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...

We develop an information-theoretic framework for economic modeling. This framework is based on principles of entropic inference that are designed for reasoning on the basis of incomplete information. We take the point of view of an external observer who has access to limited information about...

Given aggregated data, a framework for estimating the entries of a social accounting matrix (SAM), or any large matrix of expenditures, trade or income flows, is developed. Under this framework it is possible to evaluate the contribution of structural and supply-side information, as well as...

We develop a new way to incorporate prior information within an Information-Theoretic (IT) estimation framework. The estimator considers many potential priors and uses a simple statistic to choose the optimal solution. Our method outperforms its competitors for all finite data.