- Learning to apply econometric methods in an empirical context related to innovation.

- Training to discern between alternative methods of analysis.

- Competency in the use of statistical and econometric software.

- A certain degree of development of their capacity to carry out their own empirical research.

.- Developing the ability to choose the most suitable econometric techniques between those known for data analysis in a particular case. It covers competence CE.4 of the Bachelor’s Degree in Economics.

- Learning to apply the econometric methods studied in an empirical context related to innovation, maintaining a critical sense when assessing the uncertainty associated to the results. It covers competence CE.3 of the Bachelor’s Degree in Economics.

- Ability to judge the results of econometric analyses obtained by themselves, and/or to start conducting empirical researches leading to those results. It covers competences CE5, CE.10, CE.12 and CE.17 of the Bachelor’s Degree in Economics.

- Competency to read and communicate effectively in English within a professional environment. It covers competence CE. 14 of the Bachelor’s Degree in Economics.

Specifically, the following skills are developed:

- Autonomous learning ability. It covers competence C.23 of the Bachelor’s Degree in Economics.

- Adaptability to new situations. It covers competence C.24 of the Bachelor’s Degree in Economics.

- Ability to develop scientific criticism and self-criticism. It covers competence C.25 of the Bachelor’s Degree in Economics.

- Definition of key economic concepts related to innovation.

- Analysis of real-world scenarios concerning innovation.

- Use of databases.

- Empirical applications of statistical and econometric techniques for modelling innovation data.

- Analysis and interpretation of finding from empirical studies.

- Role of innovation in economic growth.

- Evaluation of the relevance of innovation policies.

Fagerberg, J. and D.C. Mowery (2006). The Oxford Handbook of Innovation. Oxford University Press.

Fagerberg, J., Mowery, D.C. and R. Nelson (2009). The Oxford Handbook of Innovation. Oxford University Press.

Hamilton, J. (1994. Time Series Analysis. Princeton.

Hsiao, C. (2007). Analysis of Panel Data. Cambridge University Press.

Wooldridge, J.M. (2010). Econometric Analysis of Cross Section and Panel Data Modeling. The MIT Press.

Arundel, A. and R. Garrelfs (1997), Innovation Measurement and Policies. European Commission, Luxembourg, EIMS publication 50.

Arundel, A. and I. Kabla (1998), “What percentage of innovations are patented? Empirical estimates for European firms”, Research Policy 27, 127–141.

Baltagi, B.H. (2013), Econometric analysis of panel data. Wiley. 5^th edition.

Boeing, P. and P. Hünermund (2020), “A global decline in research productivity? Evidence from China and Germany”, Economics Letters 197, 109646.

European Commission (2003), The European Report on Science and Technology Indicators. European Commission, Brussels.

Hünermund, P. and D. Czarnitzki, D. (2019), “Estimating the Causal Effect of R&D Subsidies in a Pan-European Program”, Research Policy 48, 115–124.

Kline, S.J. and N. Rosenberg (1986), “An Overview of innovation” in R. Landau and N. Rosenberg (eds.) The positive sum strategy, National Academy Press, Washington D.C., pp. 275-304.

Licht, G. and K. Zoz (1996), “Patents and R&D: an econometric investigation using applications for German, European, and US patents by German companies”. ZEW Discussion Paper No. 19.

Assessment criteria:

• 40% of the final grade (continuous assessment grade): evaluation of the student work during the course, including the task of writing an essay/paper and giving a presentation.
• 60% of the final grade: writing a final exam.

To pass the course, a definitive grade equal to or higher than 5 (out of 10) is required, without a minimum of grade in the continuous assessment or in the final exam (but always the sum of both must be equal to or higher than 5).

The assessment criteria will be the same in the ordinary call and the extraordinary call. Since the continuous assessment requires a follow-up throughout the course, you cannot resit this part in the extraordinary call or in the calls corresponding to the early evaluation.

Class attendance is mandatory and, consequently, is not part of the final grade.

The student must have fulfilled 80% of their face-to-face dedication so that their work is considered part of the final grade.

- Questionnaires.

- In-class assessment.

- Essay/paper.

- Presentations.

- Final exam.

Consideraciones Generales.

The course requires an average dedication of 150 hours (6 ECTS): 45 hours (30%) correspond to face-to-face dedication or mandatory interaction with the teacher and 105 hours (70%) correspond to autonomous work.

Recomendaciones para la evaluación

It is recommended that the student strives to pass the continuous assessment.

Recommendation for "second-chance" assessment

The grade obtained in the continuous assessment cannot be resat and will be maintained for all the calls that are made during the corresponding academic year, as well as for the call for early evaluation corresponding to the next academic year or the successive ones.

Recomendaciones para la recuperación.

The grade obtained in the continuous assessment cannot be resat and will be maintained for all the calls that are made during the corresponding academic year, as well as for the call for early evaluation corresponding to the next academic year or the successive ones.