Results 21 to 30 of about 7,158 (259)
Quantile Trend Regression and Its Application to Central England Temperature
Mathematics, 2022 The identification and estimation of trends in hydroclimatic time series remains an important task in applied climate research. The statistical challenge arises from the inherent nonlinearity, complex dependence structure, heterogeneity and resulting non-
Harry Haupt, Markus Fritsch
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Has the relationship between the real exchange rate and its fundamentals changed over time?
Baltic Journal of Economics, 2022 In this paper we contribute to the literature on determining the real exchange rate by using models that incorporate structural breaks and nonlinearities. We estimate cointegrated dynamic ordinary least squares regressions and quantile regressions.
Juan Carlos Cuestas +2 more
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Qualified Intergenerational Mobility: A Measurement Approach Using Quantile Regressions
Economia Aplicada, 2016 This article analyzes the intergenerational mobility according quali- fied equal opportunities presented by Anderson et al. (2009). We conduct two empirical tests with parents and children education data in Canada and Brazil. We estimate the relationship
Ana Claúdia Annegues, Erik Figueiredo
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Mathematical and Computational Applications, 2023 The usefulness of (probability) distributions in the field of biomedical science cannot be underestimated. Hence, several distributions have been used in this field to perform statistical analyses and make inferences. In this study, we develop the arctan
Suleman Nasiru +2 more
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Quantile Regression Estimates of Confidence Intervals for WASDE Price Forecasts
Journal of Agricultural and Resource Economics, 2010 This study uses quantile regressions to estimate historical forecast error distributions for WASDE forecasts of corn, soybean, and wheat prices, and then compute confidence limits for the forecasts based on the empirical distributions.
Olga Isengildina-Massa +2 more
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Local quantile regression [PDF]
Journal of Statistical Planning and Inference, 2013 Quantile regression is a technique to estimate conditional quantile curves. It provides a comprehensive picture of a response contingent on explanatory variables. In a flexible modeling framework, a specific form of the conditional quantile curve is not a priori fixed.
Wolfgang Karl Härdle +2 more
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Smoothing Quantile Regressions [PDF]
Journal of Business & Economic Statistics, 2019 We propose to smooth the entire objective function, rather than only the check function, in a linear quantile regression context. Not only does the resulting smoothed quantile regression estimator yield a lower mean squared error and a more accurate Bahadur-Kiefer representation than the standard estimator, but it is also asymptotically differentiable.
Marcelo Fernandes +2 more
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FIRM HETEROGENEITY AND EXPORT ACTIVITY OF EUROPEAN FIRMS: A QUANTILE ANALYSIS
Revista de Economía Mundial, 2018 This paper examines the extent to which firms’ characteristics are related to export activity behaviour. Using a dataset comprised of harmonized and detailed firm-level data from six European countries (Austria, France, Germany, Italy, Spain and United ...
Vicente Orts, Josep Martí
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Cogent Economics & Finance, 2020 This paper explores the symmetric and asymmetric dependency structure of decomposed return series of Gold and eight cryptocurrencies to establish the hedging and diversification potentials of these asset classes.
Peterson Owusu Junior +2 more
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CoRR, 2019 Quantile regression is a tool for learning conditional distributions. In this paper we study quantile regression in the setting where a protected attribute is unavailable when fitting the model. This can lead to "unfair'' quantile estimators for which the effective quantiles are very different for the subpopulations defined by the protected attribute ...
Dana Yang, John Lafferty, David Pollard
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