Emmanuel Barnedo Presentor

lizardgossypibomaΔιαχείριση

28 Οκτ 2013 (πριν από 3 χρόνια και 7 μήνες)

81 εμφανίσεις

Emmanuel
Barnedo

Presentor


Anal yzi ng t he Macr oeconomi c Ef f ect s of Oi l Pr i ce Changes i n t he Phi l i ppi nes


Introduction


Methodology


Conceptual Framework


Analytical Framework


Results and Discussion


Summary and Conclusion


Policy Implications


Limitations of the Study


“Crude

oil

and

various

petroleum

product

are

crucial

in

literally

fueling

the

economy

of

a

nation


If

blood

is

the

lifeline

of

our

body,

then

oil

is

the

lifeline

of

the

economy



-
Anakpawis

Rep. Crispin Beltran (2008)

Anal yzi ng t he Macr oeconomi c Ef f ect s of Oi l Pr i ce Changes i n t he Phi l i ppi nes

Anal yzi ng t he Macr oeconomi c Ef f ect s of Oi l Pr i ce Changes i n t he Phi l i ppi nes


During the oil
crises
in the
1970s,
many
countries,
experienced recession
(
Lee and Chui,
2009;
Barsky

and
Kilian
, 2001).


In the 2000s, the Philippines proved once
more that it was indeed vulnerable to the
sustained increase in oil prices.


The real Gross Domestic Product (GDP) had
declined considerably in 2007 until 2009 where
oil prices had reached its peak in 2008
.

Anal yzi ng t he Macr oeconomi c Ef f ect s of Oi l Pr i ce Changes i n t he Phi l i ppi nes


The main objective of this study is to analyze
how changes in oil prices affect crude oil
consumption and some key macroeconomic
indicators in the Philippines
.

Anal yzi ng t he Macr oeconomi c Ef f ect s of Oi l Pr i ce Changes i n t he Phi l i ppi nes


Specifically, the study aimed to accomplish
the following:


To determine the effects of world and local oil
price changes in oil consumption and key
macroeconomic indicators, such as inflation
rate, investment, employment and real Gross
Domestic Product;


To examine the time of disruption brought
about by the world and local oil price oil price
shocks;

Anal yzi ng t he Macr oeconomi c Ef f ect s of Oi l Pr i ce Changes i n t he Phi l i ppi nes


Specifically, the study aimed to accomplish
the following
: (
cont
…)


To compare the effects of these shocks in
terms of the pattern of disruption on the
domestic oil consumption and the key
macroeconomic indicators; and


Lastly, to provide policy implications to
lessen the impact of oil price changes.


Conceptual

Framework


Test

of

Stationarity


Vector

Autoregressive

(VAR)

Model


Impulse

Response

Model


Sources

of

Data

Let:


World oil price changes be
D.DBOIL;


Local oil price changes be
D.DSOIL;


inflation be
INF
;


total oil consumption
PPS
;


investment be
FCF;


total employment be
EMP;
and


Gross
Domestic Product
be
GDP
.



The
Augmented Dickey Fuller (ADF) test is used.

𝑌
𝑡
=

𝑖
𝑌
𝑡

1
+

𝑖


𝑌
𝑡

𝑛
𝑛
𝑡
=
1
+

𝑖




is the differencing operator;

𝑖

is the white error term;
and


and


are the coefficients of the one period
lagged value
𝑌
𝑡

1

and

𝑌
𝑡

𝑛
, respectively, where


𝑌
𝑡

𝑛
𝑛
𝑡
=
1
=

𝑌
𝑡

1
+

𝑌
𝑡

2
+

+

𝑌
𝑡

𝑛

(are higher
order autocorrelation) such that
n

is the optimum lag
length determined using sequential search method.


𝑌
𝑡
=

𝑖
𝑌
𝑡

1
+

𝑖


𝑌
𝑡

𝑛
𝑛
𝑡
=
1
+

𝑖

Null Hypothesis:


= 1 (
𝑌
𝑡

is non
-
stationary or there is a unit root)

Alternative Hypothesis:


≠ 1 (
𝑌
𝑡

is stationary or there is no unit root)




It follows
the same asymptotic distribution as the Dickey
-
Fuller test so the same critical values can be used.


