GASOLINE DEMAND ANALYSIS
Applied Economics
MGE 302
David Ennocenti
Dr. Hutton
Gasoline Demand Analysis.max
PROBLEM STATEMENT
Since the early 1970's our economy has been plagued by a
combination of high unemployment and high inflation. A condition
identified by economists as stagflation. During this period our
nation, as well as the rest of the world, has had to deal with the
burden of an energy crisis. Of particular importance is our
nation's dependence on petroleum, more specifically gasoline.
What effect the energy crisis has had on economic growth is
evident. In late 1973, there was the Arab oil embargo, then in
1974, there was a tremendous increase in the price of imported oil.
The great and sudden increase in the price of imported crude oil
had a very substantial effect on both the national and inter-
national economy. It helped to create inflation at home, and
transfered tens of billions of dollars from the oil-consuming
nations to the oil-producing nations.@
In the early 1970's William E. Simon, the nation's energy czar,
wanted to see if more generous reliance on the classic market
mechanism-price as the means of equating demand with supply -
would help solve the energy crisis. His goal was to cut gasoline
consumption by 20 to 30 percent.
In order to see if this goal is feasible it is necessary to
do a demand analysis. A demand analysis serves two major manager-
ial objectivess (1) it provides the insights necessary for the
effective manipulation of demand, and (2) it aids in forecasting
sales and revenues.
How consumption of gasoline will be effected by price increases
depends on it's price elasticity of demand.
Gasoline Demand Analysis.max
Price elasticity of demand is defined as the ratio of the per-
centage change in quantity demanded to the percentage change in
price, assuming all other factors influencing demand remain un-
changed. (i.e., ceteris parabus)
Before making a hypothesis as to the nrice elasticity of
demand for gasoline, I will first take into consideration factors
that govern the size of the coefficient. These factors are (1) the
number and closeness of substitutes for the commodity (2) number
of uses of the commodity and (3) expenditures on the commodity.
The more available and better the substitutes for a commodity,
the greater its price elasticity is likely to be. In the case
of gasoline there are no substitutes to speak of ( with the recent
exception of gasahol and even that is made mostly from gasoline.)
The greater the number of uses of a commodity, the greater
its price elasticity. Gasoline has one use, that is to run engines.
The greater the percentage of income spent on a commodity,
the greater its elasticity is likely to be. For example, the de-
mand for automobiles is likely to be much more price elastic than
that for gasoline.
Given these factors c!„..31- 416 that the
price elasticity of demand for gasoline is inelastic.
Demand studies of gasoline have made the following con-
clusions. Alan Greenspan of Townsend-Greenspan and Co., be-
lieves that the elasticity of gasoline demand is about -0.4.
A preliminary estimate from the Transportation Departments re-
search center shows elasticity in the -0.2 range. A model de-
veloped by Professor Hendrik S. Houthakker of Harvard and
Philip K. Verleger, Jr. of Data Resources, Inc., puts short-run
elasticity at -0.3.
Since demand is hypothesized to be price inelastic, if price
were to increase so would total revenue increase.
a
Gasoline Demand Analysis.max
Empirical evidence supports the hypothesis that demand for
gasoline is price inelastic. The price per gallon of regular grade
gasoline, excluding taxes, increased in the second quarter of 1980
by more than 40 percent over the price of the same quarter in 1979.
Figures published in Business Week's Corporate Scoreboard shows
that in the middle of one of the sharpest recessions on record,
oil companies increased their net aftertax earnings in the second
quarter 1980 by some 32% over the year ago figure while the rest
of the U.S. industry was suffering a profit decline of 18%. 0
The coefficient on income elasticity of demand measures the
percentage change in the amount of a commodity purchased per unit
of time resulting from a given percentage change in a consumer's
income, ceteris paribus.
When the coefficient is negative, the good is inferior. If
elasticity is positive, the good is normal. A normal good is
usually a luxury if its income elasticity of demand is greater
than one, otherwise it is a necessity. Therefore, gasoline's
044, t•
income elasticity should be since it is neither an in-
ferior nor luxury good.
