- Semester: I
- Number of Credits: 4
Module 1: Basics of Statistical Inference
Data Issues: time series, cross section and panel data
Ideas of Probability and distribution functions - Mathematical expectation, Law of large numbers (without proof) - Central limit Theorem (without proof)
Properties of estimators: point versus interval estimation – Hypothesis Testing
Module 2: The Regression Model
The classical linear regression model: theory of least squares - Gauss Markov theorem - Statistical properties of the least square estimator in finite samples – Inference and prediction - dummy variables - distributed lags – restricted least squares
Module 3: Violation of the Classical Assumptions
Problem of Heteroscedasticity and Autocorrelation - Remedial Measures – Multicollinearity and specification issues
Module 4: Time Series Modeling
ARIMA Modelling: Box-Jenkins approach (identification, estimation and diagnostic testing)
Unit Roots and Cointegration: Data Generating Processes, Dickey-Fuller and Phillips-Perron approaches to unit root tests
Essential Texts
1. |
William Enders: Applied Econometric Time Series, Wiley, Second Edition, 2003 |
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2. |
William E. Griffiths, R. Carter Hill, George G. Judge: Learning and Practicing Econometrics, Wiley, 1993, |
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Paperback edition. |
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3. |
Damodar Gujarati: Basic Econometrics, McGraw-Hill, Fourth Edition, 2002 |
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4. |
Jan Kmenta: Elements of Econometrics, McMillan Publishing, Second Edition, 1990 |
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