Automobile Sales and the Automobile Sales and the General EconomyGeneral Economy

ECON240AECON240AGroup #1Deepti Goyal

Rory Tyler HofstatterHairuo Hu

Joel Benjamin LindenbergSooyeon Angela Shin

Michael John StrombergKathy Zha

Ling Zhu

IntroductionIntroduction

Dependent Variable◦Amount of Auto Sales

Independent Variables◦Unemployment◦Price of Oil◦Average Mileage per Gallon◦Income per Capita

Why Study Such Variables?Why Study Such Variables?

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

Auto Sales by MakeAuto Sales by Make

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

20080.00

20.00

40.00

60.00

80.00

100.00

120.00

others

Hyundai

Honda

Nissan

Toyota

Volksw agen

GM

Ford

Daimler

Chrysler

American Motors

Trucks vs. CarsTrucks vs. Cars

year

1974

1976

1978

1980

1982

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1994

1996

1998

2000

2002

2004

2006

2008

0

2000

4000

6000

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10000

12000

14000

16000

18000

20000 Trucks

Cars

How the Study is ConductedHow the Study is Conducted

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

Data GatheringData Gathering

Auto Sales: Ward’s Automotive GroupUnemployment Rate:

US Bureau of Labor StatisticsAnnual Crude Oil Prices:

US Bureau of Labor StatisticsIncome per Capita:

US Department of Commerce, Bureau of Economic Analysis

35 Years (1974-2008)

Variable HistogramsVariable Histograms

Histogram of AUTO

AUTO

Frequency

10000 12000 14000 16000 18000

02

46

8

Histogram of AVGMPG

AVGMPG

Frequency

18 20 22 24 26 28 30

02

46

810

12

14

Histogram of INCOME

INCOME

Frequency

10000 20000 30000 40000

01

23

45

6

Histogram of OIL

OIL

Frequency

0 20 40 60 80 100

05

10

15

Histogram of UNEMP

UNEMP

Frequency

4 5 6 7 8 9 10

02

46

810

12

Variable BoxplotsVariable Boxplots

20

22

24

26

28

30

Boxplot of AVGMPG

AVGMPG

5000

150002

50003

5000

Boxplot of INCOME

INCOME

20

40

60

80

Boxplot of OIL

OIL

45

67

89

Boxplot of UNEMP

UNEMP

Auto Sales vs. TimeAuto Sales vs. Time

Unemployment Rate vs. TimeUnemployment Rate vs. Time

Oil Price vs. TimeOil Price vs. Time

Income per Capita vs. TimeIncome per Capita vs. Time

Average Mileage vs. TimeAverage Mileage vs. Time

Correlation between VariablesCorrelation between Variables

AUTO

20 24 28 20 40 60 80

12000

16000

20

24

28

AVGMPG

INCOME

5000200003

5000

20

40

60

80

OIL

12000 16000 5000 20000 35000 4 5 6 7 8 9

45

67

89

UNEMP

Correlation between VariablesCorrelation between Variables

Correlation – Auto Sales and Other Correlation – Auto Sales and Other VariablesVariables

Negative Slope!

20 22 24 26 28 30

12000

14000

16000

18000

AVGMPG

AU

TO

5000 15000 25000 35000

12000

14000

16000

18000

INCOME

AU

TO

20 40 60 80

12000

14000

16000

18000

OIL

AU

TO

4 5 6 7 8 9

12000

14000

16000

18000

UNEMP

AU

TO

Regression EquationRegression Equation

Auto Sales = c1*Avgmpg + c2*Income+c3*oilprice + c4 * Unemployment + constant

Regression IRegression I

Highly Significant F-statistic

Barely Significant at 5% level

All other variables are significant at 5% level

Dependent Variable: TOTALAUTOS

Method: Least Squares

Date: 11/28/09 Time: 14:30

Sample(adjusted): 1 35

Included observations: 35 after adjusting endpoints

Variable Coefficient Std. Error t-Statistic Prob.

AVGMPG 226.2330 91.73632 2.466122 0.0196INCOME 0.084573 0.039173 2.158980 0.0390

OILPRICE -34.56953 16.32058 -2.118156 0.0426

UNEMP -714.5807 211.1398 -3.384395 0.0020

C 12504.28 2335.651 5.353661 0.0000

R-squared 0.741293 Mean dependent var 14794.74Adjusted R-squared 0.706799 S.D. dependent var 2107.357

S.E. of regression 1141.092 Akaike info criterion 17.04892

Sum squared resid 39062750 Schwarz criterion 17.27112

Log likelihood -293.3562 F-statistic 21.49035

Durbin-Watson stat 0.572137 Prob(F-statistic) 0.000000

Diagnostic of Regression IDiagnostic of Regression I

Residual vs. Fitted Values

Slightly skewed to the left

But still normally distributed

Heteroskedasticity?Heteroskedasticity?

