Weighted Derivative Estimation of Quantal Response Models : Simulations and Applications to Choice of Truck Freight Carrier

Under the assumption of a single-index model the weighted average density derivative (WAD) estimator, estimates regression parameters up to scale. The small sample performance of ratio estimators are studied. For spherical errors in a latent variable specification the WAD estimator, in terms of bias and mean square error (MSE), demonstrates performance similar to the logit maximum likelihood estimator. Under heteroskedastic errors the WAD estimator performs better. In an empirical application concerning choices of freight transports we find that the WAD estimators evidences improved performance in one of the two sectors studied compared with standard parametric models