Generalized method of moments - 편집 역사

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2026-06-09T06:06:50Z

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← 이전 판		2026년 6월 9일 (화) 15:06 판
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	'''Generalized method of moments''' (GMM) is an estimation framework that generalizes the classical method of moments. A '''moment condition''' is an expectation that equals zero at the true parameter values. Under certain theoretical moment conditions, parameters can be estimated by forcing their sample analogues as close to zero as possible.~~<ref>Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators. ''Econometrica'' 50(4): 1029-1054.</ref>~~ While GMM yields consistent, asymptotically normal estimates, it is flexible as it does not rely on a specific probability distribution for the data, contrary to other estimation methods such as maximum likelihood.		'''Generalized method of moments''' (GMM) is an estimation framework that generalizes the classical method of moments. A '''moment condition''' is an expectation that equals zero at the true parameter values. Under certain theoretical moment conditions, parameters can be estimated by forcing their sample analogues as close to zero as possible. While GMM yields consistent, asymptotically normal estimates, it is flexible as it does not rely on a specific probability distribution for the data, contrary to other estimation methods such as maximum likelihood.

	== Relation to OLS and IV ==		== Relation to OLS and IV ==

	[[Ordinary least ~~square~~ (OLS)]] and [[instrumental variable (IV)]] estimators can be seen as special cases of GMM. In an OLS regression <math>y_i = \beta x_i + \epsilon_i</math>, the assumption <math>\mathbb{E}(x_i \epsilon_i) = 0</math> is a moment condition. The OLS estimator can be derived by setting the sample analogue:		[[Ordinary least squares (OLS)]] and [[instrumental variable (IV)]] estimators can be seen as special cases of GMM. In an OLS regression <math>y_i = \beta x_i + \epsilon_i</math>, the assumption <math>\mathbb{E}(x_i \epsilon_i) = 0</math> is a moment condition. The OLS estimator can be derived by setting the sample analogue:

	<div style="text-align: center;">		<div style="text-align: center;">
10번째 줄:		10번째 줄:

	== References ==		== References ==
	~~<references />~~
			* Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators. ''Econometrica'' 50(4): 1029–1054.

			[[분류:금융수학과 방법론]]

2025년 8월 28일 (목) 04:17에 Junhopark님의 편집

2025-08-28T04:17:08Z

← 이전 판		2025년 8월 28일 (목) 13:17 판
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	[[Ordinary least square (OLS)]] and [[instrumental variable (IV)]] estimators can be seen as special cases of GMM. In an OLS regression <math>y_i = \beta x_i + \epsilon_i</math>, the assumption <math>\mathbb{E}(x_i \epsilon_i) = 0</math> is a moment condition. The OLS estimator can be derived by setting the sample analogue:		[[Ordinary least square (OLS)]] and [[instrumental variable (IV)]] estimators can be seen as special cases of GMM. In an OLS regression <math>y_i = \beta x_i + \epsilon_i</math>, the assumption <math>\mathbb{E}(x_i \epsilon_i) = 0</math> is a moment condition. The OLS estimator can be derived by setting the sample analogue:

			<div style="text-align: center;">
	<math>\frac{1}{N}\sum_i x_i (y_i - \hat\beta x_i) = 0</math>		<math>\frac{1}{N}\sum_i x_i (y_i - \hat\beta x_i) = 0</math>
			</div>

	== References ==		== References ==
	<references />		<references />

2025년 8월 28일 (목) 04:15에 Junhopark님의 편집

2025-08-28T04:15:54Z

← 이전 판		2025년 8월 28일 (목) 13:15 판
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	== Relation to OLS and IV ==		== Relation to OLS and IV ==

	[[Ordinary least square (OLS)]] and [[instrumental variable (IV)]] estimators can be seen as special cases of GMM. In an OLS regression <math>y_i = \beta x_i + \epsilon_i</math>, the assumption <math>\mathbb{E}(x_i \epsilon_i) = 0</math> is a moment condition.		[[Ordinary least square (OLS)]] and [[instrumental variable (IV)]] estimators can be seen as special cases of GMM. In an OLS regression <math>y_i = \beta x_i + \epsilon_i</math>, the assumption <math>\mathbb{E}(x_i \epsilon_i) = 0</math> is a moment condition. The OLS estimator can be derived by setting the sample analogue:

			<math>\frac{1}{N}\sum_i x_i (y_i - \hat\beta x_i) = 0</math>

	== References ==		== References ==
	<references />		<references />

Junhopark: 새 문서: '''Generalized method of moments''' (GMM) is an estimation framework that generalizes the classical method of moments. A '''moment condition''' is an expectation that equals zero at the true parameter values. Under certain theoretical moment conditions, parameters can be estimated by forcing their sample analogues as close to zero as possible.Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators. ''Econometrica'' 50(4): 1029-1054. W...

2025-08-28T04:12:59Z

새 문서: '''Generalized method of moments''' (GMM) is an estimation framework that generalizes the classical method of moments. A '''moment condition''' is an expectation that equals zero at the true parameter values. Under certain theoretical moment conditions, parameters can be estimated by forcing their sample analogues as close to zero as possible.<ref>Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators. ''Econometrica'' 50(4): 1029-1054.</ref> W...

새 문서

'''Generalized method of moments''' (GMM) is an estimation framework that generalizes the classical method of moments. A '''moment condition''' is an expectation that equals zero at the true parameter values. Under certain theoretical moment conditions, parameters can be estimated by forcing their sample analogues as close to zero as possible.<ref>Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators. ''Econometrica'' 50(4): 1029-1054.</ref> While GMM yields consistent, asymptotically normal estimates, it is flexible as it does not rely on a specific probability distribution for the data, contrary to other estimation methods such as maximum likelihood.

== Relation to OLS and IV ==

[[Ordinary least square (OLS)]] and [[instrumental variable (IV)]] estimators can be seen as special cases of GMM. In an OLS regression <math>y_i = \beta x_i + \epsilon_i</math>, the assumption <math>\mathbb{E}(x_i \epsilon_i) = 0</math> is a moment condition.

== References ==
<references />