The Determinants of Demand for
Hybrid Cars
Shad Ahmed
Mark Baldwin
Kelly Fogarty
Michael Kendra
Overview
Objectives
 Hypotheses / Variable Examined
 Software
 Approach
 Model
 Variables
 Statistics
 Results
 Policy Implications

Objectives

To develop an econometric model and analyze historical
data sets to determine which variables explains what
factors drive the demand for hybrid vehicles in order to
confirm or deny public speculation.

To maximize the statistical significance of the model in
order to provide forecasters with a working model that can
be used to make predictions about future demand for
hybrid cars.

To provide the environmentally conscience public,
automobile industry, and law makers a foundation upon
which to make policy decisions based on objective
reasoning.

To provide a solid foundational model upon which future
research projects can build on.
Hypotheses
H1: The demand for hybrid cars is explained
by gas prices.
 H2 : The demand for hybrid cars is
explained by the Producers Price Index for
automobiles.
 H3: The demand for hybrid cars is explained
by the personal consumption on
expenditures for automobiles by the US
population

Variables examined
Dependent variable: Demand for hybrid vehicles

Economic Indicators
– PPI for motor vehicles
– Personal consumption
on motor vehicles
– Bank loan rate
– Consumer credit
outstanding
– Unemployment rate

Energy Indicators
– Price of gasoline
– Barrels of gasoline
consumed
– Total energy
consumption of US
population
Variable Identification and Definition
Variable
 Demand for Hybrid Cars
 PPI for motor vehicles
 Personal consumption on
motor vehicles
 Bank loan rate
 Consumer credit outstanding
 Unemployment rate
 Price of gasoline
 Barrels of gasoline consumed
 Total energy consumption of
US population
Type
Dep
End
End
Hypothesized Sign
Exo
Exo
Exo
End
End
End
Neg
Pos
Neg
Pos
Pos
Pos
Neg
Pos
Software
WinORSfx was used to develop the model.
 Availability of Economic data from
Economagic
 Extensive ability to determine statistical
significance.

Approach






Monthly data sets were used from 2004 – 2007.
Stepwise regression was run to determine which variables
to eliminate from the model.
Remaining variables were examined for practicality.
Ordinary Least Square method was used to test the
remaining variables for multicollinearity,
homoscedasticity, explainability, and serial correlation.
First Difference was run to attempt to eliminate serial
correlation.
A final model was assembled.
Determinants Model
Qx = -167376 + 758*P + 163.20*Pgas + .104C
Qx = Demand for hybrid vehicles
P = PPI for automobiles
Pgas = Price of gasoline
C = Personal consumption of automobiles
Predictive Ability of Model
Predictive Ability (OLS)
Dependent Variable: Total Hybrid Sales
32,500
29,250
26,000
22,750
19,500
16,250
13,000
9,750
6,500
3,250
3
6
9
12
15
18
21
24
27
Observation
Actual
Predicted
30
33
36
39
42
45
F-statistic
The P-value 0.00001 is significantly below
the critical value, 0.05
 The model is statistically significant above
the 95% confidence interval

F value: 44.63
P value: 0.00001
Coefficient of Determination

Demonstrates that a high degree of variability in
hybrid sales can be explained by variation in the
independent variables

Root MSE
SSQ(Res)
Dep.Mean
Coef of Var (CV)
Multiple R
R-Squared
Adj R-Squared






3489.600
426205816.031
15818.923
22.060%
89.038%
79.278%
77.502%
Multicollinearity

No evidence of multicollinearity is present
in the model (VIF<10)
Average VIF = 1.037
Parameter VIFs
Variable:
Price of gasoline
PPI of automobiles
Personal consumption on
Automobiles
VIF
1.040
1.053
1.018
Constant Variance

White’s Test shows that the model is
homoskedastistic
White’s Test = 8.32
P-Value for White’s = .502
Residual
Constant Variance Graph
Constant Variance Test (OLS)
Dependent Variable: Total Hybrid Sales
14,700
12,600
10,500
8,400
6,300
4,200
2,100
0
-2,100
-4,200
2,413
4,826
7,239
9,652
12,065
14,478
Predicted
16,891
19,304
21,717
24,130
26,543
28,956
Auto Correlation

Durbin Watson test shows evidence of Auto
Correlation

Ho: Rho = 0
Rho: Pos & Neg
Rho: Positive
Rho: Negative




Reject
Do Not Reject
Reject
First difference solution attempted; resulted
in a new R-squared value of .277
Normality of Error Terms
Normal Probability Chart (OLS)
Dependent Variable: Total Hybrid Sales
14,700
12,600
10,500
8,400
6,300
4,200
2,100
0
-2,100
-4,200
-9,000
-7,500
-6,000
-4,500
-3,000
-1,500
0
Expected Residual
1,500
3,000
4,500
6,000
7,500
9,000
Elasticities
Variable
Parameter Estimate
Price of gasoline
2.89
PPI of automobiles
8.38
Personal consumption on
3.87
Automobiles
Elasticity Implications

Income elasticity
– Hybrids are a “luxury” item
– Elasticity is >1
– As income increases, Qx increases

Cross price elasticity
– Gasoline and other automobiles are substitutes
– Elasticity is >1
– As prices of gasoline and other autos increases,
Qx increases