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Analysis of Dynamic Data
Engineering & Social Sciences Program
Madrid, Spain
Dates: 1/16/25 - 6/4/25
Analysis of Dynamic Data
OVERVIEW
CEA CAPA Partner Institution: Universidad Carlos III de Madrid
Location: Madrid, Spain
Primary Subject Area: Business
Instruction in: English
Course Code: 14058
Transcript Source: Partner Institution
Course Details: Level 300, 400
Recommended Semester Credits: 3
Contact Hours: 42
Prerequisites: Statistics I, Statistics II
DESCRIPTION
1. Introduction
1.1 Dynamic data in business administration problems
1.2 Objectives of the analysis of dynamic data: description of the evolution and forecasting
1.3 Differences 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 Characteristics 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 between international prices
4. Models for volatilities
4.1 Empirical characteristics 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
1.1 Dynamic data in business administration problems
1.2 Objectives of the analysis of dynamic data: description of the evolution and forecasting
1.3 Differences 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 Characteristics 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 between international prices
4. Models for volatilities
4.1 Empirical characteristics 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