Non-nested hypothesis testing inference for GAMLSS models

Two or more regression models are said to be non-nested if neither can be obtained from the remaining models when parametric restrictions are imposed. Tests for choosing between linear non-nested regression models are found in literature, such as J and MJ tests. In this paper we propose variants of these two tests for the GAMLSS (Generalized Additive Models for Location, Scale and Shape) class of models. We report Monte Carlo evidence on finite sample behaviour of the proposed tests. Bootstrap-based testing inference is also considered. Overall, bootstrap MJ test had the best performance. An empirical application is presented and discussed.     

Simulation-based model comparison methodology with application to road accident models

This article assumes the goal of proposing a simulation-based theoretical model comparison methodology with application to two time series road accident models. The model comparison exercise helps to quantify the main differences and similarities between the two models and comprises of three main stages: (1) simulation of time series through a true model with predefined properties; (2) estimation of the alternative model using the simulated data; (3) sensitivity analysis to quantify the effect of changes in the true model parameters on alternative model parameter estimates through analysis of variance, ANOVA. The proposed methodology is applied to two time series road accident models: UCM (unobserved components model) and DRAG (Demand for Road Use, Accidents and their Severity). Assuming that the real data-generating process is the UCM, new datasets approximating the road accident data are generated, and DRAG models are estimated using the simulated data. Since these two methodologies are usually assumed to be equivalent, in a sense that both models accurately capture the true effects of the regressors, we are specifically addressing the modeling of the stochastic trend, through the alternative model. Stochastic trend is the time-varying component and is one of the crucial factors in time series road accident data. Theoretically, it can be easily modeled through UCM, given its modeling properties. However, properly capturing the effect of a non-stationary component such as stochastic trend in a stationary explanatory model such as DRAG is challenging. After obtaining the parameter estimates of the alternative model (DRAG), the estimates of both true and alternative models are compared and the differences are quantified through experimental design and ANOVA techniques. It is observed that the effects of the explanatory variables used in the UCM simulation are only partially captured by the respective DRAG coefficients. This a priori, could be due to multicollinearity but the results of both simulation of UCM data and estimating of DRAG models reveal that there is no significant static correlation among regressors. Moreover, in fact, using ANOVA, it is determined that this regression coefficient estimation bias is caused by the presence of the stochastic trend present in the simulated data. Thus, the results of the methodological development suggest that the stochastic component present in the data should be treated accordingly through a preliminary, exploratory data analysis.     

Likelihood-based inference for the transmuted log-logistic model in the presence of right-censored data

Although new transmuted distributions have been widely proposed in the last few years, it is rarely to find those class of models applied in survival analysis in the presence of censored lifetimes. In this context, we are concerned in applying the transmuted log-logistic model in the study of discharge times of patients treated with the Linezolid drug. For model fitting, we use and compare different profile likelihood methods. Analysis of residuals and influence are also presented. All results are validated in a simulation study by considering the bootstrap techniques.     

EASING THE CONSTRAINTS OF MOTHERHOOD: THE EFFECTS OF ALL‐DAY SCHOOLS ON MOTHERS' LABOR SUPPLY

Low rates of female labor force participation (LFP) have been linked to the absence of childcare policies. This article examines the degree to which extending the school day by 3.5 hours in elementary schools, a large implicit childcare subsidy, affects LFP, the number of weekly hours worked, and the monthly earnings of females with elementary‐school‐age children. To do so, we exploit within‐individual variation in access to full‐time schools and a rotating panel of households that contains 12 years of individual‐level data on labor outcomes and sociodemographic characteristics. Results from long‐difference models show that extending the school day increases mothers' labor supply, increasing LFP by 5.5 percentage points and the number of weekly hours worked by 1.8. Moreover, these increases are accompanied by a raise in monthly earnings. (JEL I25, J13, J22)     

Urban risks due to climate change in the Andean municipality of Pasto,Colombia: A Bayesian network approach

The current trends of climate change will increase people's exposure to urban risks related to events such as landslides, floods, forest fires, food production, health, and water availability, which are stochastic and very localized in nature. This research uses a Bayesian network (BN) approach to analyze the intensity of such urban risks for the Andean municipality of Pasto, Colombia, under climate change scenarios. The stochastic BN model is linked to correlational models and local scenarios of representative concentration trajectories (RCP) to project the possible risks to which the municipality of Pasto will be exposed in the future. The results show significant risks in crop yields, food security, water availability and disaster risks, but no significant risks on the incidence of acute diarrheal diseases (ADD) and acute respiratory infections (ARI), whereas positive outcomes are likely to occur in livestock production, influenced by population growth. The advantage of the BN approach is the possibility of updating beliefs in the probabilities of occurrence of events, especially in developing, intermediate cities with information-limited contexts.     

Generalized log-gamma regression models with cure fraction

In this paper, the generalized log-gamma regression model is modified to allow the possibility that long-term survivors may be present in the data. This modification leads to a generalized log-gamma regression model with a cure rate, encompassing, as special cases, the log-exponential, log-Weibull and log-normal regression models with a cure rate typically used to model such data. The models attempt to simultaneously estimate the effects of explanatory variables on the timing acceleration/deceleration of a given event and the surviving fraction, that is, the proportion of the population for which the event never occurs. The normal curvatures of local influence are derived under some usual perturbation schemes and two martingale-type residuals are proposed to assess departures from the generalized log-gamma error assumption as well as to detect outlying observations. Finally, a data set from the medical area is analyzed.     

Women and poverty: A gender-sensitive approach

Poverty is not a gender neutral condition as the number of poor women exceeds that concerning men and women and men experience poverty in distinctive ways. This paper discusses the relevance of incorporating a gender perspective in poverty studies and provides a portrait of poverty among women in Portugal. Following a multidimensional concept of poverty, the methodology used comprises a cross-sectional and a dynamic analysis of poverty, using data from ECHP (1995–2001). The results point to the importance of various factors contributing to women's vulnerability to poverty, such as issues related to labor market, lone motherhood, ageing and education.