A varying coefficient approach to global flexibility in demand analysis: a semiparametric approximation

To reduce dimensionality issues, this article derives a globally flexible demand system that can be estimated non-parametrically with a specially devised temporal kernel. Statistical and economic results from a meat demand application underscores the usefulness of a temporal kernel in globally appro...

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Autores:: Sarmiento, Camilo

Tipo de recurso:: Article of journal

Fecha de publicación:: 2005

Institución:: Escuela Colombiana de Ingeniería Julio Garavito

Repositorio:: Repositorio Institucional ECI

Idioma:: eng

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dc.title.eng.fl_str_mv	A varying coefficient approach to global flexibility in demand analysis: a semiparametric approximation
title	A varying coefficient approach to global flexibility in demand analysis: a semiparametric approximation
spellingShingle	A varying coefficient approach to global flexibility in demand analysis: a semiparametric approximation Demanda (teoría económica) Elasticidad (economía) Funciones de demanda (teoría económica) Economía - Modelos matemáticos demand system global flexibility integrability kernel regression time varying coefficient Sistema de demanda Flexibilidad global Integrabilidad Regresión del núcleo Coeficiente variable en el tiempo
title_short	A varying coefficient approach to global flexibility in demand analysis: a semiparametric approximation
title_full	A varying coefficient approach to global flexibility in demand analysis: a semiparametric approximation
title_fullStr	A varying coefficient approach to global flexibility in demand analysis: a semiparametric approximation
title_full_unstemmed	A varying coefficient approach to global flexibility in demand analysis: a semiparametric approximation
title_sort	A varying coefficient approach to global flexibility in demand analysis: a semiparametric approximation
dc.creator.fl_str_mv	Sarmiento, Camilo
dc.contributor.author.none.fl_str_mv	Sarmiento, Camilo
dc.contributor.researchgroup.spa.fl_str_mv	Centro de Estudios Hidráulicos
dc.subject.armarc.none.fl_str_mv	Demanda (teoría económica) Elasticidad (economía) Funciones de demanda (teoría económica) Economía - Modelos matemáticos
topic	Demanda (teoría económica) Elasticidad (economía) Funciones de demanda (teoría económica) Economía - Modelos matemáticos demand system global flexibility integrability kernel regression time varying coefficient Sistema de demanda Flexibilidad global Integrabilidad Regresión del núcleo Coeficiente variable en el tiempo
dc.subject.proposal.eng.fl_str_mv	demand system global flexibility integrability kernel regression time varying coefficient
dc.subject.proposal.spa.fl_str_mv	Sistema de demanda Flexibilidad global Integrabilidad Regresión del núcleo Coeficiente variable en el tiempo
description	To reduce dimensionality issues, this article derives a globally flexible demand system that can be estimated non-parametrically with a specially devised temporal kernel. Statistical and economic results from a meat demand application underscores the usefulness of a temporal kernel in globally approximating an integrable demand system.
publishDate	2005
dc.date.issued.none.fl_str_mv	2005-02
dc.date.accessioned.none.fl_str_mv	2024-01-31T17:13:53Z
dc.date.available.none.fl_str_mv	2024-01-31T17:13:53Z
dc.type.spa.fl_str_mv	Artículo de revista
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dc.identifier.issn.spa.fl_str_mv	0002-9092
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dc.identifier.eissn.spa.fl_str_mv	0002-9092
dc.identifier.instname.spa.fl_str_mv	Universidad Escuela Colombiana de Ingeniería Julio Garavito
dc.identifier.reponame.spa.fl_str_mv	Repositorio Digital
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identifier_str_mv	0002-9092 Universidad Escuela Colombiana de Ingeniería Julio Garavito Repositorio Digital
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dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.citationedition.spa.fl_str_mv	No. 1 Vol. 87 Febrero 2005
dc.relation.citationendpage.spa.fl_str_mv	47
dc.relation.citationissue.spa.fl_str_mv	1
dc.relation.citationstartpage.spa.fl_str_mv	38
dc.relation.citationvolume.spa.fl_str_mv	87
dc.relation.ispartofjournal.eng.fl_str_mv	American Journal of Agricultural Economics
