Using 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). / Graduate / 0501 / 0508 / 0463 / jonduan@uvic.ca
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/6711 |
| Date | 23 September 2015 |
| Creators | Duan, Jun |
| Contributors | Giles, David E. A. |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Available to the World Wide Web, http://creativecommons.org/licenses/by-nc-nd/2.5/ca/ |
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