Gasoline Demand Analysis.max
MODEL DEVELOPMENT
In developing a demand function price and disposable income
are usually included as factors influencing consumption of a com-
modity. Since gasoline is a normal good. as the price of gasoline
increases, the amount of gasoline demanded should decrease. As
per capita disposable income increases the quantity of gasoline
demanded could be expected to increase. Again this is because gas-
oline is a normal good.
In addition to price and disposable income, there are other
factors which effect the consumption of gasoline. The number of
registered motor vehicles is included in this demand analysis. As
the number of registered motor vehicles increases (decreases) the
quantity of gasoline demanded should increase (decrease).
ImitaAnuch as there are no substitutes for gasoline one can-
not be included in this model. However, although people cannot
find another commodity in which to run their cars, people can use
other means of transportation. To take account of this factor,
urban transit systems revenue is included in the demand analysis.
As this increases the demand for gasoline should decrease.
At the start of the third quarter 1974, President Richard Nixon
initiated a mandatory fifty-five mile per hour speed limit. This
was introduced as an effort to improve the fuel efficiency of
automobiles and curb demand for gasoline. This variable is entered
in the demand function as a dummy variable, taking a value of one
for years in which the fifty-five mile per hour speed limit was used
and a value of zero for all others.
A trend variable is included in the demand function to ex-
plain the change in quantity demanded over time. As this value
increases demand should increase.
Gasoline Demand Analysis.max
Since the data in this demand analysis is quarterly, seasonal
trends are a counted for by the use of dummy variables. The de-
mand for gasoline is typically lowest in the first quarter, higher
in the second, reaches its peak in the third quarter, and drops
to a level in the fourth quarter which is slightly lower than the
second quarter and higher than ther level of the first quarter.
In summary, the factors considered for this analysis are
price, per capita disposable income, number of registered motor
vehicles, urban transit revenue, the introduction of the fifty-
five mile per hour speed limit, a trend variable, and dummy var-
iables to measure seasonal effects. All of these variables were
used in the statistical analysis.
The demand function to be tested is:
Y1= a + bXi + bXt + bXp + bXu + bXm bXr + bXa + bXb + bXc + e
Where: Y1 = Quantity of gasoline demanded in millions of barrels.
Xi =Per carita disposable income in 1967 dollars.
Xt = Quarterly trend variable form the first quarter 1970
to the fourth quarter 1978.
Xp = Price per gallon of regular grade gasoline in 1967.
Xu = Urban transit revenue in 1967 dollars.
Xm - Dummy variable for the introduction of the fifty-
five mile per hour speed limit.
Xr = Registered motor vehicles in millions.
Xa = Seasonal trend variable (1) for the first quarter of
each year.
Xb = Seasonal trend variable (1) for the second quarter
of each year.
Xc = Seasonal trend variable (1) for the third quarter of
each year.
e = The disturbance term.
Gasoline Demand Analysis.max
(Data sources for the nrevious cage are listed in the appendix)
Given the previous information the exrected signs of the co-
efficients are as follows:
It = a + bXi bXt bXp - bXu - bXm + bXr - bXa + bXb + bXc.
The reason for the expected negative sign for Xa is, this
is the quarter in which gasoline consumption reaches its lowest
point. Xb should be positive due to the upswing in demand in the
second quarter over the first quarter. In the third quarter, gas-
oline consumption reaches its peak. The ore, Xc should be
positive.
6
9
Gasoline Demand Analysis.max
EVALUATION OF RESULTS
The method of analysis used in this demand study is the
least squares regression method. The multiple regression equation
identifies the best fitting line based on the method of least
squares.
The multiple regression model used to estimate the demand
function for gasoline yielded the following results:
Y1 = 146.372 + .017Xi ¨ .018Xt - 372.744Xp
(.710) (.140) (-7.515)
-.014Xu + 2.746Xm + 4.058Xr - 54.729Xa
(-3.186) (.539) (3.713) (-11.484)
+ 6.920Xb 21.540Xc
(1.970) (7.635)
Where, the values in parentheses represent t-ratios.
