Transcript Automobile Sales and the General Economy ECON240A

Automobile Sales and the
General Economy
ECON240A
Group #1
Deepti Goyal
Rory Tyler Hofstatter
Hairuo Hu
Joel Benjamin Lindenberg
Sooyeon Angela Shin
Michael John Stromberg
Kathy Zha
Ling Zhu
Introduction

Dependent Variable
◦ Amount of Auto Sales

Independent Variables
◦ Unemployment
◦ Price of Oil
◦ Average Mileage per Gallon
◦ Income per Capita

Trend towards vehicles with better fuel
efficiency

Automobile sales have been decreasing,
particularly for bigger vehicles notably in
the past couple of years

Impact of current recession on the auto
sales industry
Why Study Such Variables?
120.00
others
Hyundai
Honda
Nissan
Toyota
Volksw agen
GM
100.00
80.00
60.00
40.00
Ford
Daimler
Chrysler
American Motors
20.00
Auto Sales by Make
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
1978
1976
1974
1972
0.00
Trucks vs. Cars
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
18000
1982
1980
1978
1976
1974
year
20000
Trucks
Cars
16000
14000
12000
10000
8000
6000
4000
2000
0

Exploratory Data Analysis
◦ Histograms
◦ Box Plots
◦ Scatter Diagram
◦ Time Series Trend

Regression Analysis
◦ Correlation Diagram
◦ Bi-variate Regression using OLS method
◦ Normality test using Jarque-Bera Statistics
◦ Heteroskedasticity
How the Study is Conducted
Auto Sales: Ward’s Automotive Group
 Unemployment Rate:
US Bureau of Labor Statistics
 Annual Crude Oil Prices:
US Bureau of Labor Statistics
 Income per Capita:
US Department of Commerce,
Bureau of Economic Analysis
 35 Years (1974-2008)

