# Office of Academic AffairsIndian Institute of Science Education and Research Berhampur

Humanities and Social Sciences

HSS 304: Applied Econometric Analysis (3)

Learning Objectives:

1. The nature of econometrics and economic data.
2. The simple regression model and properties of OLS estimators
3. Multiple regression analysis - Estimation and Inference
4. Multiple regression analysis with Binary (dummy) variables
5. Basic regression analysis with time series data.
6. Statistical analysis with R programming.

Course Contents:

The nature of econometrics and economic data: definition of econometrics; cross-sectional data; time series data; panel data; causality; ceteris paribus.

The simple regression model and properties of OLS estimators: Deriving the ordinary least squares (OLS) estimates; fitted values, residuals and goodness-of-fit; units of measurement and functional form; unbiasedness and variances of OLS estimators; regression through origin.

Multiple regression analysis- Estimation and Inference: Deriving OLS estimates; interpreting the OLS regression equation; OLS fitted values, residuals and goodness-of-fit; omitted variable bias; multicollinearity; standard error of OLS estimators; efficiency of OLS – Gauss-Markov theorem; Testing hypotheses against one-sided alternatives, two-sided alternatives; p-value, t-test, F-test, confidence intervals; reporting linear regression results.

Multiple regression analysis with Binary (dummy) variables: using dummy variables for multiple categories; interactions among dummy variables; testing for differences in regression functions across groups; the linear probability model.

Basic regression analysis with time series data: Static models; finite distributed lag models; Gauss-Markov theorem; functional form, dummy variables and index number; trends and seasonality.

Statistical analysis with R programming: Upload, read, modify, create data; writing codes to run regressions; computing p-values, confidence intervals; plotting graphs