The only inconsistency between the hypothesized results and
the actual results is the positive value of the beta coefficient
for Xm, the variable for the fifty-five mile per hour speed
limit. This may have been caused by problems I will discuss
later in the case.
The coefficient of determination measures the proportion of
the variation in the independent variable (quantity of gasoline
demanded) that is explained by the regression line (the independent
variables) This is calculated by dividing the explained sum of
squares,(regression sum of squares) by the total sum of squares.
This value is calculated to be .99407 and is designated by R square.
In other words, the regression equation explains 99.4 percent of
the variation in the quantity of gasoline demanded.
A value of .99703 is calulated for r, the coefficient of
correlation. The sample coefficient of correlation, r, is some-
what biased as an estimator, with an absolute value which is too large.
Gasoline Demand Analysis.max
An unbiased estimator for the coefficient of determination,
R square, is adjusted R square.
e This value is .99201.
Another method of evaluating the explanatory power of the
regression equation is the use of the F-test. This is used to
test the null hypothesis that all the regression coefficients are
zero.
Critical F at the 5 percent level of significance for 9/26
degrees of feedom is 2.27. The F-ratio is the mean square re-
gression divided by the mean square residual. If the calculated
F is greater than the critical F, the decision is to reject the
null hypothesis that there is no significant relationship be-
tween the dependent variable and the independent variables of the
regression equation.
The calculated value of F is 483.9. Since this value is
greater than the critical value of 2.27, we reject the null
hypothesis that there is no significant relationship between the
dependent variable and the independent variables.
Testing the individual beta's of the coefficients is ac-
complished by the use of the t-ratio. This statistical test is
used to determine whether the smple value b for each of the coef-
ficients is significantly different from zero. The null hypothesis
is that B=0 and the alternate hypothesis is that BX0. The sample
statistic will have a t distribution with (n-k-1) degrees of
freedom. Where nz the number of observations and k = the number
of independent variables. Degrees of freedom for this analysis
is (36-9-1) 26. Critical values for t at the 5 percent level
of GiunifinannP nnd 2 cipgrppR of frepcinm ArP 2J)56
and +2.056.
If the calculated value of t is either less than -2.056 or greater
than +2.056, the decision is to reject the null hypothesis and
conclude that a statistically significant relationship exists be-
tween the quantity of gasoline demanded and the independent varihip.
Gasoline Demand Analysis.max
The test statistic is b-0 divided by the standard error of beta.
The tests for the individual beta's are as follows.
For per capita disposable income t = .710 which means b is not
significantly different from zero.
The quarterly trend variable representing time, Xt, has a
t-ratio of .138 and this is not statistically significant.
For the price of gasoline t = -7.515. This is statistically
significant.
Urban transit revenue has a t-ratio of -3.186. This is
significantly different form zero.
For the introduction of the fifty-five mile per hour speed
limit, the dummy variable Xm, b has a t-ratio equal to .539. This
is not statistically significant.
Registered motor vehicles has a t-ratio of 3.713. This is
significantly different from zero.
Seasonal trend variable Xa has a t value of -11.484. This
is significantly different from zero.
Seasonal trend variable Xb has a t-ratio equal to 1.970.
This is not statistically significant.
Seasonal trend variable Xc has a t of 7.635 and is statistic-
ally significant.
In summary, significant variables in the equation are, Price
per gallon, urban transit revenue, registered motor vehicles,
seasonal trend variable Xa, and seasonal trend variable Xc.
Variables not statistically significant are, per capita disposable
income, time, the fifty-five mile per hour speed limit, and
seasonal trend variable Xb. These variables may have tested to be
insignificant due to problems that can occur with multiple
regression analysis which I will define.
9
Gasoline Demand Analysis.max
10
"Otlix , Cry0
There are four assumptions of multiple c-errrtrnti7511 analysis
these are that : (1) all variables involved in the analysis are
random variables (2) the relationships are all linear (3) the
conditional variances are all equal and (4) the conditional
disturbances are all normal. These requirements are quite stringent
and are seldom completely satisfied in real data situations.
