Search results for " Quantile Regression"
showing 10 items of 26 documents
Productivity analysis of Latvian companies using ORBIS database
2021
International audience; This research study uses ORBIS microdata at the company level to analyse productivity of 167 thousand economically active Latvian companies over 2011-2018. The aim of the study is twofold-to find factors consistently associated with productivity at the company level; and to recommend possible criteria for companies to receive a state support (from the view of enhancing aggregate productivity in the long term). Our research results show that productivity of Latvian companies is positively related to their size, age, as well as location closer to Riga and other big cities. However, there is a substantial within-group variation in productivity between companies. Multiva…
NEIGHBORHOOD EFFECTS IN SPATIAL HOUSING VALUE MODELS. THE CASE OF THE METROPOLITAN AREA OF PARIS (1999)
2009
In hedonic housing models, the spatial dimension of housing values are traditionally processed by the impact of neighborhood variables and accessibility variables. In this paper we show that spatial effects might remain once neighborhood effects and accessibility have been controlled for. We notably stress on three sides of neighborhood effects: social capital, social status and social externalities and consider the accessibility to the primary economic center as describing the urban spatial trend. Using spatial econometrics specifications of the hedonic equation, we estimate whether spatial effects impact the housing values. Our empirical case concerns the Metropolitan Area (MA) of Paris i…
Quantile Regression Coefficients Modeling: a Penalized Approach
2018
Modeling quantile regression coefficients functions permits describing the coefficients of a quantile regression model as parametric functions of the order of the quantile. This approach has numerous advantages over standard quantile regression, in which different quantiles are estimated one at the time: it facilitates estimation and inference, improves the interpretation of the results, and is statistically efficient. On the other hand, it poses new challenges in terms of model selection. We describe a penalized approach that can be used to identify a parsimonious model that can fit the data well. We describe the method, and analyze the dataset that motivated the present paper. The propose…
Non-crossing parametric quantile functions: an application to extreme temperatures
2019
Quantile regression can be used to obtain a non-parametric estimate of a conditional quantile function. The presence of quantile crossing, however, leads to an invalid distribution of the response and makes it difficult to use the fitted model for prediction. In this work, we show that crossing can be alleviated by modelling the quantile function parametrically. We then describe an algorithm for constrained optimisation that can be used to estimate parametric quantile functions with the noncrossing property. We investigate climate change by modelling the long-term trends of extreme temperatures in the Arctic Circle.
A new indicator for higher education student performance
2014
The debate on academic achievement is a heated issue that involves all the higher education contexts. This paper attempts to provide an indicator that can make the measurement of university student performance easier and that can be easily applied to different systems, making comparisons more fair. The Italian University System is used as a starting point to make several considerations on the current measures and to build up a new performance indicator. Then, a generalization for other marking systems is shown and finally a quantile regression is performed to investigate some determinants of the new performance indicator, also with respect to the current one.
Do personal characteristics affect the Rasch measures of perceived physical risk? A quantile regression approach
2012
This paper focuses on the measurement of the perception of healthy risk according to personal characteristics. The Physical Risk Assessment Inventory was adopted as measurement tool and it was administered to 551 students enrolled in the first and in the fifth classes of some high schools of Palermo. The analysis of the determinants of the perceived risk is based on its quantitative measures. Therefore the analysis has been developed into two tracks. First track is devoted to obtain a quantitative measure of the perceived risk: an Extended Logistic Rasch Model was used considering separately males and females. Results highlight the different perception of risk between males and females, alt…
A penalized approach to covariate selection through quantile regression coefficient models
2019
The coefficients of a quantile regression model are one-to-one functions of the order of the quantile. In standard quantile regression (QR), different quantiles are estimated one at a time. Another possibility is to model the coefficient functions parametrically, an approach that is referred to as quantile regression coefficients modeling (QRCM). Compared with standard QR, the QRCM approach facilitates estimation, inference and interpretation of the results, and generates more efficient estimators. We designed a penalized method that can address the selection of covariates in this particular modelling framework. Unlike standard penalized quantile regression estimators, in which model selec…
Parametric estimation of non-crossing quantile functions
2021
Quantile regression (QR) has gained popularity during the last decades, and is now considered a standard method by applied statisticians and practitioners in various fields. In this work, we applied QR to investigate climate change by analysing historical temperatures in the Arctic Circle. This approach proved very flexible and allowed to investigate the tails of the distribution, that correspond to extreme events. The presence of quantile crossing, however, prevented using the fitted model for prediction and extrapolation. In search of a possible solution, we first considered a different version of QR, in which the QR coefficients were described by parametric functions. This alleviated th…
Multiple smoothing parameters selection in additive regression quantiles
2021
We propose an iterative algorithm to select the smoothing parameters in additive quantile regression, wherein the functional forms of the covariate effects are unspecified and expressed via B-spline bases with difference penalties on the spline coefficients. The proposed algorithm relies on viewing the penalized coefficients as random effects from the symmetric Laplace distribution, and it turns out to be very efficient and particularly attractive with multiple smooth terms. Through simulations we compare our proposal with some alternative approaches, including the traditional ones based on minimization of the Schwarz Information Criterion. A real-data analysis is presented to illustrate t…
Nonlinear parametric quantile models
2020
Quantile regression is widely used to estimate conditional quantiles of an outcome variable of interest given covariates. This method can estimate one quantile at a time without imposing any constraints on the quantile process other than the linear combination of covariates and parameters specified by the regression model. While this is a flexible modeling tool, it generally yields erratic estimates of conditional quantiles and regression coefficients. Recently, parametric models for the regression coefficients have been proposed that can help balance bias and sampling variability. So far, however, only models that are linear in the parameters and covariates have been explored. This paper …