To enable students to acquire the knowledge and skills necessary to understand, specify, and estimate classical multiple regression models using real-world data and appropriate software tools.
Additional info
Subject and role of econometrics in economics. Economic data. Stages of econometric research. Simple linear regression model. Population regression function and sample regression function. Basic concepts of probability theory and inferential statistics. Software packages for econometric modeling.
Regression analysis with cross-sectional data: The OLS method and assumptions of the classical model. Properties of estimators, interpretation, and inference. Extensions – changes in units of measurement and functional forms.
Multiple regression models: Model specification, estimation, and interpretation. Measures of goodness of fit of a regression model. Testing the statistical significance of estimated parameters. Challenges and extensions: the effects of data scaling and incorporation of nonlinearity in analysis. Multiple regression analysis with qualitative data: binary variables. Heteroskedasticity. Regression analysis with time series: model specification, estimation, and interpretation.
