FIN 484 2 Exercises 1

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Exercise 1

Date BWL Sales
Barry Keating: Series Title: US Retail Sales: Beer, wine and liquor stores: NAICS 4453: NSA: Millions of dollars 		DPI
Jan-2003 2134 8251.3 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-2003 2059 8268.1 Calculate the MAPE and RMSE for the data used to run the model and for the hold-up period (7 months)
Mar-2003 2289 8317.6 2. Run a bivariate model using BWL Sales and DPI using data from Jan-2003 to Dec-2006
Apr-2003 2348 8356.8 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-2003 2593 8412.0 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-2003 2450 8449.6 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-2003 2629 8567.8
Aug-2003 2678 8648.0
Sep-2003 2478 8572.4
Oct-2003 2659 8606.2
Nov-2003 2678 8678.3
Dec-2003 3681 8712.4
Jan-2004 2308 8753.6
Feb-2004 2232 8792.7
Mar-2004 2411 8839.9
Apr-2004 2567 8884.2
May-2004 2702 8960.1
Jun-2004 2660 8989.2
Jul-2004 2886 9015.5
Aug-2004 2641 9049.3
Sep-2004 2639 9066.9
Oct-2004 2753 9110.0
Nov-2004 2792 9119.1
Dec-2004 3843 9458.4
Jan-2005 2254 9148.5
Feb-2005 2355 9179.0
Mar-2005 2576 9235.1
Apr-2005 2691 9279.7
May-2005 2767 9326.7
Jun-2005 2846 9359.1
Jul-2005 2995 9422.6
Aug-2005 2865 9476.0
Sep-2005 2861 9518.7
Oct-2005 2884 9578.4
Nov-2005 3025 9622.2
Dec-2005 4267 9675.3
Jan-2006 2562 9848.2
Feb-2006 2667 9894.7
Mar-2006 2918 9929.2
Apr-2006 2963 9957.7
May-2006 3207 9971.2
Jun-2006 3252 10017.0
Jul-2006 3322 10049.7
Aug-2006 3228 10079.7
Sep-2006 3212 10116.6
Oct-2006 3120 10147.8
Nov-2006 3359 10186.3
Dec-2006 4588 10254.7
Jan-2007 2710 10295.7
Feb-2007 2748 10356.6
Mar-2007 3176 10424.2
Apr-2007 3037 10442.3
May-2007 3459 10466.5
Jun-2007 3578 10476.0
Jul-2007 3541 10515.3

Exercise 2

QTR ACC FUEL 1.QTR: Quarter (Quarters 1-29 = Control, Quarters 30-50 = Experimental)
1 192 32.592 2.ACC: Injuries and fatalities from Wednesday to Saturday nighttime accidents
2 238 37.25 3.FUEL: Fuel consumption (million gallons) in Albuquerque
3 232 40.032
4 246 35.852 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
5 185 38.226 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)
6 274 38.711 Part of your job is to decide whether or not the program was effective in reducing DWI related accidents
7 266 43.139
8 196 40.434 1. Using the number of accidents (ACC) and the fuel consumption (FUEL), calculate the average number of injuries before and after the program.
9 170 35.898 Does it look like the program was effective? Explain
10 234 37.111 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)
11 272 38.944 Describe the results for the FUEL and quarter dummies and comment on the evidence related to the efficacy of the BATmobile program.
12 234 37.717 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
13 210 37.861 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
14 280 42.524 evidence of a ramping up effect of the BATmobile program in reducing DWI related accidents.
15 246 43.965
16 248 41.976
17 269 42.918
18 326 49.789
19 342 48.454
20 257 45.056
21 280 49.385
22 290 42.524
23 356 51.224
24 295 48.562
25 279 48.167
26 330 51.362
27 354 54.646
28 331 53.398
29 291 50.584
30 377 51.32
31 327 50.81
32 301 46.272
33 269 48.664
34 314 48.122
35 318 47.483
36 288 44.732
37 242 46.143
38 268 44.129
39 327 46.258
40 253 48.23
41 215 46.459
42 263 50.686
43 319 49.681
44 263 51.029
45 206 47.236
46 286 51.717
47 323 51.824
48 306 49.38
49 230 47.961
50 304 46.039