- 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.

K2: Know basic humanistic contents, oral and written expression, following ethical principles and completing a multidisciplinary training profile. K3: Understand the functioning of the company's activity, and know how to identify areas for improvement through the use of Statistics, Data Science and Operations Research K11: Know the methodology that allows to statistically describe a set of data from numerical measures and graphs, both univariate and multivariate, highlighting possible relationships between variables of interest. K12: now how to identify or be able to create the statistical or probabilistic model appropriate to the specific problem arising in each business activity (finance, marketing, production planning and control, etc.). C1: Develop and master interpersonal skills on initiative, responsibility, conflict resolution and negotiation, which are essential in the professional environment. C2: Ability to efficiently manage the information contained in a company's databases for statistical use, as well as to know how to design the process of acquiring new information (data) useful for the company. C3: derivados o la estimación del movimiento del tipo de interés y/o de los tipos de cambio/ Ability to use financial tools to solve problems such as estimating risk, calculating the cost of capital, valuing assets and/or derivatives, or estimating the movement of interest and/or exchange rates. C4: Ability to develop and validate statistical models that help to address and solve problems relevant to today's society. S1: To plan and organize team work making the right decisions based on available information and gathering data in digital environments. S2: To use information interpreting relevant data avoiding plagiarism, and in accordance with the academic and professional conventions of the area of study, being able to assess the reliability and quality of such information. S5: Manipulate computationally and analytically the established models, taking advantage of the power of statistical methods, optimization, etc., and perform the analysis of the results obtained. S6: Communicate the results, the conclusions of the models and the proposed solutions in a way that is intelligible to the rest of the company, so that they are accepted and implemented by the decision-makers

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.