dc.relation.references.spa.fl_str_mv	Alston, J.L., and J.A. Chalfant. “The Silence of the Lambdas: A Test of the Almost Ideal and Rotterdam Models.” American Journal of Agricultural Economics 75(1993):304–13. Alston, J.L., and R. Green. “Elasticities in AIDS Models.” American Journal of Agricultural Economics 72(1990):442–45. Barten, A.P., and L.J. Bettendorf. “Price Formation of Fish: An Application of an Inverse Demand System.” European Economic Review 33(1989):1509–26. Cleveland, W.S. “Robust Locally Weighted Regression and Smoothing Scatterplots.” Journal of the American Statistical Association 79(1979):829–36. Deaton, A.S., and J. Muellbauer. “An Almost Ideal Demand System.” American Economic Review 70(1980):312–26. Diewert, W.E. “Application of Duality Theory.” In M.D. Intrilligator and D.S. Kendtrick, eds. In Frontiers of Quantitative Economics. Amsterdam: North-Holland Publishing Co., 1974, pp. 106–71. Engle, R.F., W.J. Granger, J. Rice, and A. Weiss. “Semiparametric Estimates of the Relation between Weather and Electricity Sales.” Journal of the American Statistical Association 81(1986):310–20. Fan, J. “Design Adaptive Nonparametric Regression.” Journal of the American Statistical Association 87(1992):998–1004. ——. “Local Linear Regression Smoothers and their Minimax Efficiencies.” Annals of Statistics 30(1993):196–216. Gallant, A.R. “On the Bias in Flexible Functional Forms and Essentially Unbiased from the Fourier Flexible Form.” Journal of Econometrics 15(1981):211–45. Granger, C.W.J. “Macroeconomics: Past and Future.” Journal of Econometrics 100(2001):17– 19. Jorgenson, D.W., L.J. Lau, and T.M. Stoker. “The Trascendental Logarithmic Model of Aggregate Consumer Behaviour.” In R.L. Basmann and G. Rhodes, eds. Advances in Econometrics. Vol.1. 1982, pp. 97–238 Moschini, G., and K.D. Meilke. “Modeling the Pattern of Structural Change in U.S. Meat Demand.” American Journal of Agricultural Economics 71(1989):235–61. Moschini, G., and D. Moro. “Structural Change and Demand Analysis: A Cursory Review.” European Review of Agricultural Economics 23(1996):239–61. Piggott, N.E. “The Nested PIGLOG Model: An Application to US Food Demand.” American Journal of Agricultural Economics 84(2003):1– 15. Rudin, W. Principles of Mathematical Analysis, Third Edition. New York: McGraw-Hill, 1976. Ruppert D., and M.P. Wand. “Multivariate Locally Weighted Least Squares Regression.” Annals of Statistics 9(1994):65–78 Schmalensee, R., and T. Stoker. “Household Gasoline Demand in the U.S.” Econometrica 67(1999):645–62. Silverman, B.W. Density Estimation for Statistics and Data Analysis. New York: Chapman and Hall, 1986. Stone, R. “Measurement of Consumer’s Expenditures and Behavior in the United Kingdom.” Vol. 1. Cambridge, UK: Cambridge University Press, 1954 Stone, C.J. “Optimal Convergence Rates for Nonparametric Estimators.” Annals of Statistics 8(1980):1348–60 Zellner, A. “An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests of Aggregation Bias.” Journal of the American Statistical Association 57(1962):348– 68.
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dc.format.extent.spa.fl_str_mv	11 páginas
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dc.publisher.spa.fl_str_mv	American Agricultural Economics Association
dc.publisher.place.spa.fl_str_mv	Estados Unidos
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spelling	Sarmiento, Camiloa48a67bcfacaaa8d4773d8fd3f6d85acCentro de Estudios Hidráulicos2024-01-31T17:13:53Z2024-01-31T17:13:53Z2005-020002-9092https://repositorio.escuelaing.edu.co/handle/001/28020002-9092Universidad Escuela Colombiana de Ingeniería Julio GaravitoRepositorio Digitalhttps://repositorio.escuelaing.edu.coTo reduce dimensionality issues, this article derives a globally flexible demand system that can be estimated non-parametrically with a specially devised temporal kernel. Statistical and economic results from a meat demand application underscores the usefulness of a temporal kernel in globally approximating an integrable demand system.Para reducir los problemas de dimensionalidad, este artículo deriva un sistema de demanda globalmente flexible que puede estimarse de forma no paramétrica con un núcleo temporal especialmente diseñado. Los resultados estadísticos y económicos de una aplicación de la demanda de carne subrayan la utilidad de un núcleo temporal para aproximarse globalmente a un sistema de demanda integrable.11 páginasapplication/pdfengAmerican Agricultural Economics AssociationEstados Unidoshttps://www.aaea.org/A varying coefficient approach to global flexibility in demand analysis: a semiparametric approximationArtículo de revistainfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85No. 1 Vol. 87 Febrero 20054713887American Journal of Agricultural EconomicsAlston, J.L., and J.A. Chalfant. “The Silence of the Lambdas: A Test of the Almost Ideal and Rotterdam Models.” American