Essays on Model Uncertainty and Real Estate Markets

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dc.contributor.advisorSun, Yiguo
dc.contributor.advisorAnglin, Paul
dc.contributor.advisorStengos, Thanasis
dc.contributor.authorXiao, Hui
dc.date.accessioned2020-09-01T18:42:33Z
dc.date.available2020-09-01T18:42:33Z
dc.date.copyright2020-08
dc.date.created2020-07-20
dc.date.issued2019-06
dc.degree.departmentDepartment of Economics and Financeen_US
dc.degree.grantorUniversity of Guelphen_US
dc.degree.nameDoctor of Philosophyen_US
dc.degree.programmeEconomicsen_US
dc.description.abstractChapter 1 focuses on model selection and model averaging, both of which are approaches for handling modelling uncertainties. I aim to supplement the literature by studying the class of OLS post-selection estimators. Inspired by the shrinkage averaging estimator (SAE) and the Mallows model averaging (MMA) criterion, I further propose a shrinkage MMA (SMMA) estimator for averaging high-dimensional sparse models. The Monte Carlo design features an expanding sparse parameter space and further considers the effect of the effective sample size and the degree of model sparsity on estimators' finite sample performances. I find that the SMMA outperforms when averaging high-dimensional sparse models. In Chapter 2, the conventional perfect competition model is inadequate for the heterogeneous, illiquid, and decentralized housing market, which clears via multiple local time-varying equilibria. I first propose a spatial search model that caters to such market characteristics and provides theoretical micro-foundations to motivate the econometric model. Then, I introduce a nonlinear spatiotemporal autoregressive model with autoregressive disturbances (NLSTARAR) and augmented by local time-varying factors to unify the current hedonic pricing framework and uncover the real estate market structure by simultaneously identifying the spatiotemporal structure of the market's spatial dependence and its interaction with the housing market microstructure. To address model uncertainty, I propose both model selection and model averaging estimation strategies. Chapter 3 applies the methodologies developed in Chapter 2 to study the Greater Toronto Area (GTA) real estate market using a unique GTA dataset. The NLSTARAR model captures the effects of the local time-varying market microstructure besides the hedonic, demographic, and policy effects on the housing market. By model selection, I show that the real estate pricing is driven by a local time-varying market structure that effectively responds to the heterogeneity in assets consistent with existing theories. The local time-varying market microstructure dominates the spatial spillover effects with unexpected market shocks generating the market volatility. I further employ the rolling window approach to show that the uncovered real estate market structure captures the shifts in the market state, evolves as a market pricing mechanism, and better forecasts the real estate market out-of-sample.en_US
dc.identifier.citationXiao, H., & Sun, Y. (2019). On Tuning Parameter Selection in Model Selection and Model Averaging: A Monte Carlo Study. Journal of Risk and Financial Management, 12(3), 109. DOI:10.3390/jrfm12030109. http://dx.doi.org/10.3390/jrfm12030109
dc.identifier.urihttps://hdl.handle.net/10214/21156
dc.language.isoenen_US
dc.publisherUniversity of Guelphen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectreal estateen_US
dc.subjecthedonic pricingen_US
dc.subjectcross-sectional dependenceen_US
dc.subjectnonlinear regressionen_US
dc.subjectmodel averagingen_US
dc.subjectmodel selectionen_US
dc.subjecttuning parameter choiceen_US
dc.titleEssays on Model Uncertainty and Real Estate Marketsen_US
dc.typeThesisen_US
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