1. Alcohol Consumption Allyson Cady Dave Klotz Brandon DeMille Chris Ross

2. Data Total alcohol –Beer –Wine –Spirits Yearly data, from 1935-1999 Gallons of ethanol consumption, per capita

3. Alcohol Consumption Prohibition ended in 1935

4. Why alcohol consumption? Big industry with big money (approx. $70 billion in 1997, an increase of 17% from 5 years prior) Health issues Drunk driving and other alcohol related deaths We are college students We recently went through a period of war

5. WWIIVietnamPersian Gulf War-time Drinking

6. War-time Dummy Originally, we were planning to include a dummy variable to capture a wartime/non-wartime trend Although this kind of variable is useful in explaining the past, it doesn’t help with forecasting The dummy variable was left out of all models

7. Data is evolutionary To get rid of the evolutionary properties, –Take log –First difference  Results in percentage change in each period After doing this, the data becomes much more stationary

8. Modeling First attempts used ARMA techniques, but found that MA processes worked better Similar model found for all data sets

9. Model- Total Alcohol Dependent Variable: DLNALLALC Method: Least Squares Date: 05/26/03 Time: 19:26 Sample(adjusted): 1935 1999 Included observations: 65 after adjusting endpoints Convergence achieved after 30 iterations Backcast: 1923 1934 VariableCoefficientStd. Errort-StatisticProb. C0.0124230.0078741.5777890.1199 MA(1)0.2025830.1029551.9676860.0537 MA(4)0.2002710.0380705.2605780.0000 MA(9)0.5320560.03801313.996520.0000 MA(12)-0.3677950.077569-4.7415180.0000 R-squared 0.480532 Mean dependent var0.012668 Adjusted R-squared0.445900 S.D. dependent var0.054615 S.E. of regression0.040654 Akaike info criterion-3.493621 Sum squared resid0.099166 Schwarz criterion-3.326361 Log likelihood118.5427 F-statistic13.87567 Durbin-Watson stat1.510219 Prob(F-statistic)0.000000 Inverted MA Roots.83 -.42i.83+.42i.79.50 -.86i.50+.86i -.10 -.91i -.10+.91i -.43+.70i -.43 -.70i -.80+.57i -.80 -.57i -.99

10. Model- Total Alcohol

11. Model- Beer Dependent Variable: DLNBEER Method: Least Squares Date: 05/26/03 Time: 19:33 Sample(adjusted): 1935 1999 Included observations: 65 after adjusting endpoints Convergence achieved after 12 iterations Backcast: 1922 1934 VariableCoefficientStd. Errort-StatisticProb. C0.0108790.0051652.1063210.0393 MA(1)0.3380150.0571395.9156270.0000 MA(8)0.4820680.0735786.5517680.0000 MA(13)-0.3460070.000297-1163.4710.0000 R-squared 0.559543 Mean dependent var0.011038 Adjusted R-squared0.537881 S.D. dependent var0.042006 S.E. of regression0.028555 Akaike info criterion-4.214377 Sum squared resid0.049740 Schwarz criterion-4.080568 Log likelihood140.9672 F-statistic25.83085 Durbin-Watson stat1.867790 Prob(F-statistic)0.000000 Inverted MA Roots.85 -.42i.85+.42i.84.44+.78i.44 -.78i.14+.88i.14 -.88i -.39+.91i -.39 -.91i -.68 -.54i -.68+.54i -.95 -.29i -.95+.29i

12. Model- Beer

13. Model- Spirits Dependent Variable: DLNSP Method: Least Squares Date: 05/26/03 Time: 19:46 Sample(adjusted): 1935 1999 Included observations: 65 after adjusting endpoints Convergence achieved after 16 iterations Backcast: 1923 1934 VariableCoefficientStd. Errort-StatisticProb. C0.0119520.0138010.8659970.3899 MA(1)0.2104940.0394685.3332990.0000 MA(4)0.3611970.0514997.0136420.0000 MA(9)0.4564850.0678996.7230140.0000 MA(12)-0.3494410.096900-3.6061840.0006 R-squared0.529189 Mean dependent var0.012178 Adjusted R-squared0.497802 S.D. dependent var0.093814 S.E. of regression0.066482 Akaike info criterion-2.509955 Sum squared resid0.265195 Schwarz criterion-2.342694 Log likelihood86.57354 F-statistic16.85995 Durbin-Watson stat1.560509 Prob(F-statistic)0.000000 Inverted MA Roots.82+.44i.82 -.44i.79.51+.85i.51 -.85i -.09 -.89i -.09+.89i -.46 -.70i -.46+.70i -.80+.58i -.80 -.58i -.96

14. Model- Spirits

15. Model- Wine Dependent Variable: DLNWINE Method: Least Squares Date: 05/26/03 Time: 19:50 Sample(adjusted): 1935 1999 Included observations: 65 after adjusting endpoints Convergence achieved after 20 iterations Backcast: 1924 1934 VariableCoefficientStd. Errort-StatisticProb. C0.0229480.0138781.6536100.1033 MA(4)0.7119250.0004001781.7030.0000 MA(11)-0.2573020.057245-4.4947510.0000 R-squared 0.439531 Mean dependent var0.023382 Adjusted R-squared0.421451 S.D. dependent var0.100067 S.E. of regression0.076113 Akaike info criterion-2.268129 Sum squared resid0.359182 Schwarz criterion-2.167772 Log likelihood76.71418 F-statistic24.31080 Durbin-Watson stat2.309783 Prob(F-statistic)0.000000 Inverted MA Roots.81.75 -.59i.75+.59i.45 -.76i.45+.76i -.15 -.80i -.15+.80i -.67+.73i -.67 -.73i -.78 -.30i -.78+.30i

16. Model- Wine

17. Summary of Models Total Alcohol: C, MA(1), MA(4), MA(9), MA(12) Beer: C, MA(1), MA(8), MA(13) Spirits: C, MA(1), MA(4), MA(9), MA(12) Wine: C, MA(4), MA(11)

18. Forecast- All alcohol

19. Forecast- Beer

20. Forecast- Spirits

21. Forecast- Wine

22. Forecasts in Gallons per Capita

23. Forecast Results All forecasts show a similar pattern, with gradual increases expected in the future 1999 Consumption2010 ForecastExpected ChangeExpected Percent Change Total2.21 gallons2.52 gallons0.31 gallons14.0% Beer1.251.400.1512.0% Spirits0.640.730.0914.1% Wine0.320.440.1237.5%

24. Conclusions Americans are expected to increase their alcohol consumption by 14% over the period from 1999 to 2010 –Wine is expected to see the largest percentage increase –Beer is expected to see the largest absolute increase Increased consumption could lead to more difficulties with drunk driving, health issues, etc. –Awareness will become increasingly critical in the near future

25. The End