Econometrics

Unit – I

Nature and Scope of Econometrics and CLRM: Nature and Concept of Econometrics; Distinction between Economic Model and Econometric model; Concept of stochastic relation, Role of random disturbance in econometric model; Types of data; Classical Linear Regression Model (Simple linear regression and multiple linear regression) – the classical assumptions.

Unit – II

Estimation of Classical Linear Regression Model: Estimation of model by method of ordinary least squares (Derivation in simple linear model (SLRM) and multiple linear model (MLRM) with two regressors only); Simple correlation, partial correlation and multiple correlation (Definition, and interpretation in the context of SLRM and MLRM); Economic interpretations of the estimated model; Properties of the Least Squares Estimators (BLUE) in SLRM- Gauss-Markov theorem; Qualitative (dummy) independent variables – interpretation of slope dummy and intercept dummy.

Unit – III

Statistical Inference: Use of standard normal, χ2, t, and F distributions in linear regression model; hypothesis testing- basic concepts of null hypothesis, alternative hypothesis, type I and type II errors, power of a test, p-value; Confidence interval and level of significance; Single test (t test and χ2 test);Joint test (F test); Goodness of fit (in terms of R2, adjusted R2 and F statistic), Analysis of Variance (ANOVA).

Unit – IV

Violations of Classical Assumptions: Multicollinearity - Consequences, Detection (Variance Inflationary Factor (VIF)) and Remedies; Heteroskedasticity - Consequences, Detection (Lagrange Multiplier test) and Remedies; Autocorrelation - Consequences, Detection (Durbin-Watson test) and Remedies.