This module aims to familiarise students with modern data analysis and econometrics for applied economics. We teach students how to carry out high-quality empirical research using economic data and how to interpret data analysis results to support economic policy decisions. We will cover the most important concepts of applied data analysis for economics such as: econometric model formulation, data search and study design, data measurement, linear regression, hypothesis testing, prediction, goodness-of-fit, model specification criteria and tests, regression with dummy variables and panel data regression analysis.

The aim of this course is to provide students with the skills required to undertake independent applied research using modern econometric methods. As such, a key component involves the development of the necessary practical skills to apply the approaches and methods developed in lectures to real data for the purpose of exploring interesting economics related research questions. The syllabus is as follows: 1. Introduction; what is econometrics; two variables regression: some basic ideas; economic data types and sources of data; 2. The two variables regression analysis: linear regression model; 3. CLRM: the assumptions underlying the method of OLS; the Gauss-Markov Theorem; 4. Interval estimation and hypothesis testing; 5. Prediction, goodness-of-fit, and modeling issues, log-linear models; 6. Multiple regression analysis: the problem of estimation; 7. Further inference in the multiple regression model; 8. Dummy variable regression models; 9. Relaxing the assumptions of classical model: multicollinearity; 10. Relaxing the assumptions of classical model: heteroscedasticity and autocorrelation; 11. Panel data models: pooled data model, fixed-effects and random-effects model;

On successful completion of this module, students will be able to: 1) Analyse and evaluate economic data using tools taught in lectures and lab; 2) Apply the appropriate methods of statistical analysis to real-life economic problems; 3) Interpret the results of statistical analysis and derive conclusions for economic policy.

On successful completion of this module, students will be able to: 1) Appreciate the range of available economic data sources; 2) Organise and measure economic data for the purpose of economic analysis; 3) Report and explain the results of economic data analysis.

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This module will be taught using real-world data, showing how to use data analytic techniques to a range of economic situations, thus encouraging knowledgably attributes. Furthermore, students will also be equipped with theoretical and practical skills through the use of both lectures and computer labs. The latter will involve students learning how to use contemporary software tools such as Stata or R. As part of the module assessment it is envisaged that students will articulate in these matters through written project submissions on economics related topics of their choice, which will encourage problem-solving, proactivity and innovative behaviour.