3 Hours/Week, 3 Credits

Multiple regression and linear estimation: generalized and weighted least squares. Gauss-Markov Aitkenís theorem. Estimation and tests for linear restriction. Heteroscedasticity: detection and testing for heteroscedasticity, estimation with heteroscedastic disturbances. Multicollinearity: concept of exact and near multicollinearity, estimable functions, effects of multicollinearity, detection and remedial measures of multicollinearity. Autocorrelation: sources and consequences of autocorrelation, tests for autocorrelated disturbances, estimation of parameters. Dummy variables: general concepts, use of dummy variables in regression analysis. Errors in variables: basic ideas, consequences and tests for error in variables, estimation of parameters. Binary models, selection of variables.