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...

We use a nonlinear, nonparametric method to forecast unemployment rates. This method is an extension of the nearest-neighbor method but uses a higher-dimensional simplex approach. We compare these forecasts with several linear and nonlinear parametric methods based on the work of Montgomery et...

Empirical evidence from agricultural labor markets is consistent with efficiency-wage theory and inconsistent with several alternative explanations. According to this theory, the higher wage or deferred payment (benefits) that direct-hire growers pay relative to that of farm labor contractors is...

Urban areas tend to have much more productive labor and higher salaries than rural areas, and there are vast differences across urban areas. Areas with high salaries and high productivity tend to have employers that invest in much more research and development than areas with low salaries and...

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 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.