A consistent specification test for models defined by conditional moment restrictions

This article addresses statistical inference in models defined by conditional moment restrictions. Our motivation comes from two observations. First, generalized method of moments, which is the most popular methodology for statistical inference for these models, provides a unified methodology for statistical inference, but it yields inconsistent statistical procedures. Second, consistent specification testing for these models has abandoned a unified approach by regarding as unrelated parameter estimation and model checking. In this article, we provide a consistent specification test, which allows us to propose a simple unified methodology that yields consistent statistical procedures. Although the test enjoys optimality properties, the asymptotic distribution of the considered test statistic depends on the specific data generating process. Therefore, standard asymptotic inference procedures are not feasible. Nevertheless, we show that a simple original wild bootstrap procedure properly estimates the asymptotic null distribution of the test statistic

A CONSISTENT SPECIFICATION TEST FOR MODELS DEFINED BY CONDITIONAL MOMENT RESTRICTIONS

This article addresses statistical inference in models defined by conditional moment restrictions. Our motivation comes from two observations. First, generalized method of moments, which is the most popular methodology for statistical inference for these models, provides a unified methodology for statistical inference, but it yields inconsistent statistical procedures. Second, consistent specification testing for these models has abandoned a unified approach by regarding as unrelated parameter estimation and model checking. In this article, we provide a consistent specification test, which allows us to propose a simple unified methodology that yields consistent statistical procedures. Although the test enjoys optimality properties, the asymptotic distribution of the considered test statistic depends on the specific data generating process. Therefore, standard asymptotic inference procedures are not feasible. Nevertheless, we show that a simple original wild bootstrap procedure properly estimates the asymptotic null distribution of the test statistic.

A Consistent Test for the Martingale Difference Hypothesis

This paper considers testing that an economic time series follows a martingale difference process. The martingale difference hypothesis has been typically tested using information contained in the second moments of a process, that is, using test statistics based on the sample autocovariances or in the periodograms. Tests based on these statistics are inconsistent since they just test necessary conditions of the null hypothesis. In this paper we consider tests that are consistent against all fixed alternatives and against Pitman's local alternatives. Since the asymptotic distributions of the tests statistics depend on the data generating process, the tests are implemented using a modification of the wild bootstrap procedure. The paper justifies theoretically the proposed tests and examines their finite sample behavior by means of Monte Carlo experiments. In addition we include an application to exchange rate data.nonlinear dependence,nonparametric, correlation, bootstrap

Software use & integration in CI/CD to reduce vulnerabilites and perform work/stress load

This final degree project deals with the use of tools in order to find security vulnerabilities and performance limitations in order to make applications more robust and solid against attacks. In addition, it also explains how to integrate these tools in continuous integration and continuous delivery environments. The tools selected for the job are Zap (security vulnerabilities), Gatling (performance limitations) and Jenkins (CI/CD environment integration). This project consists of a guide of the configuration and use of all the tools, a sample of results and an in-depth analysis of a simulated environment. There is also a mention about how the work has been organized and it ends with a critical conclusion about the tools used and how the project has been done

La STJUE Brüstle y sus implicaciones para la protección jurídica de las invenciones biotecnológicas

La STJUE Brüstle resuelve una cuestión prejudicial planteada por el Tribunal Supremo alemán sobre la interpretación del artículo 6.2.c de la Directiva 98/44/CE del Parlamento Europeo y del Consejo, de 6 de julio de 1998, relativa a la protección jurídica de las invenciones biotecnológicas. De acuerdo con la sentencia, el concepto de embrión humano se debe interpretar en un sentido amplio, y en general siempre que se intente patentar material obtenido de un embrión se producirá una utilización con fines industriales o comerciales. Si en el desarrollo de la invención se han destruido embriones humanos, la patente se considerará contraria al orden público.Brüstle Judgment resolves a preliminary ruling posed by the German Supreme Court regarding the interpretation of Article 6.2.c of the Directive EC/98/44 of the European Parliament and Council, on the legal protection for biotechnological inventions. The decision interprets widely the definition of human embryo. It also affirms that it is not possible to obtain a patent for human embryos as this would imply the use of these embryos with industrial or commercial purposes. According to the Court patents are contrary to ordre public when they imply the destruction of human embryo

Towards an interactive framework for robot dancing applications

Estágio realizado no INESC-Porto e orientado pelo Prof. Doutor Fabien GouyonTese de mestrado integrado. Engenharia Electrotécnica e de Computadores - Major Telecomunicações. Faculdade de Engenharia. Universidade do Porto. 200

Size Corrected Power for Bootstrap Tests

This note provides an alternative perspective for size-corrected power for a test. The advantage of this approach is that it allows the calculation of size-corrected power for bootstrap tests.Size-adjusted power, Monte Carlo