- Statistical inference methods I - Statistical inference methods II

- The general objective is to understand the dynamic properties of time series and the analysis of the causal dynamic relationships existing between different time series in order to perform forecasts. - Transversal objectives: To interpret time series data. To use R or Python for data analysis.

1- Fundamentals of Time Series 1.1 Introduction to Time Series 1.2 Components and Analysis of Time Series 1.3 Transformations and Stationarity 2- Classical Time Series Models 2.1 ARIMA Models 2.2 Validation of Time Series Models 3- Vector Autoregressive Models (VAR) 3.1 Introduction to VAR Models 3.2 Estimation and Diagnosis of VAR Models 3.3 Advanced Applications of VAR Models 4- Cointegration and VAR Models with Error Correction Mechanism (VECM) 4.1 Cointegration in VAR Models 4.2 Introduction to VECM 4.3 Application of VECM Models

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.