However, multiple correlation analysis is quite robust in the sense
that some of these assumptions, and particularly assumption number
four, can be violated without serious consequences in terms of the
validity of the results.
Problems which can violate some of these assumptions are,
autocorrelation, heteroscedasticity, specification and measurement
errors, and multicollinearity.
Autocorrelation refers to the existence of a significant
pattern in the successive values of the error term. Positive
autocorrelation is inferred whenever successive positive (negative)
disturbances tend to be followed by disturbances of the s2me sign.
Negative autocorrelation is inferred whenever successive positive
(negative) disturbances tend to be followed by disturbances of the
opposite sign. The overall growth of the economy coupled with
business cycles causes most economic time series to have an over-
all upward trend with periodic upturns and downturns around this
trend producing positive autocorrelation. qp Seasonal trend
variables, which are included in this analysis, can help eliminate
this problem.
The Durbin-Watson test is reported in the comnuter printout
and is a test for autocorrelation. A Durbin-Watson value of less
than 1.5 is usually a indication that positive autocorrelation,is
present. The value reported in this regression is 2.482, which
clearly indicates there is no positive autocorrelation nroblem.
Gasoline Demand Analysis.max
Critical values for Durbin-Watson are 1.5 and 2.5. Values outside
these extremes usually imply autocorrelation, The reported value
of 2.482 is close to 2.5. This high value may have been caused
by the number of runs of signs (25) of the residuals. Any further
runs of signs may have caused autocorrelation problems. Invest-
igation of the residuals shows areas where the disturbances are
followed by disturbances of the same sign, as is the case with
positive autocorrelation. For this reason, and the fact that
Durbin-Watson is below 2.5, I feel that autocorrelation is not a
serious problem with this regression.
Homoscedasticity is the assumntion that the error terms,
which are independent random variables, have a uniform variability
about the regression line. The lack of a uniform variance of the
error terms is known as heteroscedasticity. Observation of the re-
siduals gives no indication of heteroscedasticity. The residuals
appear to be randomly distributed about the regression line.
Measurement errors are possibly present in this regression
equation. These errors occur in the collection process of the
data used. For example, the nrice per gallon of gasoline was
made from a random sample of fifty-five U,S. cities at midmonth
prices. This sample may be a different value than the true price.
A problem that is present in this model is multicollinearity.
Multicollinearity is indicated whenever two or more expanatory
variables are highly correlated and the standard errors of their
regression coefficients become large, causing the t-ratios to go
down. (D Multicollinearity can also cause sign changes of the
coefficients.
Mentioned earlier was the fact that the coefficient for the
fifty-five mile per hour speed limit should have been negative but
the value in the regression became positive.
Gasoline Demand Analysis.max
12
The explanation for this occurrencemulticollinearity, This
variable is highly correlated with time, price, and registered motor
vehicles. Their correlation coefficients with the fifty-five mile
per hour speed limit are .866, .861, and.839, respectively. The
relationship between these variables may have caused the stand-
ard error to increase causing the t-ratio to prove the speed limit
variable is not significant. Multicollinearity may also have
caused the sign change with this variable.
Other variables which proved to be insignificant were , per
capita disposable income, time, and seasonal trend variable Xb.
The trend variable representing time is highly correlated
with income, price, registered motor vehicles, and the variable
for the speed limit. Their respective correlation coefficients are
.912, .816,.991, and .866. This clearly indicates that the trend
variable for time is highly correlated with these other variables.
The beta value for time is .180 and its standard error is 1.294.
The standard error may have increased due to multicollinearity
causing the t-test to be an unreliable indicator of the statistical
significance of the trend variable for time.
Correlation coefficients between income and the variables
time and registered motor vehicles are .912 and .921, respectively.
As with the trend variable for time, the beta value for income may
have proven insignificant due to multicollinearity. Beta for income
is .017 and its standard error is .024. The error term may have beet
inflated thus, causing the t-ratio to decrease and prove the
variable to be insignificant.
Seasonal trend variable Xb, representing the second quarter
has a beta which is not significantly different from zero. Unlike
the three pervious variables, multicollinearity had no effect on
the t-ratio of the trend vari,qhlp Yh.
