Call for application for the course Advanced Benchmarking Models and Techniques is now open. Deadline for a request of participation: 10 of March 2021 The course is organized by the DIAG Department of Sapienza University of Rome and will take place on the Meet Platform 22-25-29 March 2021. Given their high flexibility and the recent developments introduced in the methodological literature, quantitative benchmarking techniques are suited for being applied to all the different datasets available and that will be developed…
The third RISIS Research Seminar will take place on 11th November from 12:30 am to 2:00 pm (CET) and will focus on R&D networks and their effects on heterogeneous modes of knowledge creation: evidence from a spatial econometric perspective with a presentation of Dr. Martina Neuländtner and Dr. Thomas Scherngell, AIT (Austrian Institute of Technology). Dr.Rune Fitjar, University of Stavanger, is involved as a discussant. The ’beneficial’ effect of R&D networks on regional knowledge creation is…
Providing the essential tools for a correct application of some popular Network Science, Spatial Models and Machine Learning methods in various STI contexts. This is the main purpose of RISIS online SCHOOL on DATA SCIENCE organized by CNR-IRCrES in collaboration with AIT, planned in virtual form from 15th to 26th of February 2021. In particular: Machine Learning techniques prove useful for factor importance detection, as well as for classification purposes in a model-free…
The course objective is to introduce the main features of panel data econometric models and present use cases and examples based on the VICO dataset. Topics covered in the course include specification, estimation, and inference in the context of models that take into account unobserved heterogeneity and individual effects (e.g. fixed-effects vs. random-effects models). Call for application for the course on Panel Data Methods and Applications is now open. Deadline for a request of participation: 15th of October 2020 …
The course objectives are the introduction of basic concepts and rationales of multilevel analysis, the understanding of the applicability of the method, specifically to datasets in research policy and higher education studies, know and practice on the basic steps for conducting analysis with statistical software, and the developing of competence in interpreting results and driving implications for analysis and policy. Call for application for the course on Applications of multilevel models to research policy and higher education…
