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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.
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]
In statistics, a generalized estimating equation (GEE) is used to estimate the parameters of a generalized linear model with a possible unmeasured correlation between observations from different timepoints. [1] [2]
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With several coauthors such as Kenneth J. Singleton, Scott F. Richard, and Robert Hodrick, Hansen applied GMM to study models of asset valuation. Together with Ravi Jagannathan he showed that the ratio of any stochastic discount factor 's standard deviation to its mean is at least as great as any asset's Sharpe ratio ; this result is known as ...
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
A typical finite-dimensional mixture model is a hierarchical model consisting of the following components: . N random variables that are observed, each distributed according to a mixture of K components, with the components belonging to the same parametric family of distributions (e.g., all normal, all Zipfian, etc.) but with different parameters