1. Economics 240C Forecasting US Retail Sales

2. Group 3

3. Motivations: Forecasting US retail sales for the remaining months of 2004 to provide indicative evidence to explore George W. Bush’s claim of economic recovery. Forecasting US retail sales for the remaining months of 2004 to provide indicative evidence to explore George W. Bush’s claim of economic recovery.

4. Introduction: Retail Sales as an economic indicator: Retail Sales as an economic indicator: Consumer Confidence Consumer Confidence General Economic Performance General Economic Performance Business Cycle ‘Turns’ Business Cycle ‘Turns’

5. Data: Real Retail and Food Service data obtained from Fred II Real Retail and Food Service data obtained from Fred II Monthly Time Series from January 1992 to April 2004. Monthly Time Series from January 1992 to April 2004. Seasonally Adjusted Seasonally Adjusted

6. Initial Observations: Evolutionary Series Evolutionary Series Trend in mean Trend in mean Unit Root Test : 120000 130000 140000 150000 160000 170000 180000 92939495969798990001020304 SALES ADF Test Statistic-0.093053 10% Critical Value-2.6745 0 5 10 15 20 25 120000130000140000150000160001070000180000 Series: SALES Sample 1992:01 2004:04 Observations 148 Mean 148619.1 Median 146538.5 Maximum 178098.0 Minimum 120607.0 Std. Dev. 16095.97 Skewness-0.097460 Kurtosis 1.767251 Jarque-Bera 9.605597 Probability 0.008207

7. Stationarity Difference Data into fractional change to achieve stationarity: Dsales = Sales – Sales(-1) Difference Data into fractional change to achieve stationarity: Dsales = Sales – Sales(-1) -10000 -5000 0 5000 10000 15000 92939495969798990001020304 DSALES 0 10 20 30 40 -5000-2500025005000750010000 Series: DSALES Sample 1992:02 2004:04 Observations 147 Mean 376.5374 Median 368.0000 Maximum 10945.00 Minimum-5031.000 Std. Dev. 1473.569 Skewness 1.920182 Kurtosis 20.60472 Jarque-Bera 1988.632 Probability 0.000000

8. Correlogram and Root Test

9. September 11th Large Spike in series between September and October 2001 Large Spike in series between September and October 2001 Intervention Model needed for this one off event. Intervention Model needed for this one off event. -10000 -5000 0 5000 10000 15000 92939495969798990001020304 DSALES After achieving an approximately stationary series:

10. Intervention model We use time dummies to model the pulse function for the September 11 th 2001 event. We put in dummies for the months of September, October, November of 2001. We use time dummies to model the pulse function for the September 11 th 2001 event. We put in dummies for the months of September, October, November of 2001. So our model becomes an intervention model. So our model becomes an intervention model.

11. Model Estimation I

12. Model I Residuals 0 5 10 15 20 -2000-1000010002000 Series: Residuals Sample 1992:09 2004:04 Observations 140 Mean-15.26165 Median 48.56197 Maximum 2377.545 Minimum-2676.280 Std. Dev. 897.8525 Skewness-0.405495 Kurtosis 3.428258 Jarque-Bera 4.906470 Probability 0.086015

13. Model Estimation II

14. Model II Residual Comparing the residuals of model II with those of model I, there is an improvement in the Jarque-Bera statistic. This shows that the ARCH/GARCH improves the model. Comparing the residuals of model II with those of model I, there is an improvement in the Jarque-Bera statistic. This shows that the ARCH/GARCH improves the model.

15. Diagnostic of the model The actual-fitted residual graph shows that the model tracks the observations well, especially the large spike due to the 9/11/2001 event. The actual-fitted residual graph shows that the model tracks the observations well, especially the large spike due to the 9/11/2001 event.

16. Note: The pulse function is highly significant The pulse function is highly significant Equation as a whole highly significant Equation as a whole highly significant Variance Term Arch(1) included to improve Kurtosis of model’s residuals Variance Term Arch(1) included to improve Kurtosis of model’s residuals Lack of structure or significant spikes in residual correlogram Lack of structure or significant spikes in residual correlogram

17. Forecast of dsales for 2004:05 to 2004:12

18. Forecast of retail sales

19. Conclusion Our forecast shows the difference in retail sales is not going to fluctuate very much in the rest of 2004. Our forecast shows the difference in retail sales is not going to fluctuate very much in the rest of 2004. The absolute value of retail sales will continue its upward trend. The absolute value of retail sales will continue its upward trend. Whether it signals economic recovery is rather unclear and it will make President Bush reelection campaign less certain too. Whether it signals economic recovery is rather unclear and it will make President Bush reelection campaign less certain too.