Gasoline Demand Analysis.max
This is evident from the fact that its highest correlation
coefficient with any variable is -.333 this is with both the
trend variables Xa and Xc. Deductive reasoning can lead to the
conclusion that the beta for trend B is in fact not statistically
significant. This is because the demand for gasoline in the first
quarter is at its lowest annual point. In the third quarter
demand reaches its peak. Since the second quarter, as well as the
k beta
fourth, falls between these two extremes4can be hypothesized to
be statistically insignificant. This is because the second and
fourth quarters would approximate the average of the four quarters
more closely than the first and third quarters (the two extremes)
Ommission of this variable would result in multicollinearity
problems between the trend variables A and C.
In summary, multicollinearity may have affected the following
variables time, income, and the fifty-five mile per hour speed limit.
However, the presence of high intercorrelation among the independent
variables does not necessarily invalidate the use of the regres-
sion equation for prediction purposes. Provided that the inter-
correlation pattern among the explanatory variables persists into
the future, the equation can produce reliable forecasts of the
value of the dependent variable. 0
It can reasonably be expected that the fifty-five mile per
hour speed limit will continue into the future as well as an in-
crease in the number of motor vehicles. The trend variable, time,
will obviously also increase into the future. Therefore, mul-
ticollinearity should not invalidate the use of this regression
equation for prediction purposes.
95 percent confidence intervals for the individual beta
coefficients are given in the computer output. The papameter
beta can be estimated by constructing the following confidence in-
terval.
13
Gasoline Demand Analysis.max
b (+) or (-) t(standard error of beta)
Degrees of freedom associated with t are (n-k-1). Where,
k is the number of independent variables and n is the number of
observations. With 26 degrees of freedom the value for t is 2.056.
Confidence intervals are as follows:
Income Xi .032 to .065
Time Xt 2.481 to 2.840
Price Xp - 475.0 to-271.0
Urban Transit Revenue Xu - .022 to ,005
Speed Limit Xm - 7.73 to 13.22
Registered Motor Vehicles Xr + 1.8. to 6.30
Trend A Xa - 64.5 to-44.9
Trend B Xb - .301 to 14.141
Trend C Xc 15.7 to 27.3
The standard error of estimate can be used to establish
prediction interval for the dependent variable given specific
values of the independent variables. This value has been cal-
culated by the computer to be 4.844.. A prediction interval can
be estimated by the normal distribution or by the t distribution.
When using the normal distribution a 95 % confidence interval
would be, the Y estimate minus (2x4.844) to the Y estimate plus
(2x4.844). When using t, the 95% confidence interval with 26
degrees of freedom is, the Y estimate minus (2.056 x 4.844) to
the Y estimate plus (2.056 x 4.844).
As expected, income elasticity of demand is estimated in
this analysis to be inelastic. The value of .086 mens that as
income increases (decreases) by 1% demand will increase (decrease)
by .086%. This value, though quite low, is reasonable for gas-
oline. Since gasoline is not an inferior good the elasticity should
be positive. Luxury goods have an elasticity which is greater than
nn e.
Gasoline Demand Analysis.max
Gasoline, which most people view as a necessity, should have an
elasticity greater than zero and less than one.
The validity of this calculated value is questionable due
the
to the fact that/theta coefficient did not prove to be significantly
different from zero. As mentioned earlier, this may have been
caused by multicollinearity.
The price elasticity of demand in this analysis is estimated
to be .15. Since this value is less than one, price elasticity of
demand is inelastic. This is consistent with what it was
hypothesized to be. The value of .15 is lower than other estimates
of .4 and .3 but is close to the .2 estimate made by the Transport-
ation Department's research center. The elasticity measure is
statistically significant because the beta coefficient for price
tested to be significant.
In order to use price as a mechanism for cutting demand by 30
percent, price would have to be increased by 200 percent or. in
other words, nrice wouldhave to be tripled. To cut demand by 20
percent would call for an increase in price of 193 percent. Which
means price would have to be more than doubled.
