Cross-disciplinary problem-solving workshop: a pedagogical approach to anticipate ergonomist engineering design collaboration

The aims of this paper are to present concept and results of an innovative educational model approach based on ergonomics involvement in industrial project. First we present Cross disciplinary Problem solving Workshop by answering three questions:1) What is a CPW: A partnership between Universities and one or several companies, purposes of it are first to increase health, well being, companies teams competencies, and competitiveness, second to train the "IPOD generation" to include risks prevention in design. 2) How does it work? CPW allows cooperation between experience and new insight through inductive methods. This model follows the Piaget (1) philosophy linking concrete world to abstraction by a learning system associating realization and abstraction. 3) Is it successful? In order to answer this third question we will show examples of studies and models performed during CPWs.It appears that the CPWs produce visible results in companies such as new process designs, new methods, and also changes in lectures. However some less visible results remain unclear: How the company personnel evolve during and after CPW? Does CPW motivate our future engineers enough to continuously improve their skills in risk prevention and innovative design?     

GMM estimation of a realized stochastic volatility model: A Monte Carlo study

This article investigates alternative generalized method of moments (GMM) estimation procedures of a stochastic volatility model with realized volatility measures. The extended model can accommodate a more general correlation structure. General closed form moment conditions are derived to examine the model properties and to evaluate the performance of various GMM estimation procedures under Monte Carlo environment, including standard GMM, principal component GMM, robust GMM and regularized GMM. An application to five company stocks and one stock index is also provided for an empirical demonstration.     

On the Usefulness or Lack Thereof of Optimality Criteria for Structural Change Tests

Elliott and Müller (2006) considered the problem of testing for general types of parameter variations, including infrequent breaks. They developed a framework that yields optimal tests, in the sense that they nearly attain some local Gaussian power envelop. The main ingredient in their setup is that the variance of the process generating the changes in the parameters must go to zero at a fast rate. They recommended the so-called qL?L test, a partial sums type test based on the residuals obtained from the restricted model. We show that for breaks that are very small, its power is indeed higher than other tests, including the popular sup-Wald (SW) test. However, the differences are very minor. When the magnitude of change is moderate to large, the power of the test is very low in the context of a regression with lagged dependent variables or when a correction is applied to account for serial correlation in the errors. In many cases, the power goes to zero as the magnitude of change increases. The power of the SW test does not show this non-monotonicity and its power is far superior to the qL?L test when the break is not very small. We claim that the optimality of the qL?L test does not come from the properties of the test statistics but the criterion adopted, which is not useful to analyze structural change tests. Instead, we use fixed-break size asymptotic approximations to assess the relative efficiency or power of the two tests. When doing so, it is shown that the SW test indeed dominates the qL?L test and, in many cases, the latter has zero relative asymptotic efficiency.     

Nonstationarity and Level Shifts With an Application to Purchasing Power Parity

This study considers testing for a unit root in a time series characterized by a structural change in its mean. The analysis is in the spirit of Perron (1990a), who showed that the existence of such a shift in a stationary time series biases the usual tests for a unit root toward nonrejection. The approach is, however, different given that we suppose the date of the change to be unknown. The statistic of interest is then the minimal t statistic over all possible breakpoints in regressions similar to those proposed by Perron (1990a). Other related statistics are also discussed. We derive and tabulate the asymptotic distributions of interest. Most of the emphasis, however, is given to the tabulation of finite-sample critical values using simulation experiments. Particular attention is given to the effect, on the finite-sample critical values, of various procedures to select the appropriate order of the estimated autoregressions. We apply the tests to analyze the issue of purchasing power parity between the United States and the United Kingdom and also between the United States and Finland, whose real exchange rates are characterized by apparent shifts in level when using particular price indexes.     

Correction to: Coping with climatic shocks: local perspectives from Haiti’s rural mountain regions

Population and Environment - The article Coping with climatic shocks: local perspectives from Haiti’s rural mountain regions, written by Caroline Staub, Anne Gilot, Molene Pierre, Gerald...     

Post hoc false positive control for structured hypotheses

In a high-dimensional multiple testing framework, we present new confidence bounds on the false positives contained in subsets S of selected null hypotheses. These bounds are post hoc in the sense that the coverage probability holds simultaneously over all S, possibly chosen depending on the data. This article focuses on the common case of structured null hypotheses, for example, along a tree, a hierarchy, or geometrically (spatially or temporally). Following recent advances in post hoc inference, we build confidence bounds for some prespecified forest-structured subsets and deduce a bound for any subset S by interpolation. The proposed bounds are shown to improve substantially previous ones when the signal is locally structured. Our findings are supported both by theoretical results and numerical experiments. Moreover, our bounds can be obtained by an algorithm (with complexity bilinear in the sizes of the reference hierarchy and of the selected subset) that is implemented in the open-source R package sansSouci available from https://github.com/pneuvial/sanssouci , making our approach operational.     

Bayesian Causality

Abstract

Although no universally accepted definition of causality exists, in practice one is often faced with the question of statistically assessing causal relationships in different settings. We present a uniform general approach to causality problems derived from the axiomatic foundations of the Bayesian statistical framework. In this approach, causality statements are viewed as hypotheses, or models, about the world and the fundamental object to be computed is the posterior distribution of the causal hypotheses, given the data and the background knowledge. Computation of the posterior, illustrated here in simple examples, may involve complex probabilistic modeling but this is no different than in any other Bayesian modeling situation. The main advantage of the approach is its connection to the axiomatic foundations of the Bayesian framework, and the general uniformity with which it can be applied to a variety of causality settings, ranging from specific to general cases, or from causes of effects to effects of causes.     

SEPARATING MORAL HAZARD FROM ADVERSE SELECTION AND LEARNING IN AUTOMOBILE INSURANCE: LONGITUDINAL EVIDENCE FROM FRANCE

The identification of information problems in different markets is a challenging issue in the economic literature. In this paper, we study the identification of moral hazard from adverse selection and learning about risk within the context of a multi‐period dynamic model. We extend the model of Abbring, Chiappori, and Pinquet (2003, Journal of the European Economic Association, 1, 767–820) to include learning about risk and insurance coverage choice over time. We derive testable empirical implications for panel data. We then perform tests using longitudinal data from France during the period 1995–1997. We find evidence of moral hazard among a sub‐group of policyholders with less driving experience (less than 15 years). Policyholders with fewer than five years of experience have a combination of learning about risk and moral hazard, whereas no residual information problem is found for policyholders with more than 15 years of experience.