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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?. - PowerPoint PPT Presentation


  • Alcohol ConsumptionAllyson CadyDave KlotzBrandon DeMilleChris Ross

  • DataTotal alcoholBeerWineSpirits

    Yearly data, from 1935-1999Gallons of ethanol consumption, per capita

  • Alcohol ConsumptionProhibition 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 issuesDrunk driving and other alcohol related deathsWe are college studentsWe recently went through a period of war

  • War-time Drinking

  • War-time DummyOriginally, we were planning to include a dummy variable to capture a wartime/non-wartime trendAlthough this kind of variable is useful in explaining the past, it doesnt help with forecastingThe dummy variable was left out of all models

  • Data is evolutionaryTo get rid of the evolutionary properties,Take logFirst differenceResults in percentage change in each period

    After doing this, the data becomes much more stationary

  • ModelingFirst attempts used ARMA techniques, but found that MA processes worked betterSimilar model found for all data sets

  • Model- Total AlcoholDependent Variable: DLNALLALCMethod: Least SquaresDate: 05/26/03 Time: 19:26Sample(adjusted): 1935 1999Included observations: 65 after adjusting endpointsConvergence achieved after 30 iterationsBackcast: 1923 1934VariableCoefficientStd. Errort-StatisticProb. C0.0124230.0078741.5777890.1199MA(1)0.2025830.1029551.9676860.0537MA(4)0.2002710.0380705.2605780.0000MA(9)0.5320560.03801313.996520.0000MA(12)-0.3677950.077569-4.7415180.0000R-squared 0.480532 Mean dependent var0.012668Adjusted R-squared0.445900 S.D. dependent var0.054615S.E. of regression0.040654 Akaike info criterion-3.493621Sum squared resid0.099166 Schwarz criterion-3.326361Log likelihood118.5427 F-statistic13.87567Durbin-Watson stat1.510219 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

  • Model- Total Alcohol

  • Model- BeerDependent Variable: DLNBEERMethod: Least SquaresDate: 05/26/03 Time: 19:33Sample(adjusted): 1935 1999Included observations: 65 after adjusting endpointsConvergence achieved after 12 iterationsBackcast: 1922 1934VariableCoefficientStd. Errort-StatisticProb. C0.0108790.0051652.1063210.0393MA(1)0.3380150.0571395.9156270.0000MA(8)0.4820680.0735786.5517680.0000MA(13)-0.3460070.000297-1163.4710.0000R-squared 0.559543 Mean dependent var0.011038Adjusted R-squared0.537881 S.D. dependent var0.042006S.E. of regression0.028555 Akaike info criterion-4.214377Sum squared resid0.049740 Schwarz criterion-4.080568Log likelihood140.9672 F-statistic25.83085Durbin-Watson stat1.867790 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

  • Model- Beer

  • Model- SpiritsDependent Variable: DLNSPMethod: Least SquaresDate: 05/26/03 Time: 19:46Sample(adjusted): 1935 1999Included observations: 65 after adjusting endpointsConvergence achieved after 16 iterationsBackcast: 1923 1934VariableCoefficientStd. Errort-StatisticProb. C0.0119520.0138010.8659970.3899MA(1)0.2104940.0394685.3332990.0000MA(4)0.3611970.0514997.0136420.0000MA(9)0.4564850.0678996.7230140.0000MA(12)-0.3494410.096900-3.6061840.0006R-squared0.529189 Mean dependent var0.012178Adjusted R-squared0.497802 S.D. dependent var0.093814S.E. of regression0.066482 Akaike info criterion-2.509955Sum squared resid0.265195 Schwarz criterion-2.342694Log likelihood86.57354 F-statistic16.85995Durbin-Watson stat1.560509 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

  • Model- WineDependent Variable: DLNWINEMethod: Least SquaresDate: 05/26/03 Time: 19:50Sample(adjusted): 1935 1999Included observations: 65 after adjusting endpointsConvergence achieved after 20 iterationsBackcast: 1924 1934VariableCoefficientStd. Errort-StatisticProb. C0.0229480.0138781.6536100.1033MA(4)0.7119250.0004001781.7030.0000MA(11)-0.2573020.057245-4.4947510.0000

    R-squared 0.439531 Mean dependent var0.023382Adjusted R-squared0.421451 S.D. dependent var0.100067S.E. of regression0.076113 Akaike info criterion-2.268129Sum squared resid0.359182 Schwarz criterion-2.167772Log likelihood76.71418 F-statistic24.31080Durbin-Watson stat2.309783 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

  • Summary of ModelsTotal 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

  • Forecast- Beer

  • Forecast- Spirits

  • Forecast- Wine

  • Forecasts in Gallons per Capita

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

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

  • The End


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