Journal of Agricultural Economics 75(1993):304–13.Alston, J.L., and R. Green. “Elasticities in AIDS Models.” American Journal of Agricultural Economics 72(1990):442–45.Barten, A.P., and L.J. Bettendorf. “Price Formation of Fish: An Application of an Inverse Demand System.” European Economic Review 33(1989):1509–26.Cleveland, W.S. “Robust Locally Weighted Regression and Smoothing Scatterplots.” Journal of the American Statistical Association 79(1979):829–36.Deaton, A.S., and J. Muellbauer. “An Almost Ideal Demand System.” American Economic Review 70(1980):312–26.Diewert, W.E. “Application of Duality Theory.” In M.D. Intrilligator and D.S. Kendtrick, eds. In Frontiers of Quantitative Economics. Amsterdam: North-Holland Publishing Co., 1974, pp. 106–71.Engle, R.F., W.J. Granger, J. Rice, and A. Weiss. “Semiparametric Estimates of the Relation between Weather and Electricity Sales.” Journal of the American Statistical Association 81(1986):310–20.Fan, J. “Design Adaptive Nonparametric Regression.” Journal of the American Statistical Association 87(1992):998–1004.——. “Local Linear Regression Smoothers and their Minimax Efficiencies.” Annals of Statistics 30(1993):196–216.Gallant, A.R. “On the Bias in Flexible Functional Forms and Essentially Unbiased from the Fourier Flexible Form.” Journal of Econometrics 15(1981):211–45.Granger, C.W.J. “Macroeconomics: Past and Future.” Journal of Econometrics 100(2001):17– 19.Jorgenson, D.W., L.J. Lau, and T.M. Stoker. “The Trascendental Logarithmic Model of Aggregate Consumer Behaviour.” In R.L. Basmann and G. Rhodes, eds. Advances in Econometrics. Vol.1. 1982, pp. 97–238Moschini, G., and K.D. Meilke. “Modeling the Pattern of Structural Change in U.S. Meat Demand.” American Journal of Agricultural Economics 71(1989):235–61.Moschini, G., and D. Moro. “Structural Change and Demand Analysis: A Cursory Review.” European Review of Agricultural Economics 23(1996):239–61.Piggott, N.E. “The Nested PIGLOG Model: An Application to US Food Demand.” American Journal of Agricultural Economics 84(2003):1– 15.Rudin, W. Principles of Mathematical Analysis, Third Edition. New York: McGraw-Hill, 1976.Ruppert D., and M.P. Wand. “Multivariate Locally Weighted Least Squares Regression.” Annals of Statistics 9(1994):65–78Schmalensee, R., and T. Stoker. “Household Gasoline Demand in the U.S.” Econometrica 67(1999):645–62.Silverman, B.W. Density Estimation for Statistics and Data Analysis. New York: Chapman and Hall, 1986.Stone, R. “Measurement of Consumer’s Expenditures and Behavior in the United Kingdom.” Vol. 1. Cambridge, UK: Cambridge University Press, 1954Stone, C.J. “Optimal Convergence Rates for Nonparametric Estimators.” Annals of Statistics 8(1980):1348–60Zellner, A. “An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests of Aggregation Bias.” Journal of the American Statistical Association 57(1962):348– 68.info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbDemanda (teoría económica)Elasticidad (economía)Funciones de demanda (teoría económica)Economía - Modelos matemáticosdemand systemglobal flexibilityintegrabilitykernel regressiontime varying coefficientSistema de demandaFlexibilidad globalIntegrabilidadRegresión del núcleoCoeficiente variable en el tiempoTEXTA Varying Coefficient Approach to Global Flexibility in Demand Analysis (1).pdf.txtA Varying Coefficient Approach to Global Flexibility in Demand Analysis (1).pdf.txtExtracted texttext/plain36094https://repositorio.escuelaing.edu.co/bitstream/001/2802/4/A%20Varying%20Coefficient%20Approach%20to%20Global%20Flexibility%20in%20Demand%20Analysis%20%281%29.pdf.txt087544a9cdc0c22bdb43308ab33e29feMD54open accessTHUMBNAILPORTADA A VARYING COEFFICIENT APPROACH TO GLOBAL (1).JPGPORTADA A VARYING COEFFICIENT APPROACH TO GLOBAL (1).JPGimage/jpeg182979https://repositorio.escuelaing.edu.co/bitstream/001/2802/3/PORTADA%20A%20VARYING%20COEFFICIENT%20APPROACH%20TO%20GLOBAL%20%281%29.JPGb6d3b8ad8f334343873da34851023a1cMD53open accessA Varying Coefficient Approach to Global Flexibility in Demand Analysis (1).pdf.jpgA Varying Coefficient Approach to Global Flexibility in Demand Analysis (1).pdf.jpgGenerated Thumbnailimage/jpeg15595https://repositorio.escuelaing.edu.co/bitstream/001/2802/5/A%20Varying%20Coefficient%20Approach%20to%20Global%20Flexibility%20in%20Demand%20Analysis%20%281%29.pdf.jpg8b7ad5d02aca312475845ec2f2d9e304MD55metadata only accessLICENSElicense.txtlicense.txttext/plain; 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A varying coefficient approach to global flexibility in demand analysis: a semiparametric approximation

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