| Competences |
| Type A | Code | Competences Specific |
| A1.4 | Apply advanced methods of theoretical and empirical analysis to company decision-taking. | |
| A1.5 | Analyse the complexity of the micro- and macroeconomic and legal environment of technology and innovation companies in a changing and ill-defined context. | |
| Type B | Code | Competences Transversal |
| B1.1 | Communicate complex ideas effectively to all sorts of audiences. | |
| B3.1 | Work in multidisciplinary teams and in complex contexts. | |
| B3.2 | Develop abilities to manage their professional career. | |
| B5.3 | Apply new technologies and advances with initiative and entrepreneurial spirit and manage and use information in an eficient manner. | |
| Type C | Code | Competences Nuclear |
| Learning outcomes |
| Type A | Code | Learning outcomes |
| A1.4 |
Understand and apply techniques for the empirical analysis of economic data. Interpret empirical analyses. Use methods of empirical analysis to prepare economic decisions. | |
| A1.5 |
Analyse national and international markets. | |
| Type B | Code | Learning outcomes |
| B1.1 |
Manage complex information. | |
| B3.1 |
Organise a team to carry out empirical analysis. | |
| B3.2 |
Take responsibility for the overall result of teamwork and not merely for the part that was done personally. Resolve disputes that arise during teamwork in a constructive, ethically responsible manner whilst taking into account the multicultural and multidisciplinary nature of the team | |
| B5.3 |
Apply latest-generation instruments for strategic analysis. | |
| Type C | Code | Learning outcomes |
| Contents |
| Topic | Sub-topic |
| Tema 1: Linear regression model | 1.1 Linear regression 1.2 Ordinary Least Squares 1.3 Misspecification problems 1.4 Inference and hypothesis testing 1.5 Examples |
| Tema 2: Dynamic regression |
2.1 Time series data 2.2 Dynamic models 2.3 Prediction 2.4 Examples |
| Tema 3: Endogeneity |
3.1 The problem of endogeneity 3.2 Instrumental variables estimation 3.3 Testing overidentification restrictions 3.4 Examples |
| Planning |
| Methodologies :: Tests | ||||||||||
| Competences | (*) Class hours |
Hours outside the classroom |
(**) Total hours | |||||||
| Introductory activities |
|
1 | 1 | 2 | ||||||
| Lecture |
|
27 | 44 | 71 | ||||||
| Personal attention |
|
2 | 0 | 2 | ||||||
| (*) On e-learning, hours of virtual attendance of the teacher. (**) The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. | ||||||||||
| Methodologies |
| Description | |
| Introductory activities | Analysis of economic data. |
| Lecture | Present and elaborate statistical/econometric models. |
| Personal attention | Office hours (consultation time). |
| Personalized attention |
| Description |
| I hold regular office hours (consultation time) after or before each class. |
| Assessment |
| Methodologies | Competences | Description | Weight | ||||
| Others | There are 3 assignment presentations after each topic (each counts 33.33% of the total mark). |
| Other comments and second exam session | |||
|
Subject's minimum attendance requirement is 80% of teaching and learning sessions for continuous assessment in first call. The 2nd exam call consists of an exam (100% of the total mark). |
|||
| Sources of information |
| Basic |
Wooldridge J.M, Introductory Econometrics: A Modern Approach, Thomson, |
||||||||
| Complementary | |||||||||
| Recommendations |
(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.