Nowcasting by the BSTS-U-MIDAS Model

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dc.contributor.authorDuan, Jun
dc.contributor.supervisorGiles, David E. A.
dc.date.accessioned2015-09-23T20:12:55Z
dc.date.available2015-09-23T20:12:55Z
dc.date.copyright2015en_US
dc.date.issued2015-09-23
dc.degree.departmentDepartment of Economicsen_US
dc.degree.levelMaster of Arts M.A.en_US
dc.description.abstractUsing high frequency data for forecasting or nowcasting, we have to deal with three major problems: the mixed frequency problem, the high dimensionality (fat re- gression, parameter proliferation) problem, and the unbalanced data problem (miss- ing observations, ragged edge data). We propose a BSTS-U-MIDAS model (Bayesian Structural Time Series-Unlimited-Mixed-Data Sampling model) to handle these prob- lem. This model consists of four parts. First of all, a structural time series with regressors model (STM) is used to capture the dynamics of target variable, and the regressors are chosen to boost the forecast accuracy. Second, a MIDAS model is adopted to handle the mixed frequency of the regressors in the STM. Third, spike- and-slab regression is used to implement variable selection. Fourth, Bayesian model averaging (BMA) is used for nowcasting. We use this model to nowcast quarterly GDP for Canada, and find that this model outperform benchmark models: ARIMA model and Boosting model, in terms of MAE (mean absolute error) and MAPE (mean absolute percentage error).en_US
dc.description.proquestcode0501en_US
dc.description.proquestcode0508en_US
dc.description.proquestcode0463en_US
dc.description.proquestemailjonduan@uvic.caen_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/6711
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/ca/*
dc.subjectforecastingen_US
dc.subjectnowcastingen_US
dc.subjectBSTS-U-MIDAS modelen_US
dc.subjecthigh frequency dataen_US
dc.subjectmixed frequency problemen_US
dc.subjecthigh dimensionalityen_US
dc.titleNowcasting by the BSTS-U-MIDAS Modelen_US
dc.typeThesisen_US

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