杏吧论坛

Dr Vassilis Hajivassiliou

Ragvir Sabharwal

Dr Rachael Meager 

This course is compulsory on the MRes/PhD in Accounting (EoA) (Economics of Accounting Track) , MSc in Economics and MSc in Economics (2 Year Programme). This course is available on the MPhil/PhD in Environmental Economics, MSc in Economics and Philosophy and MSc in Quantitative Economic History. This course is available with permission as an outside option to students on other programmes where regulations permit.

Students must have completed Introductory Course in Mathematics and Statistics (EC400).

Students should also have completed an undergraduate degree or equivalent in Economics and an introductory course in Econometrics.

In very exceptional circumstances, students may take this course without EC400 provided they meet the necessary requirements and have received approval from the course conveners (via an online* face to face meeting), the MSc Economics Programme Director and their own Programme Director. Contact the Department of Economics for more information (econ.msc@lse.ac.uk).

The course aims to present and illustrate the techniques of empirical investigation in economics.

• Regression models with fixed regressors (simple and multiple). Least squares and other estimation methods. Goodness of fit and hypothesis testing.
• Regression models with stochastic regressors.
• Asymptotic theory and its application to the regression model. Sampling error vectors. Large sample approximations.
• The partitioned regression model, multicollinearity, misspecification, omitted and added variables, measurement errors.
• Generalized method of moments.
• Maximum likelihood estimation.
• Heteroskedasticity, autocorrelation, and generalized least squares.
• Exogeneity, endogeneity, and instrumental variables. The leading causes of endogeneity.
• Nonlinear regression modelling
• Binary choice models and other Limited Dependent Variables models.
• An introduction to Non-classical econometric inference.
• Autoregressive and moving average representations of time series. Stationarity and invertibility.
• Ergodicity, Laws of Large Numbers, and Central Limit Theorems for Time Series
• Vector auto-regressions.
• Unit roots and co-integ