Finite Sample Properties of the Efficient Method of Moments
Gallant and Tauchen (1996) describe an estimation technique, known as Efficient Method ofMoments (EMM), that uses numerical methods to estimate parameters of a structural model. The technique usesas matching conditions (or moments, in the GMM jargon) the gradients of an auxiliary model that fits a s...
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| Autor*in: |
Chumacero, Rómulo A. [verfasserIn] |
|---|
| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
The Berkeley Electronic Press ; 1997 |
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| Schlagwörter: |
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| Reproduktion: |
Berkeley Electronic Press Academic Journals |
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| Übergeordnetes Werk: |
In: Studies in nonlinear dynamics and econometrics - Cambridge, Mass. : MIT Press, 1996, 2.1997, 2, art2 |
| Übergeordnetes Werk: |
volume:2 ; year:1997 ; number:2 ; pages:2 |
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| 520 | |a Gallant and Tauchen (1996) describe an estimation technique, known as Efficient Method ofMoments (EMM), that uses numerical methods to estimate parameters of a structural model. The technique usesas matching conditions (or moments, in the GMM jargon) the gradients of an auxiliary model that fits a subsetof variables that may be simulated from the structural model.This paper presents three Monte Carlo experiments to assess the finite sample properties of EMM. The first onecompares it with a fully efficient procedure (Maximum Likelihood) by estimating an invertible moving-average(MA) process. The second and third experiments compare the finite sample properties of the EMM estimatorswith those of GMM by using stochastic volatility models and consumption-based asset-pricing models. Theexperiments show that the gains in efficiency are impressive; however, given that both EMM and GMM share thesame type of objective function, finite sample inference based on asymptotic theory continues to lead, in somecases, to "over rejections," even though they are not as significant as in GMM. | ||
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(DE-627)NLEJ219573492 DE-627 ger DE-627 rakwb eng XD-US Chumacero, Rómulo A. verfasserin aut Finite Sample Properties of the Efficient Method of Moments The Berkeley Electronic Press 1997 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Gallant and Tauchen (1996) describe an estimation technique, known as Efficient Method ofMoments (EMM), that uses numerical methods to estimate parameters of a structural model. The technique usesas matching conditions (or moments, in the GMM jargon) the gradients of an auxiliary model that fits a subsetof variables that may be simulated from the structural model.This paper presents three Monte Carlo experiments to assess the finite sample properties of EMM. The first onecompares it with a fully efficient procedure (Maximum Likelihood) by estimating an invertible moving-average(MA) process. The second and third experiments compare the finite sample properties of the EMM estimatorswith those of GMM by using stochastic volatility models and consumption-based asset-pricing models. Theexperiments show that the gains in efficiency are impressive; however, given that both EMM and GMM share thesame type of objective function, finite sample inference based on asymptotic theory continues to lead, in somecases, to "over rejections," even though they are not as significant as in GMM. Berkeley Electronic Press Academic Journals Monte Carlo efficient method of moments maximum likelihood generalized method of In Studies in nonlinear dynamics and econometrics Cambridge, Mass. : MIT Press, 1996 2.1997, 2, art2 Online-Ressource (DE-627)NLEJ219537429 (DE-600)1385261-9 1081-1826 nnns volume:2 year:1997 number:2 pages:2 http://www.bepress.com/snde/vol2/iss2/art2 GBV_USEFLAG_U ZDB-1-BEP GBV_NL_ARTICLE AR 2 1997 2 2 2.1997, 2, art2 |
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(DE-627)NLEJ219573492 DE-627 ger DE-627 rakwb eng XD-US Chumacero, Rómulo A. verfasserin aut Finite Sample Properties of the Efficient Method of Moments The Berkeley Electronic Press 1997 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Gallant and Tauchen (1996) describe an estimation technique, known as Efficient Method ofMoments (EMM), that uses numerical methods to estimate parameters of a structural model. The technique usesas matching conditions (or moments, in the GMM jargon) the gradients of an auxiliary model that fits a subsetof variables that may be simulated from the structural model.This paper presents three Monte Carlo experiments to assess the finite sample properties of EMM. The first onecompares it with a fully efficient procedure (Maximum Likelihood) by estimating an invertible moving-average(MA) process. The second and third experiments compare the finite sample properties of the EMM estimatorswith those of GMM by using stochastic volatility models and consumption-based asset-pricing models. Theexperiments show that the gains in efficiency are impressive; however, given that both EMM and GMM share thesame type of objective function, finite sample inference based on asymptotic theory continues to lead, in somecases, to "over rejections," even though they are not as significant as in GMM. Berkeley Electronic Press Academic Journals Monte Carlo efficient method of moments maximum likelihood generalized method of In Studies in nonlinear dynamics and econometrics Cambridge, Mass. : MIT Press, 1996 2.1997, 2, art2 Online-Ressource (DE-627)NLEJ219537429 (DE-600)1385261-9 1081-1826 nnns volume:2 year:1997 number:2 pages:2 http://www.bepress.com/snde/vol2/iss2/art2 GBV_USEFLAG_U ZDB-1-BEP GBV_NL_ARTICLE AR 2 1997 2 2 2.1997, 2, art2 |