Thus
, if the computed absolute value of the tau statistic
(|τ|) exceeds the Mackinnon critical tau values, reject the
null hypothesis that



= 1, the series is stationary.
Otherwise, fail to reject the null hypothesis, in such case,
the series is
non
-
stationary
(Gujarati, 2004).


The augmented Dickey
-
Fuller tests for the variables under
study are
:



.
 𝐼
𝑡
=

1

.
 𝐼
𝑡

1
+

1



.
 𝐼
𝑡

𝑛
𝑛
𝑡
=
1
+

1



.
𝑆 𝐼
𝑡
=

2

.
𝑆 𝐼
𝑡

1
+

2



.
𝑆 𝐼
𝑡

𝑛
𝑛
𝑡
=
1
+

2


𝐼
𝑡
=

3
𝐼
𝑡

1
+

3


𝐼
𝑡

𝑛
𝑛
𝑡
=
1
+

3


𝑆
𝑡
=

4
𝑆
𝑡

1
+

4


𝑆
𝑡

𝑛
𝑛
𝑡
=
1
+

4



𝑡
=

5

𝑡

1
+

5



𝑡

𝑛
𝑛
𝑡
=
1
+

5



𝑡
=

6

𝑡

1
+

6



𝑡

𝑛
𝑛
𝑡
=
1
+

6



𝑡
=

7

𝑡

1
+

7



𝑡

𝑛
𝑛
𝑡
=
1
+

7




If the variable is found to be nonstationary in level form, it
must be
stationarized thru differencing/detrending.


The first VAR model used in the study with
p
-
lag is given by:

𝒀

=

+


𝒀



+


𝒀



+

+


𝒀



+




Where:

𝑌
𝑡
=
(

.
 𝐼
𝑡
,

𝐼
𝑡
,

𝑆
𝑡
,

𝑡
,

𝑡
,


𝑡
) denotes (nx1
) vector
of (
stationary/stationarized) time
variables

series ;



is (nx1) vector of drift terms
,



𝑖

is (
nxn
) coefficient matrix and



𝑡
is (nx1) vector of white noise
error
term; and

t
=1,2,…,T; p=maximum no. of lags

*No. of lags were determined using
Akaike

Information Criterion

-
A second VAR model was similarly specified for the local oil price
changes by
replacing the world oil price changes (

.
 𝐼
𝑡
)
with local
oil
price changes (

.
𝑆 𝐼
𝑡
)


