FIN 484 2 Exercises 1

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FIN 484 2 Exercises 1

DateBWL SalesDPI 

Jan-200321348251.3 1. Find the appropriate univariate model for the sale of Beer, Wine, and Liquor data (BWL Sales) and justify your answer using data from Jan-2003 to Dec-2006 

Feb-200320598268.1 Calculate the MAPE and RMSE for the data used to run the model and for the hold-up period (7 months)  

Mar-200322898317.6 2. Run a bivariate model using BWL Sales and DPI using data from Jan-2003 to Dec-2006  

Apr-200323488356.8 Compare the results to the univariate model in terms of RMSE and/or MAPE for the data used to run the model and for the hold-up period  

May-200325938412.0 3. Account for the trend and seasonality of BWL sales by adding a time index variable and dummies for months 2-12. Run this new model using data from Jan-2003 to Dec-2006

Jun-200324508449.6 Compare the results to the univariate model in terms of RMSE and/or MAPE for the data used to run the model and for the hold-up period  

Jul-200326298567.8  

Aug-200326788648.0  

Sep-200324788572.4  

Oct-200326598606.2  

Nov-200326788678.3  

Dec-200336818712.4  

Jan-200423088753.6  

Feb-200422328792.7  

Mar-200424118839.9  

Apr-200425678884.2  

May-200427028960.1  

Jun-200426608989.2  

Jul-200428869015.5  

Aug-200426419049.3  

Sep-200426399066.9  

Oct-200427539110.0  

Nov-200427929119.1  

Dec-200438439458.4  

Jan-200522549148.5  

Feb-200523559179.0  

Mar-200525769235.1  

Apr-200526919279.7  

May-200527679326.7  

Jun-200528469359.1  

Jul-200529959422.6  

Aug-200528659476.0  

Sep-200528619518.7  

Oct-200528849578.4  

Nov-200530259622.2  

Dec-200542679675.3  

Jan-200625629848.2  

Feb-200626679894.7  

Mar-200629189929.2  

Apr-200629639957.7  

May-200632079971.2  

Jun-2006325210017.0  

Jul-2006332210049.7  

Aug-2006322810079.7  

Sep-2006321210116.6  

Oct-2006312010147.8  

Nov-2006335910186.3  

Dec-2006458810254.7  

Jan-2007271010295.7  

Feb-2007274810356.6  

Mar-2007317610424.2  

Apr-2007303710442.3  

May-2007345910466.5  

Jun-2007357810476.0  

Jul-20073541

10515.3

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

QTRACCFUEL 1.QTR: Quarter (Quarters 1-29 = Control, Quarters 30-50 = Experimental)  

119232.592 2.ACC: Injuries and fatalities from Wednesday to Saturday nighttime accidents  

223837.25 3.FUEL: Fuel consumption (million gallons) in Albuquerque  

323240.032  

424635.852 The Police Department in Alburquerque, New Mexico introduced a van that housed a Blood Alcohol Testing (BAT) device to try to reduce DWI related accidents 

518538.226 This BATmobile was introduced in Quarter 30 of your data, so you have 29 observations before the program (Control) and 21 during the program (Experimental) 

627438.711 Part of your job is to decide whether or not the program was effective in reducing DWI related accidents  

726643.139  

819640.434 1. Using the number of accidents (ACC) and the fuel consumption (FUEL), calculate the average number of injuries before and after the program.  

917035.898 Does it look like the program was effective? Explain  

1023437.111 2. Run a multiple-regression model using ACC, FUEL, Quarter dummies (Q2, Q3, Q4), and a dummy variable for whether or not the program was in effect (BAT) 

1127238.944 Describe the results for the FUEL and quarter dummies and comment on the evidence related to the efficacy of the BATmobile program.  

1223437.717 3. Programs like the BATmobile usually take time to catch on (ramping up). To account for this, modify the BAT dummy variable so that the zero values remain unchanged but

1321037.861 the 1's are modified so that the new values are 1, 2, 3, 4, etc. Having this new variable, run the multiple-regression model and comment on whether or not there is  

1428042.524 evidence of a ramping up effect of the BATmobile program in reducing DWI related accidents.  

1524643.965  

1624841.976  

1726942.918  

1832649.789  

1934248.454  

2025745.056  

2128049.385  

2229042.524  

2335651.224  

2429548.562  

2527948.167  

2633051.362  

2735454.646  

2833153.398  

2929150.584  

3037751.32  

3132750.81  

3230146.272  

3326948.664  

3431448.122  

3531847.483  

3628844.732  

3724246.143  

3826844.129  

3932746.258  

4025348.23  

4121546.459  

4226350.686  

4331949.681  

4426351.029  

4520647.236  

4628651.717  

4732351.824  

4830649.38  

4923047.961  

5030446.039  

   

 

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