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Dynamics of the US Housing Market: A Quantal Response Statistical Equilibrium Approach

In this article, we demonstrate that a quantal response statistical equilibrium approach to the US housing market with the help of the maximum entropy method of modeling is a powerful way of revealing different characteristics of the housing market behavior before, during and after the recent housin...

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Detalles Bibliográficos
Autor principal: Ömer, Özlem
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512393/
https://www.ncbi.nlm.nih.gov/pubmed/33266555
http://dx.doi.org/10.3390/e20110831
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author Ömer, Özlem
author_facet Ömer, Özlem
author_sort Ömer, Özlem
collection PubMed
description In this article, we demonstrate that a quantal response statistical equilibrium approach to the US housing market with the help of the maximum entropy method of modeling is a powerful way of revealing different characteristics of the housing market behavior before, during and after the recent housing market crash in the US. In this line, a maximum entropy approach to quantal response statistical equilibrium model (QRSE) is employed in order to model housing market dynamics in different phases of the most recent housing market cycle using the S&P Case Shiller housing price index for 20 largest- Metropolitan Regions, and Freddie Mac housing price index (FMHPI) for 367 Metropolitan Cities for the US between 2000 and 2015. Estimated model parameters provide an alternative way to understand and explain the behaviors of economic agents, and market dynamics by questioning the traditional economic theory, which takes assumption for the behavior of rational utility maximizing representative agent with self-fulfilled expectations as given.
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spelling pubmed-75123932020-11-09 Dynamics of the US Housing Market: A Quantal Response Statistical Equilibrium Approach Ömer, Özlem Entropy (Basel) Article In this article, we demonstrate that a quantal response statistical equilibrium approach to the US housing market with the help of the maximum entropy method of modeling is a powerful way of revealing different characteristics of the housing market behavior before, during and after the recent housing market crash in the US. In this line, a maximum entropy approach to quantal response statistical equilibrium model (QRSE) is employed in order to model housing market dynamics in different phases of the most recent housing market cycle using the S&P Case Shiller housing price index for 20 largest- Metropolitan Regions, and Freddie Mac housing price index (FMHPI) for 367 Metropolitan Cities for the US between 2000 and 2015. Estimated model parameters provide an alternative way to understand and explain the behaviors of economic agents, and market dynamics by questioning the traditional economic theory, which takes assumption for the behavior of rational utility maximizing representative agent with self-fulfilled expectations as given. MDPI 2018-10-30 /pmc/articles/PMC7512393/ /pubmed/33266555 http://dx.doi.org/10.3390/e20110831 Text en © 2018 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ömer, Özlem
Dynamics of the US Housing Market: A Quantal Response Statistical Equilibrium Approach
title Dynamics of the US Housing Market: A Quantal Response Statistical Equilibrium Approach
title_full Dynamics of the US Housing Market: A Quantal Response Statistical Equilibrium Approach
title_fullStr Dynamics of the US Housing Market: A Quantal Response Statistical Equilibrium Approach
title_full_unstemmed Dynamics of the US Housing Market: A Quantal Response Statistical Equilibrium Approach
title_short Dynamics of the US Housing Market: A Quantal Response Statistical Equilibrium Approach
title_sort dynamics of the us housing market: a quantal response statistical equilibrium approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512393/
https://www.ncbi.nlm.nih.gov/pubmed/33266555
http://dx.doi.org/10.3390/e20110831
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