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In econometrics and statistics, the generalized method of moments (GMM) is a generic method for estimating parameters in statistical models.Usually it is applied in the context of semiparametric models, where the parameter of interest is finite-dimensional, whereas the full shape of the data's distribution function may not be known, and therefore maximum likelihood estimation is not applicable.
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.
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.
Increasing or decreasing one results in changes to one or both of the other two. For example, a policy that increases access to health services would lower quality of health care and/or increase cost. The desired state of the triangle, high access and quality with low cost represents value in a health care system. [3]
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]
GMM may refer to: Generalized method of moments, an econometric method; GMM Grammy, a Thai entertainment company; Gaussian mixture model, a statistical probabilistic model; Google Map Maker, a public cartography project; GMM, IATA code for Gamboma Airport in the Republic of the Congo
Health policy and management is the field relating to leadership, management, and administration of public health systems, health care systems, hospitals, ...
Lars Peter Hansen re-worked through the derivations and showed that it can be extended to general non-linear GMM in a time series context. [3] The Sargan test is based on the assumption that model parameters are identified via a priori restrictions on the coefficients, and tests the validity of over-identifying restrictions.