G.E. Nasr et al. r Energy Economics 22 2000 627]640 631
Fig. 3. Degree days per month for the period 1993]1997.
The daily mean temperatures used to calculate DD are obtained from climato- Ž
. logical monthly bulletins Ghaddar, 1993]1997 . Fig. 3 shows the variability of DD
throughout each year for the 1993]1997 period.
3. Model specification and methodology
The majority of the studies in the literature have mainly analyzed energy demand in countries experiencing normal economic activities under peaceful and
stable conditions. However, the economic dynamics existing in war-ravaged coun- tries, as they enter the post-war era, require special consideration. More specifi-
cally, a rationing policy of electrical energy supply might be responsible for varying the impact and statistical significance of customary determinants of energy due to
Ž .
Ž the asynchronous recovery of the private energy consumer and public energy
. supplier sectors. Due to this fact, The 1993]1997 period was divided into three
different time spans each comprising different rationing level: 1. The 1993]1994 period where extensive rationing is implemented.
2. The 1995]1997 period, comprising reduced rationing period, year 1995, and practically rationing free period, 1996]1997.
3. The 1996]1997 period where rationing is virtually non-existent in major metropolitan centers.
In this study, a static model is first used to model electrical energy demand. The Ž
. model relates electrical energy consumption to TI GDP proxy and DD. The
Ž .
model is estimated using ordinary least squares OLS and has the following form:
G.E. Nasr et al. r Energy Economics 22 2000 627]640 632
Ž . C s A q B TI q B DD q u
4
t t
1 t
2 t
t
Where: C
s electric consumption;
t
A s
constant; B
, B s response coefficients of consumption to explanatory variables;
1 2
TI s
total imports, 10
6
;
t
DD s
degree days, 8C; and
t
u s
residual term.
t
Ž .
The Durbin]Watson DW test is used to check for serial correlation of the Ž .
residuals. A first order autoregressive term given by Eq. 5 is added as a correction measure in case of rejection of the null hypothesis of no serial correlation.
Ž . u s ru
q e
5
t ty
1 t
Ž . In Eq. 5 u represents the unconditional residuals, e is the innovation in the
t t
disturbance and r is an estimate of the first-order autocorrelation coefficient. The resulting non-linear model is solved by applying a Marquardt non-linear least
squares method. Ž
. The second model of this study is the partial adjustment model PAM , which is
a dynamic model of the following form: Ž .
C s A q B TI q B DD q B C q
u 6
t 1
t 2
t 3
ty 1
t
Where C , is the previous value of electricity consumption and all other
ty 1
variables are, as defined above. The important feature of this model is its ability to capture the short-run adjustment in electrical energy to changes in TI and DD.
The use of the lagged-dependent variable as an independent variable necessitates the use of the Durbin-h test, instead of the DW, to check for serial correlation of
the residuals. The model is also estimated using OLS.
Given that regression models may produce spurious results when time series are Ž
. non-stationary Granger and Newbold, 1974 , the variables C, TI and DD are then
Ž .
tested for order of integration using the Dickey]Fuller DF and the augmented Ž
. Ž
. Dickey]Fuller ADF tests Nelson and Plosser, 1982 . The second step is to
determine whether the variables that are difference stationary have a long-run Ž
. relationship Engle and Granger, 1987 . Two cointegration tests are used in this
Ž .
Ž .
study, the Johansen 1988 and Engle and Yoo 1987 test methods. Johansen has Ž
. proposed a maximum likelihood vector autoregressive VAR procedure as a test
Ž .
of cointegration. A likelihood ratio LR , trace statistic, determines whether the hypothesis that a number of cointegrating relations at a certain significance level
can be accepted or rejected. Engle and Yoo also developed a two-stage procedure to test and estimate cointegration relationships. In the first stage, a cointegration
Ž . relationship is estimated using Eq. 4 . Variables are cointegrated if they all have a
Ž . Ž .
unit root, I 1 , and there exist a linear combination of these variables that is I 0 . The first sign indicating cointegration is that the residuals obtained from the
Ž . equation are stationary or I 0 . If the residuals are found to be stationary, the
G.E. Nasr et al. r Energy Economics 22 2000 627]640 633
residuals from the cointegration regression are used in the second stage as Ž
. estimates of the true disequilibrium errors in an error correction model ECM .
The ECM models the short-run dynamics within the framework of the long-term stable relationship established by the cointegration between variables. The ECM is
estimated using the first-order lag errors from the cointegration model with appropriate lags on the different variables as follows:
k l
m
Ž . DC s D q
D DC q
D DTI q
D D DD q
D u q
e 7
Ý Ý
Ý
t o
l i ty
1 2 i
tyi 3 i
tyi 4
tyi t
is 1
is is
Where D is the first-difference operator. The coefficient of the error, D ,
4
represents speed of adjustment toward the long-run equilibrium.
4. Results
