Modeling Sequential Information Acquisition Behavior in Rational Decision Making

Most real‐life decisions are made with less than perfect information and there is often some opportunity to acquire additional information to increase the quality of the decision. In this article, we define and study the sequential information acquisition process of a rational decision maker (DM) when allowed to acquire any finite amount of information from a set of products defined by vectors of characteristics. The information acquisition process of the DM depends both on the values of the characteristics observed previously and the number and potential realizations of the remaining characteristics. Each time an observation is acquired, the DM modifies the probability of improving upon the products already observed with the number of observations available. We construct two real‐valued functions whose crossing points determine the decision of how to allocate each available piece of information. We provide several numerical simulations to illustrate the information acquisition incentives defining the behavior of the DM. Applications to knowledge management and decision support systems follow immediately from our results, particularly when considering the introduction and acceptance of new technological products and when formalizing online search environments.     

On beta regression residuals

We propose two new residuals for the class of beta regression models, and numerically evaluate their behaviour relative to the residuals proposed by Ferrari and Cribari-Neto. Monte Carlo simulation results and empirical applications using real and simulated data are provided. The results favour one of the residuals we propose.     

Professionalizing youth development: the role of higher education

Certificates, credentials, and degrees offered through institutions of higher education allow youth development professionals to acquire educational experiences that are recognized in a formal manner.     

Housing Valuation,Wealth Perception,and Homeowners’ Portfolio Composition

This study explores the link between wealth perception from an appreciation of the residential property price and homeowners’ preference toward asset categories pooled by risk. Household survey data for Portugal were used to build shares of low, medium, and high risk assets representing fractions of household’s total wealth. Data showed incomplete household portfolios along with housing capturing the largest share of households’ wealth, in line with the literature on composition puzzles. The findings indicate robust empirical evidence that the rate of housing valuation is an indicator of households’ wealth perception. When the housing price appreciates with respect to its initial cost, households suffer an endowment effect and tend to increase diversity in their portfolio, expanding the holdings of safe deposits as they raise the share of high risk assets.     

Challenging the neoliberal view of education: the Center for Ecoliteracy as a transformative educational practice

The neoliberal rationality of recent decades has, as one of its maxims, that of extending market logic to the rest of social dimensions on the basis of competition. However, the spreading of this neoliberal ‘drug’ requires profound political reforms. It calls for a cultural engineering of the public sector which has been fostered by the New Public Management (NPM). This form of governmentality controls current power relations and gives a new meaning to the management of public services, including the educational sector. In this paper, we maintain that there are ‘spaces of resistance’ against the neoliberal educational rationality. We analyse the Center for Ecoliteracy (CFE) as a transformative experience which integrates a holistic perspective into educational practice. We compare the principles of the NPM with those of the CFE, showing that there are more humanistic approaches to education which aim to teach students the need to live in sustainable communities.     

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)