Article

Handling heterogeneity among units in quantile regression. Investigating the impact of students’ features on University outcome

Authors:
To read the full-text of this research, you can request a copy directly from the authors.

Abstract

In many real data applications, statistical units belong to different groups and statistical models should be tailored to incorporate and exploit this heterogeneity among units. This paper proposes an innovative approach to identify group effects through a quantile regression model. The method assigns a conditional quantile to each group and provides a separate analysis of the dependence structure inside the groups. The relevance of the proposal is provided through an empirical analysis investigating the impact of students' features on University outcome. The analysis is performed on a sample of graduated students; the degree mark is the response variable, a set of variables describing the students' profile are used as regressors, and the attended School determines the group effects. A working example and a small simulation study are introduced to highlight the main features of the proposed approach.

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

... Building on the literature and the information gathered from the focus group analysis and to test the research hypotheses, we collected data through a questionnaire with 1,111 respondents representing Millennials and GenZ university students in the Northwest of Italy. Finally, following the methodology proposed recently by Davino and Vistocco (2018), we implemented a quantile regression analysis considering the presence of group effects (afraid or not afraid of . ...
... Apart from the fact that quantile regression allows us to consider the impact of the covariates on the entire distribution of y, an additional advantage is that it is more robust to non-normal errors and outliers, and invariant to monotonic transformations. In this study, we consider a quantile regression analysis considering the presence of group effects (afraid or not afraid of COVID-19) following the methodology proposed recently by Davino and Vistocco (2018). ...
... Davino and Vistocco (2018) proposed a methodology which uncovers the heterogeneity between different groups based on a single estimation process. The methodology includes first a global estimation, then recognition of the best model for each individual/unit, followed by recognition of the best model for each set and, finally, partial estimation (Davino and Vistocco, 2018). In particular, the first step, which includes the global estimation in the quantile regression model, is estimated by excluding the group variable (afraid or not COVID-19): ...
Article
The purpose of this paper is to investigate the relationship between mood and air travel choices, considering the role of travel significance and the influence that COVID-19 may have on younger generations’ choices. Using a mixed-methods sequential exploratory design, a sample of 1,111 Italian respondents, belonging to younger generations is investigated. The data are analysed using a quantile regression with group effects considering attitudes towards COVID-19. The study demonstrates that there is a positive and significant relationship between mood and the number of journeys by air to destinations outside Europe, highlighting the positive moderating effect of the air travel experience and the negative moderating effect of COVID-19. This may have important implications for air transport managers interested in luring younger people to fly in the post-COVID19 era.
Article
Full-text available
Composite-based path modeling aims to study the relationships among a set of constructs, that is a representation of theoretical concepts. Such constructs are operationalized as composites (i.e. linear combinations of observed or manifest variables). The traditional partial least squares approach to composite-based path modeling focuses on the conditional means of the response distributions, being based on ordinary least squares regressions. Several are the cases where limiting to the mean could not reveal interesting effects at other locations of the outcome variables. Among these: when response variables are highly skewed, distributions have heavy tails and the analysis is concerned also about the tail part, heteroscedastic variances of the errors is present, distributions are characterized by outliers and other extreme data. In such cases, the quantile approach to path modeling is a valuable tool to complement the traditional approach, analyzing the entire distribution of outcome variables. Previous research has already shown the benefits of Quantile Composite-based Path Modeling but the methodological properties of the method have never been investigated. This paper offers a complete description of Quantile Composite-based Path Modeling, illustrating in details the method, the algorithms, the partial optimization criteria along with the machinery for validating and assessing the models. The asymptotic properties of the method are investigated through a simulation study. Moreover, an application on chronic kidney disease in diabetic patients is used to provide guidelines for the interpretation of results and to show the potentialities of the method to detect heterogeneity in the variable relationships.
Article
Full-text available
Quantile composite-based path modeling is a recent extension to the conventional partial least squares path modeling. It estimates the effects that predictors exert on the whole conditional distributions of the outcomes involved in path models and provides a comprehensive view on the structure of the relationships among the variables. This method can also be used in a predictive way as it estimates model parameters for each quantile of interest and provides conditional quantile predictions for the manifest variables of the outcome blocks. Quantile composite-based path modeling is shown in action on real data concerning well-being indicators. Health outcomes are assessed taking into account the effects of Economic well-being and Education. In fact, to support an accurate evaluation of the regional performances, the conditions within the outcomes arise should be properly considered. Assessing health inequalities in this multidimensional perspective can highlight the unobserved heterogeneity and contribute to advances in knowledge about the dynamics producing the well-being outcomes at local level.
Article
Full-text available
The aim of the paper is to propose a quantile regression based strategy to assess heterogeneity in a multi-block type data structure. Specifically, the paper deals with a particular data structure where several blocks of variables are observed on the same units and a structure of relations is assumed between the different blocks. The idea is that quantile regression complements the results of the least squares regression by evaluating the impact of regressors on the entire distribution of the dependent variable, and not only exclusively on the expected value. By taking advantage of this, the proposed approach analyses the relationship among a dependent variable block and a set of regressors blocks but highlighting possible similarities among the statistical units. An empirical analysis is provided in the consumer analysis framework with the aim to cluster groups of consumers according to the similarities in the dependence structure among their overall liking and the liking for different drivers.
Preprint
The study applies the theory of planned behaviors to evaluate economic outcomes of Vietnamese firms in connection to their planned innovation and dynamic entrepreneurship. The analysis uses data from surveys on Vietnamese small and medium manufacturing firms conducted by UNU-WIDER during 2005- 2015. Fitted in various models including normal standard one-side regressions (fixed effect models, panel robust model, and pds-lasso) and two-side structural two stage models (extended regression model, treatment effect model, and IV-Lasso), the empirical analysis estimates the impacts of innovation activities on firm performance, featured in the role of dynamic entrepreneurship and planned innovation. The study uses profit margin to measure firm performance and the intention to innovate of firm owners as a proxy for dynamic entrepreneurship. The interaction terms between innovation activities and the intention to innovate capture planned innovation. The study shows that innovative firms are not financially performing better than their non-innovative counterparts. However, planned innovation is associated with better firm performance. This holds true for all three innovation activities including introduction of a new product, introduction of a new production process and improvements to existing products/processes. In light of the theory of planned behaviors, entrepreneurial intentions embedded in planned innovation can underlie a comprehensive plan and action that guide the innovation process. The study also shows that the impact of planned innovation towards firm performance is much more pronounced when complex innovations are carried out rather than more simple ones. The findings provide important implications for the introduction of support schemes that promote innovation among small and medium enterprises in Vietnam. Any innovation support schemes, introduced either by the public or private sector that target on small and medium enterprises, should be engaged with the group of dynamic entrepreneurs to warrant a higher chance of success
ResearchGate has not been able to resolve any references for this publication.