Course Topics
1. The linear regression model - Simple/multiple linear regression - Estimation and inference on regression parameters - Goodness of fit and multicollinearity - Residue analysis and diagnostics 2. ARIMA models - AR model - MA model - ARMA model - Forecast 3. GARCH models - volatility analysis - Tests for ARCH effects - ARCH/GARCH models

Teaching format
Lectures and exercises

Educational objectives
ILO (Intended Learning Outcomes) ILO 1 Knowledge and understanding ILO 1.1 knowledge of basic and intermediate level mathematical tools for understanding and analysing economic mechanisms through theoretical models and empirical applications ILO 1.2 knowledge of tools for static, dynamic, and comparative analysis of data on individuals, firms and economies ILO 1.3 knowledge and understanding of descriptive statistics, the fundamentals of probability theory and sample methods, standard distributions and their application to economic analysis as well as linear and non-linear regression ILO 2 Ability to apply knowledge and understanding ILO 2.1 know how to analyse economic data using descriptive statics, parametric and non-parametric methods as well as linear and non-linear regression and interpret the results ILO 2.2 know how to set up and carry out an empirical project using econometric software and financial or economic databases ILO 2.3 knowing how to use IT tools for the analysis of economies ILO 3 Autonomy of judgement ILO 3.1 choose the most appropriate quantitative and qualitative methods of analysis ILO 3.2 find the necessary information in databases, legal sources and scientific literature ILO 3.3 using logical reasoning to combine information and analytical methods, also using modern software packages, to arrive at a solution ILO 4 Learning ability ILO 4.1 retrieve information from databases, scientific literature, laws and regulations as required in professional life ILO 4.2 to analyse, critically process and integrate data, information and future experience, also using advanced software

Additional educational objectives and learning outcomes
Knowledge and understanding: - Advanced knowledge and understanding of methods econometric methods related to common types of data financial and corporate data. Application of knowledge and understanding: - Ability to apply econometric methods to real types of financial data using specific software. - Ability to interpret analysis results in the context of common financial and business problems. Formulation of judgements: - Ability to think critically and make effective decisions based on appropriate econometric analysis appropriate. Communication skills: - Ability to effectively communicate the results of econometric analyses, even to a non-specialist audience. non-specialists.

Assessment
(60% of the final subject grade): Final written examination (40% of the final subject grade): Group project Analysis of a real data set using the R sofware ILO assessed 1 -- 4

Evaluation criteria
Final written examination: 60% Project: 40% Students must pass the final examination (i.e. correctly answer at least 60% of the questions in the questions in the exam) to obtain a passing grade in the course. course.

Required readings

Jim H. Stock and Mark W. Watson, Introduction to

Econometrics, Pearson International 4th Edition.

Supplementary readings

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Further information
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