Checking date: 05/04/2024

Course: 2024/2025

Analysis of dynamic data
Bachelor in Business Administration (Plan: 395 - Estudio: 204)


Department assigned to the subject: Statistics Department

Type: Electives
ECTS Credits: 6.0 ECTS


Requirements (Subjects that are assumed to be known)
Statistics I Statistics II
1. Construction of forecasts for decision making in a context of uncertainty, in which managers need to take into account the consequences of all posibilities. 2. Represent the dynamic dependence of univariate and multivariate variables describing the main dynamic properties: trends, seasonal components and cicles. 3. Measure the dependence beween economic and financial variables observed along time. 4. Measure the volatility of financial variables to obtain, for example, the Value at Risk or forecast intervals for financial returns. Interpretation of data. Use of software designed for the analysis of data.
Skills and learning outcomes
Description of contents: programme
1. Introduction 1.1 Dynamic data in business administration problems 1.2 Objetives of the analysis of dynamic data: description of the evolution and forecasting 1.3 Diferences between temporal and cross-sectional data: dependence and heterogeneity 1.4 Stochastic processes: stationarity 1.5 Marginal and conditional distributions. Uncorrelatedness and independence 1.6 Examples: Sales, oil prices, IBEX prices 2. Linear models: Forecasting 2.1 ARMA models: properties 2.2 Fitting ARMA models: estimationa and diagnosis 2.3 Forecasting using ARMA models 2.4 Forecast evaluation 2.5 Evolution and forecasts of Google Trends variables 3. Multivariate models: relationships between variables 3.1 Caracteristics of VAR models 3.2 Dynamic regression models 3.3 Transfer functions 3.4 Forecasts in dynamic regression models 3.5 Cointegration: Equilibrium correction models 3.6 Measuring the dynamic relationship beween international prices 4. Models for volatilities 4.1 Empirical caracteristics of financial variables 4.2 Properties of GARCH models 4.3 Forecasting volatilities: Computing Value at risk 4.4 Analysis of IBEX returns 4.5 Multivariate GARCH models 4.6 Correlations between exchange rate returns: Portfolio management
Learning activities and methodology
The course will have a face-to-face part classroom where both blackboard and audiovisual media are used to present the main concepts. In addition, there will be practical classes in computer classrooms where students will learn to use the software necessary to implement models in real data.
Assessment System
  • % end-of-term-examination 60
  • % of continuous assessment (assigments, laboratory, practicals...) 40

Calendar of Continuous assessment

Extraordinary call: regulations
Basic Bibliography
  • BROOKS, C.. Introductory Econometrics for finance. Cambridge University Press (2002).
  • González-Rivera, G.. Forecasting for Economics and Business. Pearson/Addison-Wesley. 2013
Additional Bibliography
  • MILLS, C.T.. The econometric modelling of financial time series. Cambridge University Press (1999).

The course syllabus may change due academic events or other reasons.