1. Issues in Modeling and Adjusting Calendar Effects in Economic Time Series Brian C. Monsell U. S. Census Bureau ICES III June 2007 U S C E N S U S B U R E A U

2. 2 Acknowledgments David Findley Bill Bell Patrick Cantwell Monica Wroblewski

3. 3 Outline Alternate Easter Regressors »Define Regressors »Model Diagnostics Used »Results on Retail Sales Series One-Coefficient Stock Trading Day »Define Regressor »Results on Industrial Inventory Series

4. 4 Alternate Easter Regressors Easter is the moving holiday that has the broadest effect in U. S. economic series »Sales increase before Easter Holiday Determine if we can improve the Easter effect model

5. 5 Basic Easter[w] Regressor E w,t 0 only in Feb, Mar, and April

6. 6 Alternate 2-Part Easter Regressor Assumes there are two effects: »Pre-Easter effect before Good Friday »Easter duration effect from Good Friday to Easter Inspired by Zhang, McLaren, Leung (2003)

7. 7 Pre-Easter Regressor PE w,t 0 only in Feb, Mar, and April

8. 8 Easter Duration Regressor ED t 0 only in March and April

9. 9 Linear Easter Regressor Assumes change in level begins w days before Easter This change increases linearly until the day before Easter Inspired by Zhang, McLaren, Leung (2003)

10. 10 Linear Easter[w] Let m y be the number of the w days before Easter that occur in March of year y.

11. 11 Weekend-Weekday Easter Assumes that the change in the level of the series is different for »Weekend days (Friday, Saturday, Sunday) »Weekdays (Monday through Thursday Construct two regressors for the proportion of weekdays and weekend days in the 16 days before Easter in each month

12. 12

13. 13 Evaluation Methods AICC Mean Sum of Squared Out of Sample Forecast Error

14. 14 AICC A standard model comparison diagnostic »Based on Akaikes Information Criteria »Smaller values are preferred

15. 15 AICC AIC Corrected (for sample size) Note: As N gets larger, AICC approaches the AIC

16. 16 Mean Squared Forecast Error

17. 17 Application to Retail Sales Series Sixteen (16) retail sales series »A regARIMA model with the Easter[8] regressor is used to generate Easter adjustment factors »January 1992 – November 2006 »Model span begins in 1995

18. 18 Current Easter Regressors For each series, compare AICC for 4 Easter Models »No Easter »Easter[1] »Easter[8] »Easter[15]

19. 19 Results of Current Easter For all series, a model with one of the Easter regressors was preferred »Easter[8] regressor was preferred in 11 of the 16 series »Difference in the AICC was substantial, ranging from 6.3 to 95.5

20. 20 Alternate Easter Regressors The alternate Easter regressors will be fit with the regARIMA model that differs from those used for production runs only in the Easter model Fit models with: »Two-Part Easter, w = 8, 15 »Linear Easter, w = 8, 15 »Weekend-Weekday Easter Regressor

21. 21 For 2-Part Easter: Only one series (Miscellaneous Food and Beverage) preferred the best 2- Part Easter model over the best current Easter model

22. 22 For Linear Easter Regressor: 11 series preferred the best linear Easter model over the best current Easter »The AICC differences were very small (only two > 1), implying there is not much difference between the models

23. 23 For Weekend-Weekday Easter: For 8 of the 16 series, the best model overall according to AICC was the Weekend-Weekday Easter model AICC differences indicate that this model should be preferred to the current Easter[8] model (range from 3 to 15) for these 8 series.

24. 24 Forecast Error Use forecast error diagnostic to determine if the model with the best AICC »Forecasts better than a model with the current Easter[8] regressor »Forecasts better than a model with no Easter Reduce out of sample forecast error using the alternate Easter models for both 1 and 12 step ahead forecasts

25. 25 Best vs. Easter[8] 9 of the 16 series had smaller forecast errors for both 1 and 12 step ahead forecasts using the best alternate model over the Easter[8]

26. 26 Best vs. No Easter Only 7 of the 16 series showed forecast improvement using the best alternate model over using no Easter regressor

27. 27 Only one of the series using the Weekend-Weekday regressor showed forecast improvement over the model with no Easter

28. 28 Constrained Stock Trading Day Findley (2006) developed formulas relating flow and stock day-of-week coefficients. Now possible to apply flow day-of week constraints to stock trading day regressors. More details in Findley and Monsell (2007)

29. 29 End of Month Stock Trading Day I* t (1) = 1 when the last day of the month is Monday, -1 when the last day is Sunday, and 0 otherwise I* t (2) = 1 when the last day of the month is Tuesday, -1 when the last day is Sunday, and 0 otherwise Etc. Bell (1984,1995)

30. 30 Constraint for 1-Coefficient Flow Trading Day Weekday/Weekend contrast model of TRAMO and X-12-ARIMA Imposes separate equality constraints on weekday and weekend coefficients

31. 31 One Coefficient Flow Trading Day D jt = number of days of type j in month t j = 1 (Mon), 2 (Tues),..., 7 (Sun)

32. 32 Stock TD 1-coefficient Regressor

33. 33 Manufacturing Inventory Series 91 inventory series of the U. S. Census Bureaus monthly Manufacturers Shipments, Inventories and Orders Survey. Series ended in October 2006 Starting date for models range from January 1992 to January 1995

34. 34 Models fit for Inventory Series Using the regARIMA model used in production as a base, models were fit with »No trading day regressors »Unconstrained stock trading day with ω = 31 »Constrained stock trading day with ω = 31

35. 35 Model Comparisons Used a standard log-likelihood difference asymptotic chi-square test, with a level of significance α = 0.05 »see Taniguchi and Kakizawa (2000)

36. 36 Log-likelihood Test Results Found 21 series where the model with no trading day was rejected in favor of a model with stock trading day

37. 37 For one of the series, the trading day model was rejected for inducing a visually significant trading day peak in the seasonally adjusted series. Rejected another series due to better forecasting performance without trading day

38. 38 Series Breakdown Type of Trading Day found significant Number of Series Unconstrained Stock TD 3 Constrained Stock TD8 Both Constrained and Unconstrained Stock TD 8

39. 39 Where Both TD models are significant Constrained Stock TD always preferred over the Unconstrained Stock TD using an appropriate chi-square test

40. 40 However Preferred Unconstrained Stock TD for two of the series where the use of the Constrained Stock TD left a visually significant spectral peak in the regARIMA residuals

41. 41 Summary: For 14 of the 19 series where a stock trading day regressor was found to be significant, the Constrained Stock TD was the preferred model

42. 42 For 12 of these 14 series, additional criteria were found to support this choice, including »Reducing visually significant spectral peaks »Reducing the number of lags with significant Ljung-Box statistics »Reduction in out-of sample forecast error

43. 43 Future Work Further examine the forecasting results for the Easter regressors Apply constrained stock trading day model to retail inventories Incorporate one-coefficient stock trading day into X-13A-S »Currently a utility to generate these variables from the X-12 regression matrix

44. 44 Contact Information Brian Monsell– brian.c.monsell@census.gov brian.c.monsell@census.gov

45. 45 Disclaimer This report is released to inform interested parties of ongoing research and to encourage discussion of work in progress. Any views expressed on statistical, methodological, technical, or operational issues are those of the authors and not necessarily those of the U.S. Census Bureau.

46. 46 Much of the data analysis for this paper was generated using Base SAS® software, SAS/AF® software, and SAS/GRAPH® software, Versions 8 and 9 of the SAS System for Windows. Copyright © 1999- 2003 SAS Institute Inc. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc., Cary, NC, USA.