A weighted generalized maximum entropy estimator with a data-driven weight
Summary: The method of Generalized Maximum Entropy (GME), proposed by \textit{A. Golan, G. Judge} and \textit{D. Miller} [Maximum entropy econometrics: robust estimation with limited data. (1996; Zbl 0884.62126)], is an information-theoretic approach that is robust to multicolinearity problem. It uses an objective function that is the sum of the entropies for coefficient distributions and disturbance distributions. This method can be generalized to the weighted GME (W-GME), where different weights are assigned to the two entropies in the objective function. We propose a data-driven method to select the weights in the entropy objective function. We use the least squares cross validation to derive the optimal weights. MonteCarlo simulations demonstrate that the proposedW-GME estimator is comparable to and often outperforms the conventional GME estimator, which places equal weights on the entropies of coefficient and disturbance distributions.
scientific article; zbMATH DE number 5664179
scientific article