MSc Financial Economics / Course details

The aims of this course unit are to:

(i) introduce students to basic modeling techniques in the analysis of cross-section (survey) data;

(ii) develop these techniques to an advanced level;

(iii) provide sufficient background to enable students to read the applied literature in core journals which apply these techniques;

(iv) prepare students for a dissertation topic that analyses cross-section data

On completion of this unit successful students will be able to:

(i) demonstrate an understanding of some of the problems associated with cross-section data, namely selection bias and causal effects, the consequences of unobserved heterogeneity, and how they might be addressed, namely via instrumental variables estimation, "sample selection" techniques, or using panel data;

(ii) understand and apply some standard techniques when modeling cross section data;

(iii) also understand and apply techniques such as regression discontinuity;

(iv) apply these techniques to real data using the computer package STATA;

(v) interpret STATA output correctly

(1) why multiple regression? Revision of the multiple regression model (W ch 2; AP ch 2; W chs 3-7)

(2) matching estimators (AP ch 3.3.1)

(3) inference and standard errors: heteroskedasticity and clustering (W ch8.1-8.3; AP ch 3.1.3, ch 8)

(4) pooling cross sections across time (W, ch13)

(5) advanced panel data methods (W, ch14)

(6) instrumental variables and 2SLS (W ch, ch17.1, AP ch 4)

(7) regression discontinuity designs (AP ch6; Lee&Lemieux RDD Designs in Economics)

(8) sample selection and Heckman’s two-step estimator (W, ch17.5)