We develop a generalized maximum entropy estimator that can estimate pure and mixed strategies subject to restrictions from game theory. This method avoids distributional assumptions and is consistent and efficient. We demonstrate this method by estimating the mixed strategies of duopolistic...

Given the objective of estimating the unknown parameters of a possibly nonlinear dynamic model using a finite (and relatively small) data set, it is common to use a Kalman filter Maximum Likelihood (ML) approach, ML-type estimators or more recently a GMM (Imbens, Spady and Johnson, 1998), BMOM...

A new information-based approach for estimating systems of many equations with nonnegativity constraints is presented. This approach, called generalized maximum entropy (GME), is more practical and efficient than traditional maximum-likelihood methods. The GME method is used to estimate an...