A Memoir on Model Selection Criterion between Two Nested and Non-Nested Stochastic Linear Regression Models

Model selection and parameter estimation in non-linear nested models: a sequential generalized DKL-optimum design

This work proposes a sequential procedure which is useful to select the best model among several nested non-linear models and to estimate efficiently the parameters of the chosen model. At the first step of this procedure, a generalized DKL-optimum design is computed, which is optimal for the double goal of model selection and parameter estimation. Subsequently, at each following step, an adapt...

Non-nested model selection in unstable environments

We consider non-nested model selection tests in the presence of possible data and parameter instabilities. The novelty of our approach is that we allow the models’ relative performance to be varying over time, whereas existing model selection techniques look for an overall best model. We argue that the time path of the models’ relative performance may contain useful information that is lost whe...

A comparison of sequential and non-sequential designs for discrimination between nested regression models

An Encompassing Test for Non-Nested Quantile Regression Models

We propose an encompassing test for non-nested linear quantile regression models and show that it has an asymptotic χ2 distribution. It is also shown that the proposed test is a regression rank score test in a comprehensive model under conditional homogeneity. Our simulation results indicate that the proposed test performs very well in finite samples. JEL classification: C12, C52

on the effect of linear & non-linear texts on students comprehension and recalling

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