UK housing bubbles and contagion : identification under cross-sectional dependence and spatial heterogeneity

Recent trends in econometrics have led to a proliferation of statistical tools that lend themselves to data-driven evaluations of the housing market (Phillips et al., 2011, 2015; Bailey et al., 2016). This thesis evaluates the suitability of existing statistical procedures in the identification of e...

Full description

Bibliographic Details
Main Author: Chelva, Beulah
Other Authors: Chaudhuri, Kausik ; Shin, Yongcheol
Published: University of Leeds 2018
Subjects:
658
Online Access:https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.745627
id ndltd-bl.uk-oai-ethos.bl.uk-745627
record_format oai_dc
spelling ndltd-bl.uk-oai-ethos.bl.uk-7456272019-03-05T16:04:28ZUK housing bubbles and contagion : identification under cross-sectional dependence and spatial heterogeneityChelva, BeulahChaudhuri, Kausik ; Shin, Yongcheol2018Recent trends in econometrics have led to a proliferation of statistical tools that lend themselves to data-driven evaluations of the housing market (Phillips et al., 2011, 2015; Bailey et al., 2016). This thesis evaluates the suitability of existing statistical procedures in the identification of explosive unit root processes, alongside the analysis of price diffusion in a time and cross-sectionally dependent system. Having date-stamped three regional housing bubbles consistent with the historical narrative, the predictive ability of macroeconomic and financial variables in bubble formation is estimated. Implementing the multi-step procedure proposed by Bailey et al. (2016), we estimate a heterogeneous dynamic spatial autoregressive model of the English housing market. The spatial parameters yield unexpected results in contradiction to prevailing economic theories of spatial dependence that remain unaddressed in the literature. To this end, we derive a unifying framework that captures the endemically heterogeneous characteristics of house price spillovers with joint treatment of common factors without loss of generality. The spatio-temporal autoregressive model with factors presents a parsimonious representation of house price diffusion with directional analysis of spillovers and identification of dominant units in the network. The derived spatial and system-wide diffusion multipliers provide meaningful insights into how a perturbation in neighbourhood house price inflation impacts a given district over a specified time horizon. The results reflect the London-centric ripple effect as a dominant factor while core-periphery spillovers dominate in urbanised areas. This thesis contributes a salient evaluation of housing dynamics and network effects consistent with theories of rational bubbles, new economic geography and spatial dependence.658University of Leedshttps://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.745627http://etheses.whiterose.ac.uk/20815/Electronic Thesis or Dissertation
collection NDLTD
sources NDLTD
topic 658
spellingShingle 658
Chelva, Beulah
UK housing bubbles and contagion : identification under cross-sectional dependence and spatial heterogeneity
description Recent trends in econometrics have led to a proliferation of statistical tools that lend themselves to data-driven evaluations of the housing market (Phillips et al., 2011, 2015; Bailey et al., 2016). This thesis evaluates the suitability of existing statistical procedures in the identification of explosive unit root processes, alongside the analysis of price diffusion in a time and cross-sectionally dependent system. Having date-stamped three regional housing bubbles consistent with the historical narrative, the predictive ability of macroeconomic and financial variables in bubble formation is estimated. Implementing the multi-step procedure proposed by Bailey et al. (2016), we estimate a heterogeneous dynamic spatial autoregressive model of the English housing market. The spatial parameters yield unexpected results in contradiction to prevailing economic theories of spatial dependence that remain unaddressed in the literature. To this end, we derive a unifying framework that captures the endemically heterogeneous characteristics of house price spillovers with joint treatment of common factors without loss of generality. The spatio-temporal autoregressive model with factors presents a parsimonious representation of house price diffusion with directional analysis of spillovers and identification of dominant units in the network. The derived spatial and system-wide diffusion multipliers provide meaningful insights into how a perturbation in neighbourhood house price inflation impacts a given district over a specified time horizon. The results reflect the London-centric ripple effect as a dominant factor while core-periphery spillovers dominate in urbanised areas. This thesis contributes a salient evaluation of housing dynamics and network effects consistent with theories of rational bubbles, new economic geography and spatial dependence.
author2 Chaudhuri, Kausik ; Shin, Yongcheol
author_facet Chaudhuri, Kausik ; Shin, Yongcheol
Chelva, Beulah
author Chelva, Beulah
author_sort Chelva, Beulah
title UK housing bubbles and contagion : identification under cross-sectional dependence and spatial heterogeneity
title_short UK housing bubbles and contagion : identification under cross-sectional dependence and spatial heterogeneity
title_full UK housing bubbles and contagion : identification under cross-sectional dependence and spatial heterogeneity
title_fullStr UK housing bubbles and contagion : identification under cross-sectional dependence and spatial heterogeneity
title_full_unstemmed UK housing bubbles and contagion : identification under cross-sectional dependence and spatial heterogeneity
title_sort uk housing bubbles and contagion : identification under cross-sectional dependence and spatial heterogeneity
publisher University of Leeds
publishDate 2018
url https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.745627
work_keys_str_mv AT chelvabeulah ukhousingbubblesandcontagionidentificationundercrosssectionaldependenceandspatialheterogeneity
_version_ 1718999854875672576