Abstract
Quantile regression estimates can be presented in tables alongside linear regression estimates. A possible advantage of this approach to presenting quantile regression results is that it is easy to compare the values of the coefficients and standard errors with OLS estimates and across quantiles. As we have seen, quantile estimates actually contain far more information than can be presented in simple tables. The estimates imply a full distribution of values for the dependent variable. It also is easy to show how changes in the explanatory variables affect the distribution of the dependent variable.
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McMillen, D.P. (2013). Linear and Nonparametric Quantile Regression. In: Quantile Regression for Spatial Data. SpringerBriefs in Regional Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31815-3_2
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DOI: https://doi.org/10.1007/978-3-642-31815-3_2
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31814-6
Online ISBN: 978-3-642-31815-3
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