Multivariate conditional dependence: the effect of institutional quality on competitiveness indicator relations

• Autores: Jone Ascorbebeitia Bilbatua, Eva Ferreira García, Susan Orbe Mandaluniz
• Localización: Proceedings of the 35th International Workshop on Statistical Modelling : July 20-24, 2020 Bilbao, Basque Country, Spain / Itziar Irigoien Garbizu (ed. lit.), Dae-Jin Lee (ed. lit.), Joaquín Martínez Minaya (ed. lit.), María Xosé Rodríguez Álvarez (ed. lit.), 2020, ISBN 978-84-1319-267-3, págs. 8-13
• Idioma: inglés
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• Resumen

  • Nonparametric estimators for multivariate conditional copulas as well as for a multivariate conditional Kendall’s tau are proposed in a random design context. We also propose a flexible Wald type statistic based on Kendall’s tau estimator to test for the influence of a conditioning variable outcome in the joint distribution between two or more variables. Asymptotic properties of the estimators are derived together with a simulation study, and a data-driven smoothing parameter selection is also provided. A second simulation study presents different models to check the size and power of the test and runs comparisons with previous proposals when appropriate. For the empirical illustration, we study the relationship between some indicators from the European Regional competitiveness index (RCI). We find interesting results, such as weaker links between innovation and higher education in regions with lower institutional quality. Analyzing this kind of comovements is very useful for regulatory purposes to measure the impact of economic policies