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Alcohol Consumption

Alcohol Consumption. Allyson Cady Dave Klotz Brandon DeMille Chris Ross. Data. Total alcohol Beer Wine Spirits Yearly data, from 1935-1999 Gallons of ethanol consumption, per capita. Alcohol Consumption. Prohibition ended in 1935. Why alcohol consumption?.

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Alcohol Consumption

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  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. War-time Drinking WWII Vietnam Persian Gulf

  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 Variable Coefficient Std. Error t-Statistic Prob. C 0.012423 0.007874 1.577789 0.1199 MA(1) 0.202583 0.102955 1.967686 0.0537 MA(4) 0.200271 0.038070 5.260578 0.0000 MA(9) 0.532056 0.038013 13.99652 0.0000 MA(12) -0.367795 0.077569 -4.741518 0.0000 R-squared 0.480532 Mean dependent var 0.012668 Adjusted R-squared 0.445900 S.D. dependent var 0.054615 S.E. of regression 0.040654 Akaike info criterion -3.493621 Sum squared resid 0.099166 Schwarz criterion -3.326361 Log likelihood 118.5427 F-statistic 13.87567 Durbin-Watson stat 1.510219 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 Variable Coefficient Std. Error t-Statistic Prob. C 0.010879 0.005165 2.106321 0.0393 MA(1) 0.338015 0.057139 5.915627 0.0000 MA(8) 0.482068 0.073578 6.551768 0.0000 MA(13) -0.346007 0.000297 -1163.471 0.0000 R-squared 0.559543 Mean dependent var 0.011038 Adjusted R-squared 0.537881 S.D. dependent var 0.042006 S.E. of regression 0.028555 Akaike info criterion -4.214377 Sum squared resid 0.049740 Schwarz criterion -4.080568 Log likelihood 140.9672 F-statistic 25.83085 Durbin-Watson stat 1.867790 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 Variable Coefficient Std. Error t-Statistic Prob. C 0.011952 0.013801 0.865997 0.3899 MA(1) 0.210494 0.039468 5.333299 0.0000 MA(4) 0.361197 0.051499 7.013642 0.0000 MA(9) 0.456485 0.067899 6.723014 0.0000 MA(12) -0.349441 0.096900 -3.606184 0.0006 R-squared 0.529189 Mean dependent var 0.012178 Adjusted R-squared 0.497802 S.D. dependent var 0.093814 S.E. of regression 0.066482 Akaike info criterion -2.509955 Sum squared resid 0.265195 Schwarz criterion -2.342694 Log likelihood 86.57354 F-statistic 16.85995 Durbin-Watson stat 1.560509 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 Variable Coefficient Std. Error t-Statistic Prob. C 0.022948 0.013878 1.653610 0.1033 MA(4) 0.711925 0.000400 1781.703 0.0000 MA(11) -0.257302 0.057245 -4.494751 0.0000 R-squared 0.439531 Mean dependent var 0.023382 Adjusted R-squared 0.421451 S.D. dependent var 0.100067 S.E. of regression 0.076113 Akaike info criterion -2.268129 Sum squared resid 0.359182 Schwarz criterion -2.167772 Log likelihood 76.71418 F-statistic 24.31080 Durbin-Watson stat 2.309783 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

  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

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