VAR Model with world oil price changes

d.
 𝒊

=


+

𝜽
𝒊

.
 𝒊


𝒊
+

𝝐
𝒊
𝒊


𝒊

𝒊
=

+

𝒊
=



𝒊



𝒊

𝒊
=

+




𝒊



𝒊

𝒊
=

+

𝝉
𝒊



𝒊

𝒊
=

+


𝒊



𝒊

𝒊
=

+






𝑖
𝑡
=

2
+

𝜃
2𝑖

.
 𝑖
𝑡

𝑖
𝑝
𝑖
=
1
+

𝜖
2𝑖
𝑖
𝑡

𝑖
𝑝
𝑖
=
1
+


2𝑖
𝑠
𝑡

𝑖
𝑝
𝑖
=
1
+




2𝑖

𝑡

𝑖
𝑝
𝑖
=
1
+

𝜏
2𝑖

𝑡

𝑖
𝑝
𝑖
=
1
+


2𝑖

𝑡

𝑖
𝑝
𝑖
=
1
+

2𝑡




𝑠
𝑡
=

3
+

𝜃
3𝑖

.
 𝑖
𝑡

𝑖
𝑝
𝑖
=
1
+

𝜖
3𝑖
𝑖
𝑡

𝑖
𝑝
𝑖
=
1
+


3𝑖
𝑠
𝑡

𝑖
𝑝
𝑖
=
1
+




3𝑖

𝑡

𝑖
𝑝
𝑖
=
1
+

𝜏
3𝑖

𝑡

𝑖
𝑝
𝑖
=
1
+


3𝑖

𝑡

𝑖
𝑝
𝑖
=
1
+

3𝑡





𝑡
=

4
+

𝜃
4𝑖

.
 𝑖
𝑡

𝑖
𝑝
𝑖
=
1
+

𝜖
4𝑖
𝑖
𝑡

𝑖
𝑝
𝑖
=
1
+



4𝑖
𝑠
𝑡

𝑖
𝑝
𝑖
=
1
+



4𝑖

𝑡

𝑖
𝑝
𝑖
=
1
+

𝜏
4𝑖

𝑡

𝑖
𝑝
𝑖
=
1
+


4𝑖

𝑡

𝑖
𝑝
𝑖
=
1
+

4𝑡





𝑡
=

5
+

𝜃
5𝑖

.
 𝑖
𝑡

𝑖
𝑝
𝑖
=
1
+

𝜖
5𝑖
𝑖
𝑡

𝑖
𝑝
𝑖
=
1
+


5𝑖
𝑠
𝑡

𝑖
𝑝
𝑖
=
1
+




5𝑖

𝑡

𝑖
𝑝
𝑖
=
1
+

𝜏
5𝑖

𝑡

𝑖
𝑝
𝑖
=
1
+


5𝑖

𝑡

𝑖
𝑝
𝑖
=
1
+

5𝑡





𝑡
=

6
+

𝜃
6𝑖

.
 𝑖
𝑡

𝑖
𝑝
𝑖
=
1
+

𝜖
6𝑖
𝑖
𝑡

𝑖
𝑝
𝑖
=
1
+


6𝑖
𝑠
𝑡

𝑖
𝑝
𝑖
=
1
+




6𝑖

𝑡

𝑖
𝑝
𝑖
=
1
+

𝜏
6𝑖

𝑡

𝑖
𝑝
𝑖
=
1
+


6𝑖

𝑡

𝑖
𝑝
𝑖
=
1
+

6𝑡




It traces
the responsiveness of the dependent variable in
the VAR system to a unit shock in error terms over time.


But the error term must be nonautocorrelated (and
normally distributed) so that shocks can be represented
independently. Thus, non
-
autocorrelation and
normality of the distribution must be ensured first.



The impulse response functions for this study are given
as follows:

𝑖
𝑡
=

1
+

0
𝑤
1𝑡
+

1
𝑤

1𝑡

1
+

2
𝑤

1𝑡

2
+

+

𝑘
𝑤
1𝑡

𝑘
+
𝜇
1



𝑡
=

2
+

0
𝑤

1𝑡
+

1
𝑤
1𝑡

1
+

2
𝑤
1𝑡

2
+

+

𝑘
𝑤
1𝑡

𝑘
+
𝜇
2



𝑡
=

3
+
𝜖
0
𝑤
1𝑡
+
𝜖
1
𝑤
1𝑡

1
+
𝜖
2
𝑤
1𝑡

2
+

+
𝜖
𝑘
𝑤
1𝑡

𝑘
+
𝜇
3



𝑡
=

4
+
𝜏
0
𝑤

1𝑡
+
𝜏
1
𝑤
1𝑡

1
+
𝜏
2
𝑤
1𝑡

2
+

+
𝜏
𝑘
𝑤
1𝑡

𝑘
+
𝜇
4



𝑡
=

5
+
𝜃
0
𝑤
1𝑡
+
𝜃
1
𝑤

1𝑡

1
+
𝜃
2
𝑤
1𝑡

2
+

+
𝜃
𝑘
𝑤
1𝑡

𝑘
+
𝜇
5



The

effects

of

such

shock

upon

the

VAR

model

over

time

are

graphed

up

to

(
k
-
1
)

lags

with

its

confidence

band
.



A

second

set

of

IRFs

was

also

specified

for

local

oil

price

changes

whose

error

terms

were

represented

by


1
𝑡
,



1
𝑡

1
,



1
𝑡

2
,



,


1
𝑡

𝑘
.


