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 of significance

This paper presents a test of significance consistent under nonparametric alternatives. Under the null hypothesis, a regressor has no effect on the regression model. Our statistic does not require to estimate the model on the alternative hypothesis, which is left unspecified. Hence, no smoothing techniques are required. The statistic is a weighted empirical process which resembles the Cram~r-von Mises. The asymptotic test is consistent under Pitman's alternatives converging to the null at arate n-1/2. A Monte-Cario experiment illustrates the performance ofthe test in small samples. We also inelude two applications involving biomedical and acid rain data

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.

Consistent tests of conditional moment restrictions

We propose two classes of consistent tests in parametric econometric models defined through multiple conditional moment restrictions. The first type of tests relies on nonparametric estimation, while the second relies on a functional of a marked empirical process. For both tests, a simulation procedure for obtaining critical values is shown to be asymptotically valid. Finite sample performances of the tests are investigated by means of several Monte-Carlo experiments.Publicad

Asymptotic and bootstrap specification tests of nonlinear in variable econometric models

We address the issue of consistent specification testing in general econometric models definedı by multiple moment conditions. We develop two c1asses of moment conditions based tests. The first class of tests depends upon nonparametric functions that are estimated by kernel smoothers. The second class of tests depends upon a marked empirical process. Asymptotic and bootstrap versions of these tests are formally justified, and their finite sample performances are investigated by means of Monte-CarIo experiments

Applying stress-testing on value at risk (VaR) methodologies

In recent years, Value at Risk (VaR) methodologies, i. e., Parametric VaR, Historical Simulation and the Monte Carlo Simulation have experienced spectacular growth within the new regulatory framework which is Basle II. Moreover, complementary analyses such a Stress-testing and Back-testing have also demonstrated their usefulness for financial risk managers. In this paper, we develop an empirical Stress-Testing exercise by using two historical scenarios of crisis. In particular, we analyze the impact of the 11-S attacks (2001) and the Latin America crisis (2002) on the level of risk, previously calculated by different statistical methods. Consequently, we have selected a Spanish stock portfolio in order to focus on market risk

Deep Neural Networks for the Recognition and Classification of Heart Murmurs Using Neuromorphic Auditory Sensors

Auscultation is one of the most used techniques for detecting cardiovascular diseases, which is one of the main causes of death in the world. Heart murmurs are the most common abnormal finding when a patient visits the physician for auscultation. These heart sounds can either be innocent, which are harmless, or abnormal, which may be a sign of a more serious heart condition. However, the accuracy rate of primary care physicians and expert cardiologists when auscultating is not good enough to avoid most of both type-I (healthy patients are sent for echocardiogram) and type-II (pathological patients are sent home without medication or treatment) errors made. In this paper, the authors present a novel convolutional neural network based tool for classifying between healthy people and pathological patients using a neuromorphic auditory sensor for FPGA that is able to decompose the audio into frequency bands in real time. For this purpose, different networks have been trained with the heart murmur information contained in heart sound recordings obtained from nine different heart sound databases sourced from multiple research groups. These samples are segmented and preprocessed using the neuromorphic auditory sensor to decompose their audio information into frequency bands and, after that, sonogram images with the same size are generated. These images have been used to train and test different convolutional neural network architectures. The best results have been obtained with a modified version of the AlexNet model, achieving 97% accuracy (specificity: 95.12%, sensitivity: 93.20%, PhysioNet/CinC Challenge 2016 score: 0.9416). This tool could aid cardiologists and primary care physicians in the auscultation process, improving the decision making task and reducing type-I and type-II errors.Ministerio de Economía y Competitividad TEC2016-77785-

NAVIS: Neuromorphic Auditory VISualizer Tool

This software presents diverse utilities to perform the first post-processing layer taking the neuromorphic auditory sensors (NAS) information. The used NAS implements in FPGA a cascade filters architecture, imitating the behavior of the basilar membrane and inner hair cells and working with the sound information decomposed into its frequency components as spike streams. The well-known neuromorphic hardware interface Address-Event-Representation (AER) is used to propagate auditory information out of the NAS, emulating the auditory vestibular nerve. Using the information packetized into aedat files, which are generated through the jAER software plus an AER to USB computer interface, NAVIS implements a set of graphs that allows to represent the auditory information as cochleograms, histograms, sonograms, etc. It can also split the auditory information into different sets depending on the activity level of the spike streams. The main contribution of this software tool is that it allows complex audio post-processing treatments and representations, which is a novelty for spike-based systems in the neuromorphic community and it will help neuromorphic engineers to build sets for training spiking neural networks (SNN).Ministerio de Economía y Competitividad TEC2012-37868-C04-0