As the name suggests, the subject econometrics aims to measure economic relationship. Using economic data and applying mathematical and statistical tools, it provides empirical validity of abstract economic theory. However, application of econometrics is not confined in the domain of economics, rather widespread application of econometrics is possible in other social science and pure science domains also. After successful completion of the course, students would be able to formulate econometric model to analyze data and then would be able to establish any cause-effect relationship in their preferred areas of interest like economics, finance, management, engineering and science. An expertise in econometrics increases the job prospect of the students significantly.INTENDED AUDIENCE :Anyone interested in data analysisPREREQUISITES : Inferential statisticsINDUSTRIES SUPPORT :Banking, Analytics, Audit firms
Overview
Syllabus
Week 1: Introduction to Econometrics and Econometric Analysis, Steps involved in Econometric AnalysisWeek 2: Introduction to Classical Linear Regression Model- Two variable classical linear regression model, Assumptions of Classical Linear Regression ModelWeek 3: Classical Linear Regression Model assumptions, Estimation of the regression model, Properties of Ordinary Least Square estimatorsWeek 4: Regression analysis: Objective, Statistical Analysis and Interpretation of results, Hypothesis testing-Types of Hypothesis, Test statistic, Critical RegionWeek 5: Hypothesis testing: Level of significance and confidence interval approach; Goodness of Fit(R^2): Concepts of Explained Sum of Squares (ESS)-Residual Sum of Squares -Total Sum of SquaresWeek 6: Multiple Linear Regression Model: Interpretation of the model,Statistical Analysis, Interpretation of the resultsWeek 7: Model misspecification: R^2 vs Adjusted R^2 ; F statistics-Application of F statistics-Overall significance of the model-Equality between two regression coefficients-Testing the validity of linear restricted and Unrestricted modelsWeek 8: Application of F statistics: Testing structural break in Time Series data- Chow test, Limitations of chow test; Dummy Variable models: Introduction, Different types- ANOVA, ANCOVAWeek 9: Dummy variable models continued, Application of Difference-In-Difference for impact evaluation, Statistical Analysis of the Dummy variable modelsWeek 10: Dummy variable model for testing seasonal fluctuation: Introduction, Analysis, Dummy variable trap; Relaxing the assumptions of Classical Linear Regression Model: Multicollinearity-Introduction-Consequences-Detection-Remedial measures; Autocorrelation-Introduction-Consequences-Detection-Remedial measuresWeek 11: Heteroskedasticity: Introduction- Consequences-Detection-Remedial measures; Qualitative Response Models: Linear Probability Model,Logit ModelWeek 12: Qualitative Response Models: Probit model, Alternative measures of Goodness of Fit (R^2) in Qualitative response models, Logit vs Probit model selection, Limited dependent variable model/ Tobit Model
Taught by
Prof. Sabuj Kumar Mandal
