detection and correction of measurement problems andviolations

of the underlying assumptions, model specification, as well as use of

statistical software for regression analysis.

1. Simple Linear Regression: Estimation

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Types of data; Population regression function; Sample regression

function; Linearity in variables and parameters Ordinary Least Squares

(OLS) Estimation; Underlying assumptions; Desirable properties of

least squares estimators the Gauss-Markov theorem; Goodness-of-Fit

2. Normal Linear Regression

Normality assumption for the errors; Properties of least squares

estimators given normality; Maximum likelihood estimation

3. Simple Linear Regression: Inference

Interval estimation; Confidence interval approach to inference; Test

of significance approach to inference; Analysis of Variance;

Prediction

4. Extensions of the Simple Linear Model

Interpretation issues; Alternative functional forms; Effects of

measurement errors

5. Multiple Linear Regression: Estimation

Interpretation, Ordinary Least Squares (OLS) Estimation; Underlying

assumptions; Goodness-of-Fit; Polynomial regressions; Specificationbias

6. Multiple Linear Regression: Statistical Inference

Interval Estimation; Confidence interval approach to inference; Test

of significance approach to inference; Analysis of Variance; Overall

significance of the regression; Linear restrictions on the

coefficients

7. Dummy variables in regression models

Qualitative regressors only; qualitative and quantitative regressors;

interaction terms

8. Multicollinearity

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Nature; implications; detection; remedies

9. Heteroscedasticity

Nature; implications; detection; remedies

10. Autocorrelation

Nature; implications; detection; remedies

11 Model specification

Model selection criteria; inclusion of irrelevant variables; exclusion

of relevant variables; measurement errors

12. Matrix approach to regression analysisThe k-variable model; underlying assumptions; OLS estimation;

covariance matrix of the slope parameters; coefficient of

determination; hypothesis testing; GLS estimation

13. Nonlinear regression models

Intrinsically non-linear models; Newton-Raphson algorithm;

Gauss-Newton algorithm