MSc — 2024 entry Economics (Econometrics and Big Data)

This module provides an in-depth introduction to the building blocks of graduate-level microeconomics. It introduces students to the modelling of economic behavior under certainty and uncertainty and the interplay among economic agents in markets.

This module provides an in-depth review of the building blocks of macroeconomic theories and introduces macroeconomic datasets at the graduate level. 

This module is an introduction to the methods of specification, estimation and testing of econometric models in a general multivariate setting. The techniques are applied to real data making use of the econometric packages.

The first part of the module is designed to provide the necessary foundation in mathematical and statistical techniques for the study of economics at graduate level. The second part provides an introduction to programming using specialist programming software. Students will learn how to use numerical methods in the context of mathematic optimisation and data analysis.

The availability of high-dimensionality data sets has raised new challenges. Often, for a cross section of n individuals we may observe p individual characteristics, covariates, with p > n; i.e., the number of covariates is larger than the sample size. In this situation, standard econometric techniques fail to work. The key point is that most of the observed covariates have no predictive power and so we want to eliminate them. Data reduction is performed via regularised methods. Machine Learning provides tools for data reduction and for making out-of-sample prediction in the presence of high-dimensionality data, imposing very little structure on the data. Throughout the course, we overview the most popular machine learning methods, such as ridge regressions, LASSO (Least Absolute Shrinkage and Selection Operator), Regression Tree, Random Forest, Boosting and Bagging.

The main application of machine learning is out-of-sample prediction. Prediction accuracy is typically evaluated in terms of squared error, where the error is difference between the prediction and the actual realization. In certain situations, such as forecasts of inflation or output from the Bank of England, an accurate prediction is enough. However, there are situations, like policy evaluation, in which we care about causal effects. Suppose that the Secretary of Education introduces three additional hours of mathematics in primary school to increase student GSE scores. Here the objective is to isolate the effect of additional hours of math on GSE score. In general, for each pupil, we have a lot of individual characteristics, which we need to control for. Data reduction techniques, such as LASSO, regression tree, random forest, help to eliminate all irrelevant information so that we can isolate the effect of the policy. This module is structured in two parts. In the first part, we review econometric tools for policy evaluation, such as instrumental variables, panel data, difference-in-difference, synthetic control, regression discontinuity. In the second part, we look at the same techniques when many instruments are available or many additional control variables are available.

This module provides an overview of research methods employed in economics, laying the foundations for the dissertation, and guides students on how to carry out independent research.