Time Series Research at Statistics Canada
Steve Matthews from Statistics Canada presents current research on variance estimation for seasonally adjusted estimates and practical applications including automation of X12ARIMA options setting, machine learning techniques, and high frequency data analysis for border crossing counts. Explore the latest in time series methodology and applications in this insightful presentation.
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Presentation Transcript
Time Series Research at Statistics Canada Steve Matthews, Statistics Canada March 26th ONS Time Series Meetings
Current Research Variance Estimation for Seasonally Adjusted Estimates Bootstrap / Linearization/ Model-based State Space Models Use in benchmarking, raking, etc. Framework to compare Seasonal Adjustment Methods (ISF2018)
Practical Applications Automate setting of X12ARIMA options Machine learning techniques to supplement / replace heuristic rules Optimization approach - Define objective function to minimize w.r.t. parameters f (smoothness, residual seasonality, revisions, ) Adjusting short series Extend with back-casting (ARIMA as in X12ARIMA forecasting) Using more powerful models (ARIMA-X) to backcast Borrow strength across series? Across time? Regression Effects Trading Day Outliers (SAPW2018) Weather effects (Retail analysis paper to come)
High Frequency Data Daily border crossing counts Publish raw counts potential for seasonal adjustment, modelling and analysis Regressors for seasonality, outliers, weather, (exchange rate) Scanner data Short term: used to replace data collection mainly for data confrontation / validation / calendarization currently weekly but plan to investigate daily detail