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(DE-627)NLEJ219573492 DE-627 ger DE-627 rakwb eng XD-US Chumacero, Rómulo A. verfasserin aut Finite Sample Properties of the Efficient Method of Moments The Berkeley Electronic Press 1997 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Gallant and Tauchen (1996) describe an estimation technique, known as Efficient Method ofMoments (EMM), that uses numerical methods to estimate parameters of a structural model. The technique usesas matching conditions (or moments, in the GMM jargon) the gradients of an auxiliary model that fits a subsetof variables that may be simulated from the structural model.This paper presents three Monte Carlo experiments to assess the finite sample properties of EMM. The first onecompares it with a fully efficient procedure (Maximum Likelihood) by estimating an invertible moving-average(MA) process. The second and third experiments compare the finite sample properties of the EMM estimatorswith those of GMM by using stochastic volatility models and consumption-based asset-pricing models. Theexperiments show that the gains in efficiency are impressive; however, given that both EMM and GMM share thesame type of objective function, finite sample inference based on asymptotic theory continues to lead, in somecases, to "over rejections," even though they are not as significant as in GMM. Berkeley Electronic Press Academic Journals Monte Carlo efficient method of moments maximum likelihood generalized method of In Studies in nonlinear dynamics and econometrics Cambridge, Mass. : MIT Press, 1996 2.1997, 2, art2 Online-Ressource (DE-627)NLEJ219537429 (DE-600)1385261-9 1081-1826 nnns volume:2 year:1997 number:2 pages:2 http://www.bepress.com/snde/vol2/iss2/art2 GBV_USEFLAG_U ZDB-1-BEP GBV_NL_ARTICLE AR 2 1997 2 2 2.1997, 2, art2 |
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(DE-627)NLEJ219573492 DE-627 ger DE-627 rakwb eng XD-US Chumacero, Rómulo A. verfasserin aut Finite Sample Properties of the Efficient Method of Moments The Berkeley Electronic Press 1997 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Gallant and Tauchen (1996) describe an estimation technique, known as Efficient Method ofMoments (EMM), that uses numerical methods to estimate parameters of a structural model. The technique usesas matching conditions (or moments, in the GMM jargon) the gradients of an auxiliary model that fits a subsetof variables that may be simulated from the structural model.This paper presents three Monte Carlo experiments to assess the finite sample properties of EMM. The first onecompares it with a fully efficient procedure (Maximum Likelihood) by estimating an invertible moving-average(MA) process. The second and third experiments compare the finite sample properties of the EMM estimatorswith those of GMM by using stochastic volatility models and consumption-based asset-pricing models. Theexperiments show that the gains in efficiency are impressive; however, given that both EMM and GMM share thesame type of objective function, finite sample inference based on asymptotic theory continues to lead, in somecases, to "over rejections," even though they are not as significant as in GMM. Berkeley Electronic Press Academic Journals Monte Carlo efficient method of moments maximum likelihood generalized method of In Studies in nonlinear dynamics and econometrics Cambridge, Mass. : MIT Press, 1996 2.1997, 2, art2 Online-Ressource (DE-627)NLEJ219537429 (DE-600)1385261-9 1081-1826 nnns volume:2 year:1997 number:2 pages:2 http://www.bepress.com/snde/vol2/iss2/art2 GBV_USEFLAG_U ZDB-1-BEP GBV_NL_ARTICLE AR 2 1997 2 2 2.1997, 2, art2 |
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(DE-627)NLEJ219573492 DE-627 ger DE-627 rakwb eng XD-US Chumacero, Rómulo A. verfasserin aut Finite Sample Properties of the Efficient Method of Moments The Berkeley Electronic Press 1997 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Gallant and Tauchen (1996) describe an estimation technique, known as Efficient Method ofMoments (EMM), that uses numerical methods to estimate parameters of a structural model. The technique usesas matching conditions (or moments, in the GMM jargon) the gradients of an auxiliary model that fits a subsetof variables that may be simulated from the structural model.This paper presents three Monte Carlo experiments to assess the finite sample properties of EMM. The first onecompares it with a fully efficient procedure (Maximum Likelihood) by estimating an invertible moving-average(MA) process. The second and third experiments compare the finite sample properties of the EMM estimatorswith those of GMM by using stochastic volatility models and consumption-based asset-pricing models. Theexperiments show that the gains in efficiency are impressive; however, given that both EMM and GMM share thesame type of objective function, finite sample inference based on asymptotic theory continues to lead, in somecases, to "over rejections," even though they are not as significant as in GMM. Berkeley Electronic Press Academic Journals Monte Carlo efficient method of moments maximum likelihood generalized method of In Studies in nonlinear dynamics and econometrics Cambridge, Mass. : MIT Press, 1996 2.1997, 2, art2 Online-Ressource (DE-627)NLEJ219537429 (DE-600)1385261-9 1081-1826 nnns volume:2 year:1997 number:2 pages:2 http://www.bepress.com/snde/vol2/iss2/art2 GBV_USEFLAG_U ZDB-1-BEP GBV_NL_ARTICLE AR 2 1997 2 2 2.1997, 2, art2 |
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Gallant and Tauchen (1996) describe an estimation technique, known as Efficient Method ofMoments (EMM), that uses numerical methods to estimate parameters of a structural model. The technique usesas matching conditions (or moments, in the GMM jargon) the gradients of an auxiliary model that fits a subsetof variables that may be simulated from the structural model.This paper presents three Monte Carlo experiments to assess the finite sample properties of EMM. The first onecompares it with a fully efficient procedure (Maximum Likelihood) by estimating an invertible moving-average(MA) process. The second and third experiments compare the finite sample properties of the EMM estimatorswith those of GMM by using stochastic volatility models and consumption-based asset-pricing models. Theexperiments show that the gains in efficiency are impressive; however, given that both EMM and GMM share thesame type of objective function, finite sample inference based on asymptotic theory continues to lead, in somecases, to "over rejections," even though they are not as significant as in GMM. |
