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A general assumption of GMM is that the data Y t be generated by a weakly stationary ergodic stochastic process. (The case of independent and identically distributed (iid) variables Y t is a special case of this condition.) In order to apply GMM, we need to have "moment conditions", that is, we need to know a vector-valued function g(Y,θ) such ...
In econometrics, the Arellano–Bond estimator is a generalized method of moments estimator used to estimate dynamic models of panel data.It was proposed in 1991 by Manuel Arellano and Stephen Bond, [1] based on the earlier work by Alok Bhargava and John Denis Sargan in 1983, for addressing certain endogeneity problems. [2]
The EM algorithm consists of two steps: the E-step and the M-step. Firstly, the model parameters and the () can be randomly initialized. In the E-step, the algorithm tries to guess the value of () based on the parameters, while in the M-step, the algorithm updates the value of the model parameters based on the guess of () of the E-step.
Hansen is best known as the developer of the econometric technique generalized method of moments (GMM) and has written and co-authored papers applying GMM to analyze economic models in numerous fields including labor economics, international finance, finance and macroeconomics. This method has been widely adopted in economics and other fields ...
The generalized estimating equation is a special case of the generalized method of moments (GMM). [9] ... additional terms may apply. By using this site, ...
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To estimate parameters of a conditional moment model, the statistician can derive an expectation function (defining "moment conditions") and use the generalized method of moments (GMM). However, there are infinitely many moment conditions that can be generated from a single model; optimal instruments provide the most efficient moment conditions.
The GMM IV estimator is ... Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply. By using this site, ...