This course will involve a detailed understanding of the mechanics, advantages, and the limits/limitations of the “classical” linear regression model. Where relevant, questions of methodology will be discussed. The first part of the course will cover the theoretical and applied statistical principles which underlie Ordinary Lest Squares (OLS) regression techniques. This part will cover the assumptions needed to obtain the Best Linear Unbiased Estimates of a regression equation – also known as the “BLUE” conditions. Particular emphasis will be placed on the assumptions regarding the distribution of a model’s error term and other BLUE conditions. We will also cover hypothesis testing, sample selection, and the critical role of the t and F-statistic in determining the statistical significance of an econometric model and its associated slope or “b” parameters. The second part of the course will address the three main problems associated with the violation of a particular BLUE assumption: multicollinearity, autocorrelation, and heteroscedasticity. We will learn how to identify, address, and (hopefully) remedy each of these problems. In addition, we will take a similar approach to understanding and correcting model specification errors. The third part of the course will focus on the econometrics of time-series models including Granger causality, error-correction models, and co-integration.