Contiene: Introduction. Matrix algebra. Probability and distribution theory. Statistical inference. Computation and optimization. The classical multiple linear regression model- Specification and estimation. Inference and prediction. Functional form, nonlinearity, and specification. Data problems. Nonlinear regression models. Nonspherical disturbances, generalized regression, and GMM estimation. Heteroscedasticity. Autocorrelated disturbances. Models for panel data. Systems of regression equations. Simultaneous equations models. Regressions with lagged variables. Time-series models. Models with discrete dependent variables. Limited dependent variable and durations models.
