1. To acquaint the students with the statistical concepts used in econometrics.
2. To estimate and interpret the simple and multiple linear regression models.
3. To acquaint the students with the estimation and use of various functional forms of regression models
· Nature, scope and methodology of econometrics, types of econometrics
· Statistical concepts- Normal distribution; chi-square, t- and F-distributions; estimation of parameters; properties of estimators; testing of hypotheses: defining statistical hypotheses, confidence interval and test of significance approaches, Type I and Type II errors; power of a test. Time series, cross-section, pooled and panel data
· Nature of regression analysis - PRF and SRF
· Assumptions of Classical Linear Regression Model
· Significance of the stochastic disturbance term
· Estimation of model by method of ordinary least squares
· Reporting and interpretation of regression results
· Properties of least square estimators, Gauss-Markov theorem
· Goodness of fit- R2 and adjusted R 2
· Tests of hypotheses
· Scaling and units of measurement
· Confidence intervals
· Chow Test; Prediction
· Log-linear model
· Semi log models – lin-log model and log-lin model
· Reciprocal models
· Logarithmic reciprocal model
· Choice of functional form
· Regression through origin
· Estimation of parameters
· Properties of OLS estimators
· Partial regression coefficients
· Goodness of fit - R2 and adjusted R 2
· Testing hypotheses – individual and joint
3. Koutsoyiannis, A., Theory of Econometrics, Palgrave Macmillan, 2nd Edition, 2001.