Three Essays on Canadian Housing Markets and Electricity Market

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dc.contributor.advisorSun, Yiguo
dc.contributor.advisorStengos, Thanasis
dc.contributor.authorZhang, Yuan
dc.date.accessioned2017-05-16T18:22:34Z
dc.date.available2017-05-16T18:22:34Z
dc.date.copyright2017-05
dc.date.created2016-12-16
dc.date.issued2017-05-16
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.abstractThis thesis includes three empirical applications: spatial dynamic panel data model on Canadian housing market, impulse responses of Canadian housing market, and emission reductions of wind powered electricity in Ontario. Chapter 1 studies the spatial dependence of residential resale housing returns in 10 major Canadian Census Metropolitan areas (CMA) from 1992Q4 to 2012Q4 and makes the following methodological contributions. Firstly, in the context of a spatial dynamic panel data model we use grid search to derive the appropriate spatial weight matrix W among different possible specifications. We select the compound W with the minimum root mean squared error formed by geographical distances and the GDP levels. We further offer an interpretation of the selected W that is directly linked to the definition of the Arrow-Pratt risk aversion parameter. Secondly, contrary to common practice in the literature, we decompose our parameter estimates into direct and indirect effects and we proceed to derive and plot the impulse response functions of housing returns to external shocks. The empirical results suggest that Canadian residential housing markets exhibit statistically significant spatial dependence and spatial autocorrelation and both geographical distances and economic closeness are the dominant channels. Furthermore, in Chapter 2, we calculate impulse response functions and plot in 2-D and 3-D figures, and we find that special feature of the Canadian housing market is, as seen from the impulse response functions, that the responses to shocks do not spread widely across regions and that they fade fast over time. In Chapter 3, We use electricity output of 151 generators in seven fuel sources in year 2010, including nuclear, coal, natural gas, hydroelectric, oil/gas, wind, and wood waste/biomass, and aim to find out how wind energy affect the production of other power sources. We use three different models to estimate the marginal effects of wind powered generators on other fuel sources. The contribution of wind energy to the Ontario’s mixed electricity supply system in terms of decreasing the electricity production mainly from coal, natural gas and hydroelectric generators, and reducing the air pollutant (CO2, SO2, and NOx) emissions. When wind generators produce one MW per hour, the marginal effects from other fuel sources is replaced less than one MW. Due to instability of the wind power, the backup power is needed. The backup generators are usually thermal generators that emit air pollutants, and the fuel uses are larger than they operate at a steadily power level, which is so called "emission bias". Therefore, this makes the net marginal effects and emission reduction less than expected.en_US
dc.identifier.urihttp://hdl.handle.net/10214/10471
dc.language.isoenen_US
dc.publisherUniversity of Guelphen_US
dc.rightsAttribution-NonCommercial-NoDerivs 2.5 Canada*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/ca/*
dc.subjectSeasonal ARIMAX modelen_US
dc.subjectSpatial dependenceen_US
dc.subjectSpatial dynamic panel data modelen_US
dc.subjectSpatial weight matrix Wen_US
dc.subjectEmissions reductionen_US
dc.subjectWind energyen_US
dc.subjectCanadian residential housing returnsen_US
dc.subjectOntario wind electricityen_US
dc.titleThree Essays on Canadian Housing Markets and Electricity Marketen_US
dc.typeThesisen_US
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