| 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 | Manage complex information | |
| B3.1 | Work in a team collaboratively and with responsibility shared among multidisciplinary, multi-lingual and multicultural teams in complex environments. | |
| B3.2 | Foster interdependency and shared responsibility. | |
| B3.3 | Resolve disputes in a constructive manner. | |
| B5.3 | Apply new and advanced technologies with initiative and entrepreneurial spirit. | |
| 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. | |
| B3.3 |
Resolve disputes that arise during teamwork in a constructive manner. | |
| 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: Instrumental variables |
2.1 The problem of endogeneity 2.2 Omitted variables 2.3 Instrumental variables estimation 2.4 Testing overidentification restrictions 2.5 Examples |
| Tema 3: Heteroskedasticity |
3.1 The problem of heteroskedasticity 3.2 Testing for heteroskedasticity 3.3. Weighted Least Squares 3.4 Examples |
| Planning |
| Methodologies :: Tests | ||||||||||
| Competences | (*) Class hours |
Hours outside the classroom |
(**) Total hours | |||||||
| Introductory activities |
|
1 | 1 | 2 | ||||||
| Lecture |
|
30 | 41 | 71 | ||||||
| Personal tuition |
|
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 | Hablar de datos economicos |
| Lecture | Presentar y elaborar modelos estadísticos/econometricos |
| Personal tuition | Horas de consulta/visita. |
| Personalized attention |
| Description |
| La atención personalizada en la asignatura se plantea con horas de consulta(visita). |
| Assessment |
| Methodologies | Competences | Description | Weight | ||||
| Others |
| Other comments and second exam session | |||
|
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, |
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| 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.