Controlling demand with such substantial price increases may
come at the exrense of serious consequences. Tremendous Price
increases could bring about a massive distribution of profits,
as in 1974, from oil consumers to the oil producers. This massive
shift in profits could have serious implications for the United States
economy. It could:
(1) Lower the rate of economic growth as more and more
companies find it increasingly difficult to acquire funds for
capital investment either form internally generated sources or
from financial markets.
(2) Lead to continued loss of economic efficiency within the
corporTte sector as oil company managements, with more money than
IS
Gasoline Demand Analysis.max
they know what to do with, take on unfamiliar projects, while
other companies have to abandon some ventures because of lack of
funds.
(3) Exacerbate inflation as companies seek to boost their de-
clining profit margins by raising prices. 0
These assumntions lead to the conclusion that relying on price
as a means of reducing gasoline consumption could create further
economic problems. Perhaps the solution to our energy crisis
lies not in price increases alone, but in a combination of al-
ternatives. Some of which I will now consider.
The number of registered motor vehicles has increased by
37.5% from 1970 to 1978. The results of this regression equation
indicates that this factor has had the effect of incrensing the
demand for gasoline. Since the mid-seventies automobile manufact-
urers have been improving the fuel efficiency of automobiles.
Further improvements seem to be called for in order to help cut
gasoline consumption.
The beta coefficient for the variable representing the fifty-
five mile per hour speed limit indicates that this has not helped
decrease demand. As mentioned earlier, this value may have taken
on a positive value and proved to be insignificant due to multi-
collinearity nroblems. In any case, additional speed reductions
may be necessary in order for this variable to have any significant
effect in reducing demand. aeot
IrmA0Le.
lao 4-464,4-
According to this analysis urban transit systems has hd a
decreasing effect on gasoline demand. Increased use of public
transportation should be encouraged in order to decrease the demand
for gasoline.
The fact that gasoline has no substitutes is one of the factors
which causes its price elasticity to be inelastic.
16
Gasoline Demand Analysis.max
The development of an alternate source of energy seems to be an
imperative solution to the energy crisis. In July of 1980,
Fresident Jimmy Carter signed into law a synthetic fuels program.
This program is supposed to spur the production of 500,000 barrels
of "synfuels" a day by 1987 and two million a day by 1992. Until
the time this production takes place, measures will have to be taken
to curb the demand for gasoline. Due to economic difficulties
which may arise, reliance on substantial price increases does not
seem feasible.
In summary, moderate urice increases together with further
speed reductions, improved fuel efficiencies of new automobiles,
increased use of public transportation, and efforts to develop
an alternate source of energy may be one possible soulution to our
current energy crisis.
IT
Gasoline Demand Analysis.max
CONCLUSION
Variables that could be added to the equation are, total number
of miles driven by all vehicles, the number of licensed drivers,
and the fuel efficiency ratings of motor vehicles.
The number of miles driven by all vehicles was not found in
quarterly data in the sources used for this analysis. The beta
coefficient for this variable could be expected to be positive,
seasonal, and highly correlated with demand. The number of miles
driven per quarter could be used instead of the trend variable
representing time.
As the number of licensed drivers increases demand for gasoline
could be expected to increase. The beta coefficient for this
variable would be hypothesized to be positive.
Fuel efficiency ratings of automobiles has been a recent
issue of concern. Due to this, data may not be available for all
the years used in this analysis. One method that could be used
to include this variable in the model would be to divide the
number of miles driven by the number of barrels of gasoline de-
manded, then divide this figure by the number of motor vehicles.
This figure would be the average miles per barrel of gasoline per
motor vehicle. As this figure increases demand could be expected
to decrease. Therefore, the beta coefficient would be hypothisized.
to be negative.
Advertising expenditures were not included in this model al-
though they very well could have been. However, I do not feel that
advertising has had much effect in influencing demand consumption
in the 1970's. In marketing terminology, gasoline is in the
maturity stage of the product life cycle.
Gasoline Demand Analysis.max
In this stage marketing expenditures are directed toward brand
preference and services.
3
This would effect individual companies
but not the industry as a whole.
