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Semiparametric estimation and infere...

Fahs, Rafic Habib.

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Semiparametric estimation and inference in multinomial choice and systems of censored demand equation models with application to estimating demand systems.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Semiparametric estimation and inference in multinomial choice and systems of censored demand equation models with application to estimating demand systems./
作者:	Fahs, Rafic Habib.
面頁冊數:	138 p.
附註:	Chair: Ron C. Mittelhammer.
Contained By:	Dissertation Abstracts International63-04A.
標題:	Economics, Agricultural. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3051915
ISBN:	049365917X

Semiparametric estimation and inference in multinomial choice and systems of censored demand equation models with application to estimating demand systems.
Fahs, Rafic Habib.

Semiparametric estimation and inference in multinomial choice and systems of censored demand equation models with application to estimating demand systems. - 138 p.

Chair: Ron C. Mittelhammer.

Thesis (Ph.D.)--Washington State University, 2001.

The first essay incorporates semiparametric alternatives to maximum likelihood estimation and inference in the context of unordered multinomial response data when in practice there is often insufficient information to specify the parametric form of the function linking the observables to the unknown probabilities. We specify the function linking the observables to the unknown probabilities using a very general flexible class of functions belonging to the Pearson system of cumulative distribution equations. In this setting we consider the observations as arising from a multinomial distribution characterized by one of the CDFs in the Pearson system. Given this situation, it is possible to utilize the concept of unbiased estimating functions (EFs), combined with the concept of empirical likelihood (EL) to define an (empirical) likelihood function for the parameter vector based on a nonparametric representation of the sample's PDF. This leads to the concept of maximum empirical likelihood (MEL) estimation and inference, which is analogous to parametric maximum likelihood methods in many respects.

ISBN: 049365917XSubjects--Topical Terms:

626648
Economics, Agricultural.

Semiparametric estimation and inference in multinomial choice and systems of censored demand equation models with application to estimating demand systems.
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245 1 0 $a Semiparametric estimation and inference in multinomial choice and systems of censored demand equation models with application to estimating demand systems.
300 $a 138 p.
500 $a Chair: Ron C. Mittelhammer.
500 $a Source: Dissertation Abstracts International, Volume: 63-04, Section: A, page: 1466.
502 $a Thesis (Ph.D.)--Washington State University, 2001.
520 $a The first essay incorporates semiparametric alternatives to maximum likelihood estimation and inference in the context of unordered multinomial response data when in practice there is often insufficient information to specify the parametric form of the function linking the observables to the unknown probabilities. We specify the function linking the observables to the unknown probabilities using a very general flexible class of functions belonging to the Pearson system of cumulative distribution equations. In this setting we consider the observations as arising from a multinomial distribution characterized by one of the CDFs in the Pearson system. Given this situation, it is possible to utilize the concept of unbiased estimating functions (EFs), combined with the concept of empirical likelihood (EL) to define an (empirical) likelihood function for the parameter vector based on a nonparametric representation of the sample's PDF. This leads to the concept of maximum empirical likelihood (MEL) estimation and inference, which is analogous to parametric maximum likelihood methods in many respects.
520 $a The second essay utilizes the generalized method of moments (GMM) approach for estimating a system of multivariate Tobit equations and proposes a practical consistent estimator of model parameters. The GMM approach is based on a common set of general marginal and bivariate moment relations that hold between explanatory variables and model noise. Our consistent estimator may not be fully efficient, but it is an empirically tractable way of estimating a system of censored regressions involving large data sets with a relatively high dimensionality of censored observations.
520 $a The third essay applies the generalized method of moments (GMM) approach for estimating a system of censored demand equations based on data from a large cross-sectional survey of Chinese Household Expenditure where zero outcomes are more likely to be the expression of a corner solution (rather than infrequency of purchase). We motivate the choice of the Tobit model as a statistical representation of consumer behavior and introduce the GMM method by specifying the AIDS model modified according to translating demographic transformation. The empirical results of the censored demand system appear in general quite reasonable which illustrate the usefulness of the proposed approach.
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