Introductory Econometrics

Siby K M
Economics
₹1,000.00
  • 0 student
  • 2 lessons
  • 0 quizzes
  • 10 week duration
0 student

COURSE OBJECTIVES:

  • To make students acquaint with econometric techniques that are widely used in empirical work in Economics and other related disciplines
  • To make students learn the art of performing estimation, analysis and interpretation of the estimated econometric model

LEARNING OUTCOMES:

After the completion of the course, students will be able to –

  • Understand the econometric theories and techniques
  • Demonstrate their understanding of the appropriate econometric models for analyzing data
  • Interpret the computer output for the estimation and testing of econometric methods for analyzing data.
  • Interpret and discuss the results

REFERENCES:

  • Gujarati, Porter and Gunasekhar, Basic Econometrics, Fifth Edition
  •  James H Stock and Mark W. Watson, Introduction to Econometrics, Pearson 
Education; 3rd edition
  • RamuRamanathan, Introductory Econometrics with Applications, S.Chand & 
Company Ltd; 5th Revised edition
  • Christopher Dougherty, Introduction to Econometrics. NewDelhi: Oxford University 
Press
  • Dominick Salvatore , Derrick Reagle, Schaum’s Outline of Statistics and 
Econometrics, Second Edition, McGraw-Hill Education
  • A Koutsoyiannis, Theory of Econometrics, Second Edition, Palgrave Macmillan
  • Module 1

    Classical Liner Regression Model—Meaning and methodology—Modern interpretation of econometrics—Population regression function (PRF) —The concept of linearity in econometrics—stochastic Variable –interpretation and its significance — Sample regression function (SRF)

  • Module 2

    Estimation of PRF—The method of OLS—Advantages of OLS—Numerical Properties of OLS estimators— Statistical properties of OLS— Gauss - Markov Theorem and the assumptions of Classical Linear Regression Model

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  • Module 3

    Evaluation of SRF—Goodness of the Fit—R Square—Reliability and Precision of OLS estimators—Standard Error of the OLS Estimator and the Estimate

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  • Module 4

    Hypothesis testing and estimation—Hypothesis testing of OLS estimators—t test—Point and interval estimation (Basics)—Introduction to Multiple Regression

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  • Module 5

    Relaxing the assumptions of Classical Linear Regression Model Heteroscedasticity— nature, estimation in its presence—detection and remedial measures— Autocorrelation— nature and estimation in its presence—detection and remedial measures— Multicollinearity—nature, estimation in its presence—detection and remedial measures

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₹1,000.00