Keynesian model and develop an estimation methodology for the canonical Smets and Wouters (2007) model. A horse race between … account in the estimation. As a policy application, we show that optimal Taylor rules under AR(1) expectations inherit history …

prediction, estimation and control. Generalized Linear Models and Partial Likelihood are the basic tools in the present study … rainfall data. Subsequently we discuss a new recursive estimation method for time series following generalized linear models …, motivated by the logistic regression model in conjunction with binary time series. The estimation procedure, suitably modified …

estimation error and a communication cost. In our formulation, the transmission of a real number from the pre-processor to the …

This study looked at what components are in student solutions to computational estimation problems involving fractions …. Past computational estimation research has focused on strategies used for estimating with whole numbers and decimals while … with fractions (Hanson & Hogan, 2000) that researched adult estimation strategies and not children’s strategies. Given the …

Parameter estimation for large systems is a difficult problem, and the solution approaches are computationally … uncertainties on the system response. This article compares two new computational approaches for parameter estimation based on the … polynomial chaos theory for parameter estimation: a Bayesian approach, and an approach using an extended Kalman filter (EKF) to …

cases of this method exist in the literature including U and generalized L-statistics. This estimation technique is based on …