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Dynamic Discrete Choice Models: Methods, Matlab Code, and Exercises

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 24 Nov 2020 English Publisher:Zenodo
Authors: Jaap H. Abbring; Tobias J. Klein;

doi: 10.5281/zenodo.4287733 , 10.5281/zenodo.7259620 , 10.5281/zenodo.4287734 , 10.5281/zenodo.5938849

Dynamic Discrete Choice Models: Methods, Matlab Code, and Exercises

Abstract

Matlab package for teaching and learning the econometrics of dynamic discrete choice models. See http://ddc.abbring.org for extensive documentation and exercises. See https://github.com/jabbring/dynamic-discrete-choice for a public Github repository. Run the main Matlab script dynamicDiscreteChoice.m for an example of the estimation of a dynamic discrete choice model. The current release includes some minor text edits and excludes wercker.yml, which is now obsolete. We welcome the use of this software under an MIT license. © 2020 Jaap H. Abbring and Tobias J. Klein

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Keywords

computational economics, industrial organization, dynamic discrete choice, estimation, quantitative economics, Markov decision problem, econometrics, teaching

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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