Linear Quantile Regression and Endogeneity Correction

Factors. Three factors are explicit in our benchmark system: the number of training points, the problem dimension and the quantile level. The other factors depend on the

Once the models are trained and applied to the testing set, we analyze the quality of the probabilistic forecasts using several qualitative and quantitative tools: reliability

The results of Fisher tests of equality of coe¢ cients across quantiles (based on the above bootstrap estimation of the variance-covariance matrix of the whole quantile process)

Finally, instead of imposing Assumption 2’, one may …rst test for which coe¢ cients the hypothesis of constant e¤ect is rejected or not in typical quantile regression estimation, so

2.1 True values, estimated (posterior) mean, estimated (posterior) median and 95% con- fidence intervals of the model parameters, based on the different types of the error

A hierarchical Bayesian model is used to shrink the fixed and random effects toward the common population values by introducing an l 1 penalty in the mixed quantile regression

By taking account of model uncertainty in quantile regression, there is the possibility of choosing a single model as the “best” model, for example by taking the model with the

Table 3 provides estimates of the model parameters for the median case (τ = 0.5) as well as a comparison with the results obtained by three different estimation approaches, namely