Estimating Discrete Joint Probability Distributions for Demographic Characteristics at the Store Level Given Store Level Marginal Distributions and a Market-wide Joint Distribution PDF Download
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Author: Charles J. Romeo
Publisher:
ISBN:
Category : Demography
Languages : en
Pages : 44
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Book Description
Author: Charles J. Romeo
Publisher:
ISBN:
Category : Demography
Languages : en
Pages : 44
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Book Description
Author: Charles J. Romeo
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
This paper provides a solution to the problem of estimating a joint distribution using the associated marginal distributions and a related joint distribution. The particular application we have in mind is for estimating joint distributions of demographic characteristics corresponding to market areas for individual retail stores. Marginal distributions are generally available at the census tract level, but joint distributions are only available for Metropolitan Statistical Areas which are generally much larger than the market for a single retail store. Joint distributions over demographics are an important input into mixed logit demand models for aggregate data. Market shares that vary systematically with demographics are essential for relieving the restrictions imposed by the Independence of Irrelevant Alternative property of the logit model. We approach this problem by formulating a parametric function that incorporates both the city-wide joint distributional information and marginal information specific to the retail store's market area. To estimate the function, we form moment conditions equating the moments of the parametric function to observed data, and we input these into a GMM objective. In one of our illustrations we use four marginal demographic distributions from each of eight stores in Dominick's Finer Foods data archive to estimate a four dimensional joint distribution for each store. Our results show that our GMM approach produces estimated joint distributions which emit marginals that closely match the observed marginal distributions. Mixed logit demand estimates are also presented with show the estimates to be sensitive to the formulation of the demographics distribution.
Author: Dean V. Williamson
Publisher:
ISBN:
Category : Contracts
Languages : en
Pages : 66
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Author: Charles J. Romeo
Publisher:
ISBN:
Category : Consumers' preferences
Languages : en
Pages : 68
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Author: Abram van Dijk
Publisher: Rozenberg Publishers
ISBN: 9036101344
Category :
Languages : en
Pages : 138
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Finite mixture distributions are a weighted average of a finite number of distributions. The latter are usually called the mixture components. The weights are usually described by a multinomial distribution and are sometimes called mixing proportions. The mixture components may be the same type of distributions with di®erent parameter values but they may also be completely different distributions. Therefore, finite mixture distributions are very °exible for modeling data. They are frequently used as a building block within many modern econometric models. The specification of the mixture distribution depends on the modeling problem at hand. In this thesis, we introduce new applications of finite mixtures to deal with several di®erent modeling issues. Each chapter of the thesis focusses on a specific modeling issue. The parameters of some of the resulting models can be estimated using standard techniques but for some of the chapters we need to develop new estimation and inference methods. To illustrate how the methods can be applied, we analyze at least one empirical data set for each approach. These data sets cover a wide range of research fields, such as macroeconomics, marketing, and political science. We show the usefulness of the methods and, in some cases, the improvement over previous methods in the literature.
Author: Gregory J. Werden
Publisher:
ISBN:
Category : Airlines
Languages : en
Pages : 28
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Author: Russell Pittman
Publisher:
ISBN:
Category : Electric utilities
Languages : en
Pages : 38
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Author:
Publisher: DIANE Publishing
ISBN: 1428953213
Category :
Languages : en
Pages : 29
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Author: K. Krishnamoorthy
Publisher: CRC Press
ISBN: 1420011375
Category : Mathematics
Languages : en
Pages : 371
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In the area of applied statistics, scientists use statistical distributions to model a wide range of practical problems, from modeling the size grade distribution of onions to modeling global positioning data. To apply these probability models successfully, practitioners and researchers must have a thorough understanding of the theory as well as a
Author: Norman L. Johnson
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 360
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