Alcohol Consumption

Allyson CadyDave KlotzBrandon DeMilleChris Ross

Data

• Total alcohol– Beer– Wine– Spirits

• Yearly data, from 1935-1999• Gallons of ethanol consumption,

per capita

Alcohol Consumption

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ALLALCOHOLBEER

WINESPIRITS

Prohibition ended in 1935

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

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WINESPIRITS

WWII Vietnam Persian Gulf

War-time Drinking

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

Data is evolutionary

• To get rid of the evolutionary properties,– Take log– First differenceResults in percentage change in each period

After doing this, the data becomes much more stationary

Modeling

• First attempts used ARMA techniques, but found that MA processes worked better

• Similar model found for all data sets

Model- Total Alcohol

Dependent Variable: DLNALLALCMethod: Least SquaresDate: 05/26/03 Time: 19:26Sample(adjusted): 1935 1999Included observations: 65 after adjusting endpoints

Convergence achieved after 30 iterationsBackcast: 1923 1934Variable Coefficient Std. Error t-Statistic Prob. C 0.012423 0.007874 1.577789 0.1199MA(1) 0.202583 0.102955 1.967686 0.0537MA(4) 0.200271 0.038070 5.260578 0.0000MA(9) 0.532056 0.038013 13.99652 0.0000MA(12) -0.367795 0.077569 -4.741518 0.0000R-squared 0.480532 Mean dependent var 0.012668Adjusted R-squared 0.445900 S.D. dependent var 0.054615S.E. of regression 0.040654 Akaike info criterion -3.493621Sum squared resid 0.099166 Schwarz criterion -3.326361Log likelihood 118.5427 F-statistic 13.87567Durbin-Watson stat 1.510219 Prob(F-statistic) 0.000000Inverted MA Roots .83 -.42i .83+.42i .79 .50 -.86i

.50+.86i -.10 -.91i -.10+.91i -.43+.70i -.43 -.70i -.80+.57i -.80 -.57i -.99

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Residual Actual Fitted

Model- Total Alcohol

Model- BeerDependent Variable: DLNBEERMethod: Least SquaresDate: 05/26/03 Time: 19:33Sample(adjusted): 1935 1999Included observations: 65 after adjusting endpoints

Convergence achieved after 12 iterationsBackcast: 1922 1934Variable Coefficient Std. Error t-Statistic Prob. C 0.010879 0.005165 2.106321 0.0393MA(1) 0.338015 0.057139 5.915627 0.0000MA(8) 0.482068 0.073578 6.551768 0.0000MA(13) -0.346007 0.000297 -1163.471 0.0000R-squared 0.559543 Mean dependent var 0.011038Adjusted R-squared 0.537881 S.D. dependent var 0.042006S.E. of regression 0.028555 Akaike info criterion -4.214377Sum squared resid 0.049740 Schwarz criterion -4.080568Log likelihood 140.9672 F-statistic 25.83085Durbin-Watson stat 1.867790 Prob(F-statistic) 0.000000Inverted 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

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Residual Actual Fitted

Model- Beer

Model- SpiritsDependent Variable: DLNSPMethod: Least SquaresDate: 05/26/03 Time: 19:46Sample(adjusted): 1935 1999Included observations: 65 after adjusting endpoints

Convergence achieved after 16 iterationsBackcast: 1923 1934Variable Coefficient Std. Error t-Statistic Prob. C 0.011952 0.013801 0.865997 0.3899MA(1) 0.210494 0.039468 5.333299 0.0000MA(4) 0.361197 0.051499 7.013642 0.0000MA(9) 0.456485 0.067899 6.723014 0.0000MA(12) -0.349441 0.096900 -3.606184 0.0006R-squared 0.529189 Mean dependent var 0.012178Adjusted R-squared 0.497802 S.D. dependent var 0.093814S.E. of regression 0.066482 Akaike info criterion -2.509955Sum squared resid 0.265195 Schwarz criterion -2.342694Log likelihood 86.57354 F-statistic 16.85995Durbin-Watson stat 1.560509 Prob(F-statistic) 0.000000Inverted MA Roots .82+.44i .82 -.44i .79 .51+.85i

.51 -.85i -.09 -.89i -.09+.89i -.46 -.70i -.46+.70i -.80+.58i -.80 -.58i -.96

Model- Spirits

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Residual Actual Fitted

Model- WineDependent Variable: DLNWINEMethod: Least SquaresDate: 05/26/03 Time: 19:50Sample(adjusted): 1935 1999Included observations: 65 after adjusting endpoints

Convergence achieved after 20 iterationsBackcast: 1924 1934Variable Coefficient Std. Error t-Statistic Prob. C 0.022948 0.013878 1.653610 0.1033MA(4) 0.711925 0.000400 1781.703 0.0000MA(11) -0.257302 0.057245 -4.494751 0.0000

R-squared 0.439531 Mean dependent var 0.023382Adjusted R-squared 0.421451 S.D. dependent var 0.100067S.E. of regression 0.076113 Akaike info criterion -2.268129Sum squared resid 0.359182 Schwarz criterion -2.167772Log likelihood 76.71418 F-statistic 24.31080Durbin-Watson stat 2.309783 Prob(F-statistic) 0.000000Inverted MA Roots .81 .75 -.59i .75+.59i .45 -.76i

.45+.76i -.15 -.80i -.15+.80i -.67+.73i -.67 -.73i -.78 -.30i -.78+.30i

Model- Wine

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Residual Actual Fitted

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)

Forecast- All alcohol

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ALLTIMETOTAL

Forecast- Beer

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ALLTIMEBEER

Forecast- Spirits

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ALLTIMESPIRITS

Forecast- Wine

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ALLTIMEWINE

Forecasts in Gallons per Capita

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ALLALCOHOLBEER

SPIRITSWINE

Forecast Results

• All forecasts show a similar pattern, with gradual increases expected in the future

  1999 Consumption 2010 Forecast Expected Change Expected Percent Change

Total 2.21 gallons 2.52 gallons 0.31 gallons 14.0%

Beer 1.25 1.40 0.15 12.0%

Spirits 0.64 0.73 0.09 14.1%

Wine 0.32 0.44 0.12 37.5%

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

The End