Persona: Matilla García, Mariano
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0000-0002-6007-3522
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Matilla García
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Mariano
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Publicación Banks and financial discrimination: what can be learnt from the spanish experience?(Springer, 2019-04-15) Fernández Olit, Beatriz; Ruza Paz-Curbera, Cristina; Cuesta González, Marta María de la; Matilla García, MarianoThe paper analyses the phenomenon of financial discrimination that have been identified in many developed countries in the aftermath of the financial crisis. We would consider the process of quality worsening in the provision of banking products and services as part of the increasing problem of financial exclusion, which should consider not only the physical access to branches but also the difficulties of use of banking services and products. Our primary concern is focused on the collective of vulnerable customers, so we have carried out an analysis at a micro-scale (urban districts and municipalities) to identify the main determinants of the financial discrimination of territories according to their socioeconomic profile. This study constitutes a first attempt to analyse financial discrimination in the provision of banking products and services at an urban micro-scale. We have considered as good references the cases of Madrid and Barcelona in Spain, large urban territories with high level of social inequality. The methodology that had been applied is quantile regressions, useful technique for analysing the ‘extreme’ nature of the phenomenon of financial discrimination. Our results confirm that the more overloaded branches are settled in districts characterised by a lower socioeconomic profile, indicating a banking industry trend towards ‘low-cost’ retail banking to serve the group of less profitable – more vulnerable customers. Some recommendations are outlined for policymakers in line with the aims and scope of the Payment Accounts Directive of the European Union.Publicación A test for deterministic dynamics in spatial processes(2019-01-19) García-Córdoba, Jose A.; Matilla García, Mariano; Ruiz Marín, ManuelWe propose a statistical procedure to determine if a spatial structure that is observed in the data is generated by a deterministic (even chaotic) spatial process, rather than by a stochastic process. This procedure can be used as a specification test. It is robust against nonlinearity and nonstationarity and can complete the toolbox for testing diagnosis as well. The advantages of the presented methods are high power, simplicity, and ease and ample applicability for tests to be conducted, provided that weak conditions are required. Herein, we conduct several simulations to evaluate the performance of our procedure on well-known spatial processes and in situations where standard tests for spatial autocorrelation fail to detect spatial dependence. Guidelines for using the technique are also provided herein.Publicación Symbolic correlation integral(0747-4938; eISSN: 1532-4168, 2017-09-26) Caballero Pintado, María Victoria; Matilla-García, M; Ruís Marín, Manuel; Matilla García, MarianoThis paper aims to introduce the concept of symbolic correlation integral SC that is extensively used in many scientific fields. The new correlation integral SC avoids the noisy parameter 𝜀 of the classical correlation integral, defined by Grassberger and Procaccia (Citation1983) and extensively used for constructing correlation-integral-based statistics, as in the BDS test. Once the free parameter 𝜀 disappears, it is possible to construct a nonparametric powerful test for independence that can also be used as a diagnostic tool for model selection. The symbolic correlation integral is also extended to deal with multivariate models, and a test for causality is proposed as an example of the theoretical power of the new concept. With extensive Monte Carlo simulations, the paper shows the good size and power performance of symbolic correlation-integral-based tests.Publicación Multibenchmark reality checks(Elsevier, 2024-11) Arbués, Ignacio; Matilla García, MarianoEmpirical economic modelers often have to choose between two classes of models, with each class containing multiple models. In many cases, this decision is based on the predictive ability of the considered models. This entails that multiple testing and/or p-hacking pose known risks. This study presents a new statistical approach for comparing all model in a single test, serving as a multi-benchmark reality check test. The behavior of the test is studied asymptotically and in small finite samples. We show how the new approach works by analyzing whether one family of linear bivariate models outperforms a univariate family in predicting commodity prices. This paper raises new questions for future research. From an empirical viewpoint, we present several open questions in economic modeling that can be tested with multi-benchmark tests. Meanwhile, from a theoretical viewpoint, further studies can investigate whether a more general method for approximating or simulating the test distribution can be developed.Publicación Spatial partial causality(EDP Sciences, Società Italiana di Fisica Y Springer Berlin Heidelberg., 2021-12-20) Herrera Gomez, Marcos; Matilla García, Mariano; Ruiz Marin, ManuelThis paper, on the one hand, proposes a statistical technique to detect potential causal relationships when the researcher has georeferenced data but not time dimension, and, on the other, applies this new methodology to the analysis of potential partial causal determinants of home prices. In particular, we find that the direction of causality for home prices in California goes from the income level to prices.Publicación Non-parametric analysis of serial dependence in time series using ordinal patterns(Elsevier, 2022-04) Weiß, Christian H.; Ruiz Marín, Manuel; Keller, Karsten; Matilla García, MarianoA list of new tests for serial dependence based on ordinal patterns are provided. These new methods rely exclusively on the order structure of the data sets. Hence, the novel tests are stable under monotone transformations of the time series and robust against small perturbations or measurement errors. The standard asymptotic distributions are given, and their finite sample behavior under linear and non-linear departures from the null of independence are studied. Moreover, it is proved that under mild conditions, any ordinal-pattern-based test is nuisance free, which is appealing for modelling, as these tests can eventually be used as misspecification tests. This property is also analyzed for finite samples and illustrated through an empirical application. Much of the discussion is based on a detailed combinatorial analysis of ordinal pattern probabilities