Link: https://en.m.wikipedia.org/wiki/Generalized_method_of_moments
Description: Web ResultIn 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 …
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Link: https://blog.stata.com/2015/12/03/understanding-the-generalized-method-of-moments-gmm-a-simple-example/
Description: Web ResultDec 3, 2015 · GMM generalizes the method of moments (MM) by allowing the number of moment conditions to be greater than the number of parameters. Using these extra moment conditions makes GMM more efficient than MM. When there are more moment conditions than parameters, the estimator is said to be overidentified.
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Link: https://faculty.washington.edu/ezivot/econ583/gmm.pdf
Description: Web ResultGeneralized Method of Moments 1.1 Introduction This chapter describes generalized method of moments (GMM) estima-tion for linear and non-linear models with applications in economics and finance. GMM estimation was formalized by Hansen (1982), and since has become one of the most widely used methods of estimation for models in economics and ...
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Link: https://www.hbs.edu/research-computing-services/Shared%20Documents/Training/gmm.pdf
Description: Web ResultGeneralized Method of Moments. Xiang Ao. June 15, 2009. 1 The Method of Moments. The method of moments (MOM) is merely the following proposal: Proposition 1.1 (MOM) To estimate a population moment (or a function of population moments) merely use the corresponding sample moment (or a …
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Link: https://link.springer.com/chapter/10.1007/978-3-319-53469-5_15
Description: Web ResultAug 30, 2017 · Abstract. The generalized method of moments (GMM) is a conceptually simple and flexible estimation method that has come to play an increasingly prominent role in empirical research in economics over the last 30 years. Application of GMM requires the availability of so-called moment equations or moment conditions.
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Link: https://ocw.mit.edu/courses/14-385-nonlinear-econometric-analysis-fall-2007/2d9ba545c27ee6942947ed6911e333ee_notes_gmm.pdf
Description: Web ResultThe acronym GMM is an abreviation for ”generalized method of moments,” refering to GMM being a generalization of the classical method moments. The method of moments is based on knowing the form of up to p moments of a variable y as functions of the parameters, i.e. on E[yj] = hj(β0), (1 ≤ j ≤ p).
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Link: https://link.springer.com/chapter/10.1007/978-981-10-4856-2_7
Description: Web ResultJun 16, 2017 · Generalized method of moments (Hansen 1982) is one of the most popular methods in econometric literature. Due to this ground-break work, Hansen was awarded Nobel prize in 2013. The generalized method of moments (henceforth GMM) has become an important unifying framework for inference in econometrics during the last thirty years.
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Link: https://eml.berkeley.edu/%7Emcfadden/e240b_f01/ch3.pdf
Description: Web ResultThe E g(z,θ) are generalized moments, and the analogy principle suggests that an estimator of θo can be obtained by solving for θ that makes the sample analogs of the population moments small. Assume that linear dependancies among the moments are eliminated, so that g(z,θo) has a …
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Link: https://link.springer.com/chapter/10.1007/978-0-387-32348-0_21
Description: Web ResultThis chapter describes generalized method of moments (GMM) estimation for linear and nonlinear models with applications in economics and finance. The GMM estimation was formalized by Hansen (1982), and since has become one of the most widely used methods of estimation for models in economics and finance.
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Link: https://home.uchicago.edu/~lhansen/time_series_perspective.pdf
Description: Web ResultGeneralized Method of Moments Estimation: A Time Series Perspective. Lars Peter Hansen (University of Chicago, Chicago, Illinois, USA) ∗. February 20, 2001. Abstract. This entry describes empirical methods for estimating dynamic economic systems using time-series data.
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