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Development of a Macro Editing Approach

Development of a Macro Editing Approach. Work Session on Statistical Data Editing, Topic v: Editing based on results 21-23 April 2008 WP 30. Overview. Introduction to series of surveys that measures U.S. petroleum product supplied

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Development of a Macro Editing Approach

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  1. Development of a Macro Editing Approach Work Session on Statistical Data Editing, Topic v: Editing based on results 21-23 April 2008 WP 30

  2. Overview • Introduction to series of surveys that measures U.S. petroleum product supplied • Limitation of micro editing and need for an edit approach at the aggregate level • Approach considered for macro editing and the three types of models developed using one product as an example • In sample forecast results and out-of-sample forecast performance results • Summary and conclusions

  3. The PSRS and Micro Edit Limitations • The surveys, respondents and data collected • WPSRS: Weekly, six cut-off sample surveys • MPSRS : Monthly, nine population census surveys • PSA: Annual of revised monthly estimates, population census • Limitations • Variability of responses • Lagged population coverage • Corrective Measures • Micro editing • Imputation

  4. The Approach • Purpose of Study • Develop point and interval forecast at national and regional levels • One-month ahead forecast • Approach • Econometric time-series models • Three models : Base, ARMA, and Supplemental Models • Micro editing enhanced by providing capabilities to identify outliers at the aggregate level

  5. Model Development • Model at product level • Distillate (Low Sulfur, High Sulfur, Total) • Gasoline • Model at two geographic levels • National • Regional (PADD)

  6. Model Forms • Base Model: trends and seasonal factors expressed as: • ARMA Model: Box-Jenkins approach utilizing AR and MA to capture the variation and seasonal pattern expressed as: • Supplemental Model: Base Model with exogenous variables expressed as:

  7. US Distillate Demand: 1996-2006

  8. US Distillate Demand: 1996-2006

  9. US Distillate Demand: 1996-2006

  10. In-Sample One-Month-Out Forecast Evaluation Statistics Note: There is no evidence of bias in any of the models

  11. U.S. Distillate DemandBest Model Summary Statistics Note: Estimation period Jan 1996 through Dec 2006

  12. In-Sample Model Fit: Best Model 2000-2006( 2 forecast standard errors)

  13. In-Sample Model Fit: Best Model 2000-2006( 2 forecast standard errors)

  14. In-Sample Model Fit: Best Model 2000-2006( 2 forecast standard errors)

  15. Out-of-Sample Forecast Results: Best Model 2006-2007

  16. Out-of-Sample Forecast Results: Best Model 2006-2007, HSD

  17. Out-of-Sample Results: Best Model 2006-2007, LSD

  18. Regional Models • Regions: Petroleum Administration for Defense District • Identify exogenous variables to explain regional patterns of distillate demand • Residential heating in the Northeast (PADD 1): Heating Degree-Days • Agriculture in the Midwest (PADD 2): Precipitation HDD DEV Population-Weighted Heating Degree-Days: Deviation from Normal PRECIP DEV Area-Weighted Precipitation: Deviation from Long-Term Normal EMP TRANS Employment in Transportation Industries IPI MFG Index of Industrial Production for Durable Goods FREIGHT INDX Transportation Services Index for Freight PRICE RATIO Average monthly spot price ratio: No.2 Fuel Oil / Natural Gas

  19. Regional Model Details: In-Sample Model Fit, PADD 1 HSD

  20. Regional Model Details: In-Sample Model Fit, PADD 1, LSD

  21. Regional Model Details: In-Sample Model Fit, PADD 2, HSD

  22. Regional Model Details: In-Sample Model Fit, PADD 2, LSD

  23. Regional Model Details: Out-of-Sample Forecast Results, PADD 1, HSD

  24. Regional Model Details: Out-of-Sample Forecast Results, PADD 1, LSD

  25. Regional Model Details: Out-of-Sample Forecast Results, PADD 2, HSD

  26. Regional Model Details: Out-of-Sample Forecast Results, PADD 2, LSD

  27. Benefits & Limitations • How does this improve EIA’s current activities? • Establishes a range of expected results at the aggregate level that will alert a reviewer when to investigate possible anomalies in the respondent data • Can identify the region which provides largest contribution to deviation, guiding further editing and imputation activities prior to data release • Reduces risk of revisions to released data • Limitations of Modeling • Reasons for deviations are not always readily apparent: respondent error, structural shifts in consumption, or failure of the model to respond to external influences • Regional-level models provide guidance, but not necessarily answers • Ranges may be too large

  28. Future Plans • Model improvements • Dynamic adjustments to known issues like shifts • Better exogenous variables • Automation of gathering and formatting model inputs • Weather Data • Economic Data • Forecast generation • Expand to other key petroleum products • Gasoline and gasoline subcomponents (currently underway) • Residual fuel oil

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