Data Gathering
Histogram of AVGMPG
Histogram of INCOME
10000
12000
14000
AUTO
16000
18000
6
Frequency
3
4
0
0
0
2
1
2
4
2
Frequency
6
8
Frequency
4
6
10
5
12
8
14
Histogram of AUTO
18
22
24
26
AVGMPG
28
30
4
0
0
2
5
Frequency
Frequency
6
8
10
10
12
Histogram of UNEMP
15
Histogram of OIL
20
0
20
40
60
80
100
4
5
6
7
8
UNEMP
9
10
Variable Histograms
OIL
10000
20000 30000
INCOME
40000
AVGMPG
Boxplot of
INCOME
5000 15000 25000 35000
20 22 24 26 28 30
Boxplot of
AVGMPG
OIL
Boxplot of
UNEMP
4
20
5
6
40
7
60
8
80
9
Boxplot of
INCOME
OIL
Variable Boxplots
UNEMP
Auto Sales vs. Time
Unemployment Rate vs. Time
Oil Price vs. Time
Income per Capita vs. Time
Average Mileage vs. Time
24
28
20
40
60
80
16000
20
28
12000
AUTO
5000 20000 35000
20
24
AVGMPG
20 40 60 80
INCOME
UNEMP
12000
16000
5000
20000
35000
4
5
6
7
8
4 5 6 7 8 9
OIL
9
Correlation between Variables
Correlation between Variables
18000
12000
AUTO
14000
16000
18000
16000
AUTO
14000
12000
24
26
AVGMPG
28
30
5000
15000
25000
INCOME
35000
16000
Negative
Slope!
12000
AUTO
14000
12000
AUTO
14000
16000
18000
22
18000
20
Correlation – Auto Sales and Other
Variables
20
40
60
OIL
80
4
5
6
7
UNEMP
8
9
Auto Sales = c1*Avgmpg +
c2*Income+c3*oilprice + c4 *
Unemployment + constant
Regression Equation
Dependent Variable: TOTALAUTOS
Method: Least Squares
Date: 11/28/09 Time: 14:30
Sample(adjusted): 1 35
Included observations: 35 after adjusting endpoints
Variable
AVGMPG
INCOME
OILPRICE
UNEMP
Coefficient
226.2330
0.084573
-34.56953
-714.5807
Std. Error t-Statistic
91.73632 2.466122
0.039173 2.158980
16.32058 -2.118156
211.1398 -3.384395
Prob.
0.0196
0.0390
0.0426
0.0020
C
12504.28
2335.651
0.0000
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
0.741293
0.706799
1141.092
39062750
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
14794.74
2107.357
17.04892
17.27112
Log likelihood
Durbin-Watson stat
-293.3562
0.572137
F-statistic
Prob(F-statistic)
21.49035
0.000000
5.353661
Regression I
Barely Significant
at 5% level
All other variables are
significant at 5% level
Highly Significant
F-statistic
Residual vs. Fitted Values
Slightly
skewed to
the left
But still
normally
distributed
Diagnostic of Regression I
3000
White
Heteroskedasticity
test:
Obs*R-squared
10.58868
Probability 0.226112
RESIDUALS
F-statistics 1.409723
Probability 0.239025
2000
1000
0
-1000
-2000
-3000
10000
12000
14000
FITTED
Heteroskedasticity?
16000
18000
Dependent Variable: TOTALAUTOS
Method: Least Squares
Date: 11/28/09 Time: 14:59
Sample(adjusted): 1 35
Included observations: 35 after adjusting endpoints
Variable
INCOME
OILPRICE
UNEMP
C
Coefficient
0.134491
-46.02644
-549.5911
16619.34
Std. Error
0.036183
16.87912
216.0515
1763.169
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
0.688847
0.658735
1231.073
46981757
-296.5865
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
14794.74
2107.357
17.17637
17.35412
22.87646
Durbin-Watson stat
0.541308
Prob(F-statistic)
0.000000
Regression II
t-Statistic
3.717019
-2.726828
-2.543796
9.425836
Prob.
0.0008
0.0104
0.0162
0.0000
All variables are
significant at 5% level
with income as highly
significant
Highly Significant
F-statistic
12
Series: Residuals
Sample 1 35
Observations 35
10
8
6
4
2
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
3.13E-12
55.60103
2333.201
-2337.957
1175.507
0.077039
2.302377
Jarque-Bera
Probability
0.744359
0.689230
0
-2000
-1000
0
1000
2000
Diagnostic of Regression II
Dependent Variable: TOTALAUTOS
Method: Least Squares
Sample(adjusted): 2 35
Included observations: 34 after adjusting endpoints
Convergence achieved after 22 iterations
Variable
Coefficient Std. Error
t-Statistic
UNEMP
-618.2731
165.5458 -3.734755
OILPRICE
-89.27321
27.99955 -3.188380
INCOME
0.094867
0.093254
1.017291
AVGMPG
288.8147
102.7569
2.810661
C
10891.13
2666.290
4.084750
AR(1)
0.809449
0.110204
7.345013
R-squared
0.887058 Mean dependent var
Adjusted R-squared
0.866889 S.D. dependent var
S.E. of regression
775.8966 Akaike info criterion
Sum squared resid
16856434 Schwarz criterion
Log likelihood
-271.1799 F-statistic
Durbin-Watson stat
1.290312 Prob(F-statistic)
Inverted AR Roots
.81
Prob.
0.0009
0.0035
0.3177
0.0089
0.0003
0.0000
14833.03
2126.656
16.30470
16.57406
43.98277
0.000000
Correcting the autocorrelation
function
Dependent Variable: ERROR
Method: Least Squares
Date: 12/02/09 Time: 11:22
Sample(adjusted): 2 35
Included observations: 34 after adjusting endpoints
Variable
Coefficient Std. Error t-Statistic
C
-6.888265 133.5331 -0.051585
ERROR(-1)
0.717434 0.127652 5.620227
R-squared
0.496752 Mean dependent var
Adjusted R-squared
0.481026 S.D. dependent var
S.E. of regression
778.0532 Akaike info criterion
Sum squared resid
19371737 Schwarz criterion
Log likelihood
-273.5443 F-statistic
Durbin-Watson stat
0.981066 Prob(F-statistic)
Error Term Regression
Prob.
0.9592
0.0000
21.87280
1080.031
16.20849
16.29828
31.58696
0.000003
Dependent Variable: Y
Method: Least Squares
Date: 12/02/09 Time: 11:30
Sample(adjusted): 2 35
Included observations: 34 after adjusting endpoints
Variable
Coefficient Std. Error t-Statistic
G
-615.9022 159.3205 -3.865807
F
0.110870 0.059957 1.849160
E
-82.52244 28.51312 -2.894192
B
283.0815 102.1097 2.772328
C
3066.207 702.8768 4.362368
R-squared
0.639690
Mean dependent var
Adjusted R-squared 0.589992
S.D. dependent var
S.E. of regression
772.7143
Akaike info criterion
Sum squared resid 17315534
Schwarz criterion
Log likelihood
-271.6367
F-statistic
Durbin-Watson stat 1.133002
Prob(F-statistic)
Prob.
0.0006
0.0747
0.0071
0.0096
0.0001
4156.583
1206.765
16.27275
16.49721
12.87155
0.000004
Y = totalautos-.717*totalautos(-1)
A = (1-.717)*a
B = avgmpg-.717*avgmpg(-1)
E = oilprice-.717*oilprice(-1)
F = income-.717*income(-1)
G = unemp-.717*unemp(-1)
Y=A+ B + E + F + G
Durbin Watson Correction

Significant Factors Affecting Automobile
Sales:
 Unemployment Rate
 Income per Capita
 Fuel Economy (Avg. Mileage per Gallon)
 Avg. Price of Crude Oil

Forecasting
◦ Automobile Sales , when unemployment rate
and income per capita .

Room for Future Studies:
◦ For stronger R2 (0.74 for Reg. #1 and 0.69 for
Reg. #2), additional variables should be studied
Conclusion
Questions?