An advertising variable could have been included which may
have influenced gasoline demand. The government has been active
in the demarketing of gasoline consumption throughout the 1970's.
It would be interesting to see if this has had any effect on de-
creasing gasoline consumption. This variable would be hypothesized
to have a negative beta coefficient.
I feel this model would be useful in predicting gasoline
demand within a 95% confidence interval. The reasons for this are
the high R square value of .994 and the overall F-test which
proved to be significant. However, as with all regression models,
limitations do exist with the use of this model for a means of
predicting.
Common Limitations are:
(1) A value of the dependent variable cannot be legitimately
estimated if the values of the independent variables are outside
the range of values which served as the basis for the regression
equation.
(2) If the estimate of the dependent variable involves the
prediction of a result which has not yet occured, the historical
data which served as a basis of the regression equation may not
be relevant for future events.
(3) The use of a prediction interval is based on the as-
sumption that the conditional distributions of the dependent
variable are normal and have equal variances. k.9
• AA
11116 1d25 L 1UrrIltraUlOn iS concerneu wiuu autocorreiaLun anu
heteroscedasticity. As mentioned earlier I don't feel these problems
are present in this model.
14
Gasoline Demand Analysis.max
Most of the variables in this regression should not be
subject to the first two problems. However, the price of gas-
oline could present a nroblem if price should suddenly and sub-
stantially increase.
For example; Alan Greenspan, whose model WPS mentioned in
the introduction, feels his estimates are valid only up to 15
cents above the current price. His model was made in 1973 when
the annual average price per gallon was 27.5 cents.
Within the limitations of any regression equation, I feel
this model would be useful in predicting the demand for gasoline
within a 95% confidence interval.
20
Gasoline Demand Analysis.max
Bibliography
1) Principles of Microeconomics ; Edwin Mansfield ; Second
Edition ; Page 394
2) Principles of Microeconomics ; Readings, Issues, and Cases ;
Edwin Mansfield ; Second Edition ; Pg. 49
3) Managerial Economics ; James R. McGuigan and R. Charles Moyer ;
Second Edition ; Pg 100
4) Principles of Microeconomics ; Readings, Issues, and Cases ;
Edwin Mansfield ; Second Edition ; Pg 49
5) Business Week ; August 18, 1980 ; Pg 84
6) Business Statistics ; Leonard J. Kazmier ; First Edition Pg 304
7) Business Statistics ; Leonard J. Kazmier ; First Edition Pg 916
8) Managerial Economics ; James R. McGuigan and R. Charles Moyer;
Second Edition; Pg 84
9) Managerial Economics ; James R. McGuigan and R. Charles Moyer;
Second Edition ; Pg 87
10) Managerial Economics ; James R. McGuigan and R. Charles Moyer;
Second Edtion ; Pg 87
11) Business Week ; August 18, 1980 ; pg 84
12) Wall Street Journal ; July 11, 1980 ; pg 17
13) Principles of Marketing ; Philip Kotler ; First Edition ; Pg 354
14) Business Statistics ; Leonard J. Kazmier ; First Edtion ; Pg 304
15) Principles of Microeconomics ; Readings, Issues, and Cases ;
Edwin Mansfield ; Second Edition ; Pg 50
Gasoline Demand Analysis.max
Appendix
Data Sources
United States Bureau of the Census: Statistical Abstracts
100th Edition, Sept. 1979.
1) Motor vehicle registrations in millions includes, total
automobiles, trucks, and buses.
Survey of Current Business, Volumes 51 through 59, Volume 60
number 8.
1) Disposable personal income in billions of dollars.
2) Total United States population in millions.
3) Price per gallon of gasoline, regular grade, excluding
taxes. Taken from 55 U. S. cities at mid-month.
4) Urban Transit Systems total revenue, from passengers
carried, in millions.
5) Domestic demand of gasoline, in millions of barrels.
6) Consumer Price Index for all items, base year 1967.
(Not seasonally adjusted)
7) Consumer Price Index for Public Transportation, base year
1967 (Not seasonally adjusted)
Gasoline Demand Analysis.max
