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This chapter focuses on the least squares method for estimating parameters in linear regression models, discussing its statistical properties, including unbiasedness, consistency, and efficiency. It also addresses practical issues such as multicollinearity and missing data, while exploring both finite-sample and asymptotic properties of the estimator. The chapter concludes with a discussion on robust inference methods and the importance of understanding the assumptions underlying the classical linear regres

Author

micol muslera

Language

EN