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Gallant and Tauchen (1996) describe an estimation technique, known as Efficient Method ofMoments (EMM), that uses numerical methods to estimate parameters of a structural model. The technique usesas matching conditions (or moments, in the GMM jargon) the gradients of an auxiliary model that fits a subsetof variables that may be simulated from the structural model.This paper presents three Monte Carlo experiments to assess the finite sample properties of EMM. The first onecompares it with a fully efficient procedure (Maximum Likelihood) by estimating an invertible moving-average(MA) process. The second and third experiments compare the finite sample properties of the EMM estimatorswith those of GMM by using stochastic volatility models and consumption-based asset-pricing models. Theexperiments show that the gains in efficiency are impressive; however, given that both EMM and GMM share thesame type of objective function, finite sample inference based on asymptotic theory continues to lead, in somecases, to "over rejections," even though they are not as significant as in GMM. |
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Gallant and Tauchen (1996) describe an estimation technique, known as Efficient Method ofMoments (EMM), that uses numerical methods to estimate parameters of a structural model. The technique usesas matching conditions (or moments, in the GMM jargon) the gradients of an auxiliary model that fits a subsetof variables that may be simulated from the structural model.This paper presents three Monte Carlo experiments to assess the finite sample properties of EMM. The first onecompares it with a fully efficient procedure (Maximum Likelihood) by estimating an invertible moving-average(MA) process. The second and third experiments compare the finite sample properties of the EMM estimatorswith those of GMM by using stochastic volatility models and consumption-based asset-pricing models. Theexperiments show that the gains in efficiency are impressive; however, given that both EMM and GMM share thesame type of objective function, finite sample inference based on asymptotic theory continues to lead, in somecases, to "over rejections," even though they are not as significant as in GMM. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">NLEJ219573492</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20210707085957.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">090716s1997 xxu|||||o 00| ||eng c</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)NLEJ219573492</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="c">XD-US</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Chumacero, Rómulo A.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Finite Sample Properties of the Efficient Method of Moments</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="b">The Berkeley Electronic Press</subfield><subfield code="c">1997</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Gallant and Tauchen (1996) describe an estimation technique, known as Efficient Method ofMoments (EMM), that uses numerical methods to estimate parameters of a structural model. The technique usesas matching conditions (or moments, in the GMM jargon) the gradients of an auxiliary model that fits a subsetof variables that may be simulated from the structural model.This paper presents three Monte Carlo experiments to assess the finite sample properties of EMM. The first onecompares it with a fully efficient procedure (Maximum Likelihood) by estimating an invertible moving-average(MA) process. The second and third experiments compare the finite sample properties of the EMM estimatorswith those of GMM by using stochastic volatility models and consumption-based asset-pricing models. Theexperiments show that the gains in efficiency are impressive; however, given that both EMM and GMM share thesame type of objective function, finite sample inference based on asymptotic theory continues to lead, in somecases, to "over rejections," even though they are not as significant as in GMM.</subfield></datafield><datafield tag="533" ind1=" " ind2=" "><subfield code="f">Berkeley Electronic Press Academic Journals</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Monte Carlo</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">efficient method of moments</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">maximum likelihood</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">generalized method of</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Studies in nonlinear dynamics and econometrics</subfield><subfield code="d">Cambridge, Mass. : MIT Press, 1996</subfield><subfield code="g">2.1997, 2, art2</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)NLEJ219537429</subfield><subfield code="w">(DE-600)1385261-9</subfield><subfield code="x">1081-1826</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:2</subfield><subfield code="g">year:1997</subfield><subfield code="g">number:2</subfield><subfield code="g">pages:2</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.bepress.com/snde/vol2/iss2/art2</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-1-BEP</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_NL_ARTICLE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">2</subfield><subfield code="j">1997</subfield><subfield code="e">2</subfield><subfield code="h">2</subfield><subfield code="y">2.1997, 2, art2</subfield></datafield></record></collection>
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