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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...
Persistent link: https://www.econbiz.de/10014620899
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...
Persistent link: https://www.econbiz.de/10005246256
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...
Persistent link: https://www.econbiz.de/10004966106