Robust estimation of the SUR model

This paper proposes robust regression to solve the problem of outliers in seemingly unrelated regression (SUR) models. The authors present an adaptation of S‐estimators to SUR models. S‐estimators are robust, have a high breakdown point and are much more efficient than other robust regression estimators commonly used in practice. Furthermore, modifications to Ruppert's algorithm allow a fast evaluation of them in this context. The classical example of U.S. corporations is revisited, and it appears that the procedure gives an interesting insight into the problem.     

Pensions with heterogenous individuals and endogenous fertility

We study the design of pension schemes when fertility is endogenous and parents differ in ability to raise children. Pay-as-you-go schemes require, under perfect information, a marginal subsidy on fertility to correct for the externality they create, equal pensions, and contributions that increase or decrease with the number of children. Under asymmetric information, incentive-related distortions supplement the Pigouvian subsidy. These require an additional subsidy or an offsetting tax depending on whether the redistribution is towards people with more or with less children. In the former case, pensions are decreasing in the number of children; otherwise, they are increasing.     

A General Approach To Nonparametric Histogram Estimation

We note that some classical functional estimation problems may be reduced to a general unique framework and study an estimator within this general framework that reduces to the classical histogram type estimators in various examples presented. The convergence in probability and the almost complete convergence of this general estimator are studied obtaining convergence conditions which reduce to the classical conditions in each case. Finally, this general framework provides conditions for the convergence of the finite dimensional distributions of the associated empirical process.     

Parameter estimation of complex mixed models based on meta-model approach

Complex biological processes are usually experimented along time among a collection of individuals, longitudinal data are then available. The statistical challenge is to better understand the underlying biological mechanisms. A standard statistical approach is mixed-effects model where the regression function is highly-developed to describe precisely the biological processes (solutions of multi-dimensional ordinary differential equations or of partial differential equation). A classical estimation method relies on coupling a stochastic version of the EM algorithm with a Monte Carlo Markov Chain algorithm. This algorithm requires many evaluations of the regression function. This is clearly prohibitive when the solution is numerically approximated with a time-consuming solver. In this paper a meta-model relying on a Gaussian process emulator is proposed to approximate the regression function, that leads to what is called a mixed meta-model. The uncertainty of the meta-model approximation can be incorporated in the model. A control on the distance between the maximum likelihood estimates of the mixed meta-model and the maximum likelihood estimates of the exact mixed model is guaranteed. Eventually, numerical simulations are performed to illustrate the efficiency of this approach.     

Race differentials in employment effects of psychological distress: A study of non-Hispanic Whites and African-Americans in the United States

This study used two sub-samples of African-Americans and non-Hispanic Whites from the 2002–2003 U.S. National Survey on Drug Use and Health to examine differential effects of psychological distress (PD) on employment. Failing to reject exogeneity of PD in the employment specifications, we estimated standard probit of employment. We found that PD significantly reduced employment probability regardless of race; but the reduction was 7.4% for African-Americans, compared to 5.3% for Whites. Using individuals with PD only, we estimated the Oaxaca–Blinder decomposition and found endowments explained 61% of employment differences between Whites with PDs and African-Americans with PDs while 39% of these differences were due to unexplained factors. These findings suggest that targeted policies for prevention and effective treatment of PD might yield higher employment benefits for minorities.     

Single female‐headed households,handgun possession,and the fear of rape

The influence of fear of rape, perceived risk of rape, and prior victimization on the presence of handguns in single female‐headed households is examined. On the basis of previous research, a causal model that includes a number of control factors is developed and tested. Prior criminal victimization of the household was the only factor that was a significant predictor of the presence of a handgun. Interestingly, fear of rape had a negative, although not statistically significant, association with handgun possession, indicating that the presence of the weapon may reduce fear. Further implications are also discussed. Data were collected using a mail survey of Louisiana residents.     

Local linear regression with adaptive orthogonal fitting for the wind power application

Short-term forecasting of wind generation requires a model of the function for the conversion of meteorological variables (mainly wind speed) to power production. Such a power curve is nonlinear and bounded, in addition to being nonstationary. Local linear regression is an appealing nonparametric approach for power curve estimation, for which the model coefficients can be tracked with recursive Least Squares (LS) methods. This may lead to an inaccurate estimate of the true power curve, owing to the assumption that a noise component is present on the response variable axis only. Therefore, this assumption is relaxed here, by describing a local linear regression with orthogonal fit. Local linear coefficients are defined as those which minimize a weighted Total Least Squares (TLS) criterion. An adaptive estimation method is introduced in order to accommodate nonstationarity. This has the additional benefit of lowering the computational costs of updating local coefficients every time new observations become available. The estimation method is based on tracking the left-most eigenvector of the augmented covariance matrix. A robustification of the estimation method is also proposed. Simulations on semi-artificial datasets (for which the true power curve is available) underline the properties of the proposed regression and related estimation methods. An important result is the significantly higher ability of local polynomial regression with orthogonal fit to accurately approximate the target regression, even though it may hardly be visible when calculating error criteria against corrupted data.     

The Equity and Poverty Impacts of Aquaculture: Insights from the Philippines

Recent literature is sceptical about the ability of aquaculture development to enhance equity and reduce poverty. This article investigates the issue empirically by surveying 148 households randomly selected in five coastal communities of the Philippines. There is overwhelming evidence that aquaculture benefits the poor in important ways and that it is perceived very positively by poor and non‐poor alike. In particular, the poor derive a relatively larger share of their income from it than the rich, and a lowering of the poverty line only reinforces this result. A Gini decomposition exercise also shows unambiguously that aquaculture represents an inequality‐reducing source of income, providing employment to a large number of unskilled workers in communities characterised by large labour surpluses.