-3000

-2000

-1000

0

1000

2000

3000

10000 12000 14000 16000 18000

FITTED

RE

SID

UA

LS

White Heteroskedasticity test:

F-statistics 1.409723 Probability 0.239025

Obs*R-squared 10.58868

Probability 0.226112

Regression IIRegression II

Highly Significant F-statistic

All variables are significant at 5% level with income as highly significant

Dependent Variable: TOTALAUTOS

Method: Least Squares

Date: 11/28/09 Time: 14:59

Sample(adjusted): 1 35

Included observations: 35 after adjusting endpoints

Variable Coefficient Std. Error t-Statistic Prob.

INCOME 0.134491 0.036183 3.717019 0.0008OILPRICE -46.02644 16.87912 -2.726828 0.0104

UNEMP -549.5911 216.0515 -2.543796 0.0162

C 16619.34 1763.169 9.425836 0.0000

R-squared 0.688847 Mean dependent var 14794.74Adjusted R-squared 0.658735 S.D. dependent var 2107.357

S.E. of regression 1231.073 Akaike info criterion 17.17637

Sum squared resid 46981757 Schwarz criterion 17.35412

Log likelihood -296.5865 F-statistic 22.87646

Durbin-Watson stat 0.541308 Prob(F-statistic) 0.000000

Diagnostic of Regression IIDiagnostic of Regression II

0

2

4

6

8

10

12

-2000 -1000 0 1000 2000

Series: ResidualsSample 1 35Observations 35

Mean 3.13E-12Median 55.60103Maximum 2333.201Minimum -2337.957Std. Dev. 1175.507Skewness 0.077039Kurtosis 2.302377

Jarque-Bera 0.744359Probability 0.689230

Correcting the autocorrelation Correcting the autocorrelation functionfunction

Dependent Variable: TOTALAUTOSMethod: Least SquaresSample(adjusted): 2 35Included observations: 34 after adjusting endpointsConvergence achieved after 22 iterations

Variable Coefficient Std. Error t-Statistic Prob. UNEMP -618.2731 165.5458 -3.734755 0.0009

OILPRICE -89.27321 27.99955 -3.188380 0.0035INCOME 0.094867 0.093254 1.017291 0.3177AVGMPG 288.8147 102.7569 2.810661 0.0089

C 10891.13 2666.290 4.084750 0.0003AR(1) 0.809449 0.110204 7.345013 0.0000

R-squared 0.887058 Mean dependent var 14833.03Adjusted R-squared 0.866889 S.D. dependent var 2126.656S.E. of regression 775.8966 Akaike info criterion 16.30470Sum squared resid 16856434 Schwarz criterion 16.57406Log likelihood -271.1799 F-statistic 43.98277Durbin-Watson stat 1.290312 Prob(F-statistic) 0.000000Inverted AR Roots .81

Error Term RegressionError Term Regression

Dependent Variable: ERRORMethod: Least SquaresDate: 12/02/09 Time: 11:22Sample(adjusted): 2 35Included observations: 34 after adjusting endpoints

Variable Coefficient Std. Error t-Statistic Prob. C -6.888265 133.5331 -0.051585 0.9592

ERROR(-1) 0.717434 0.127652 5.620227 0.0000R-squared 0.496752 Mean dependent var 21.87280Adjusted R-squared 0.481026 S.D. dependent var 1080.031S.E. of regression 778.0532 Akaike info criterion 16.20849Sum squared resid 19371737 Schwarz criterion 16.29828Log likelihood -273.5443 F-statistic 31.58696Durbin-Watson stat 0.981066 Prob(F-statistic) 0.000003

Durbin Watson CorrectionDurbin Watson Correction

Dependent Variable: YMethod: Least SquaresDate: 12/02/09 Time: 11:30Sample(adjusted): 2 35Included observations: 34 after adjusting endpointsVariable Coefficient Std. Error t-Statistic Prob.

G -615.9022 159.3205 -3.865807 0.0006F 0.110870 0.059957 1.849160 0.0747E -82.52244 28.51312 -2.894192 0.0071B 283.0815 102.1097 2.772328 0.0096C 3066.207 702.8768 4.362368 0.0001

R-squared 0.639690 Mean dependent var 4156.583Adjusted R-squared 0.589992 S.D. dependent var 1206.765S.E. of regression 772.7143 Akaike info criterion 16.27275Sum squared resid 17315534 Schwarz criterion 16.49721Log likelihood -271.6367 F-statistic 12.87155Durbin-Watson stat 1.133002 Prob(F-statistic) 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

ConclusionConclusion

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

QuestionsQuestions??