The study covered the period 1991Q1
-

2010Q4. The
variables included in the study were:


oil prices of Dubai
Fateh

(DBOIL)
-

IMF


pump prices for diesel oil (DSOIL)
-

DOE


inflation rate (INF)
-

NSO


petroleum products sales (PPS)
-

DOE


fixed capital formation (FCF)
-

NSCB


total employed people (EMP)
-

NSO


Gross Domestic Product (GDP)
-

NSCB


There were some adjustments and estimations
made, such as:


oil prices of Dubai
Fateh

(DBOIL
);

and


quarterly data for petroleum product
sales


There were some adjustments and estimations
made, such as:


oil prices of Dubai
Fateh

(DBOIL
);


the first difference (
𝑌
𝑡

𝑌
𝑡

1
) of DBOIL and DSLOIL
was taken/used to represent change in world oil
price (D.DBOIL) and local oil price (D.DSLOIL); and


quarterly data for petroleum product
sales





Variables

Augmented Dickey
-
Fuller tests

Level Form

Optimal
Lag
length
a



t
-
stat



p
-
value





d.dboil
b

1

-
10.505

0.0000*

stationary

d.dsloil
b

2

-
5.982

0.0000*

stationary

PPS

2

-
2.684

0.0769

nonstationary

INF

2

-
6.143

0.0000*

stationary

EMP

2

-
0.488

0.8943

nonstationary

FCF

1

-
2.603

0.9814

nonstationary

GDP

2

0.398

0.9814

nonstationary

a
Optimal lag length was determined through sequential search method.

*
represents significant at 5% level.


a

Optimal lag length was determined through sequential search method.

b

Adjusted using first difference (
𝑌
𝑡

𝑌
𝑡

1
) to represent

𝑌
.

c

Adjusted using fourth seasonal differencing (
𝑌
𝑡

𝑌
𝑡

4
).

d

Adjusted using detrending approach.

* represents significant at 5% level.





Variables

Augmented Dickey
-
Fuller tests

Adjusted Variables

Optimal
Lag
length
a



t
-
stat



p
-
value





s4.pps
c

1

-
3.522

0.0075*

stationary

s4.fcf
c

1

-
3.487

0.0083*

stationary

detrend_emp
d

1

-
3.156

0.0227*

stationary

s4.gdp
c

(with drift)

1

-
2.511

0.0072*

stationary


According
to the
Akaike

Information Criterion
(AIC
),
the optimal lag length for
the first VAR
model was
three (3
) while the second was two.



The initial reaction of inflation was positive that may be
attributed to the direct and indirect effect(s) of an oil price
shock.

Effect(s) of World Oil Price
Shock

Effect(s) of
Local
Oil Price
Shock

Effect(s) of World Oil Price
Shock

Effect(s) of Local Oil Price
Shock


Crude oil was said to be relatively inelastic. However, the
significant decline in oil consumption also signalled that it was
becoming less inelastic over time

Effect(s) of World Oil Price
Shock

Effect(s) of Local Oil Price
Shock


The initial increase
in investment on energy
-
efficient capital
may be relatively higher compared to the decrease (or
postponement) in the investment on other capital.

Effect(s) of World Oil Price
Shock

Effect(s) of
Local
Oil Price
Shock

The slow recovery of
employment may
be
attributed to: (1) the
industry
-
specific
skills
of labor (
Loungani

1986) and (2) increase
in investment .

Effect(s) of
World

Oil Price
Shock

Effect(s) of
Local
Oil Price
Shock

The increase in GDP may be attributed
to the
increase in
investment.
Such increase may have reduce the negative
impact on energy
-
intensive sectors, such as transport


Conclusion


Policy

Implication(s)


Limitation(s)

of

the

Study

Variables

A

B

C

D

E

F

G

H

inf

+

-

0.304

0.089

14
Quarters


0.246


0.037


11

Quarters

s
4
.
pps

-


n.a.

273.138

n.a
.


6
Quarters

151


n.a.


9

Quarters

s
4
.
fcf

+

-

575.947

469.056

15
Quarters


49


219.636

12

Quarters


detrend

_emp

-

n.a.


40.089


n.a.

16
Quarters

31.211



n.a
.


14

Quarters

s
4
.
gdp

+

-


661.222

622.406

16
Quarters


150


359.379

20

Quarters

A
-

Initial

Response

to

the

Oil

Price

Shock

(the

same

for

both)

B
-

Second

Response

to

the

Oil

Price

Shock

(the

same

for

both)

C
-

Magnitude

of

the

Initial

response

(Max

Value)

(World

Oil

Price

Changes)

D
-

Magnitude

of

the

Second

Response

(Max

Value)

(World

Oil

Price

Changes)

E
-

Length

of

Disruption

of

the

World

Oil

Price

Shock

F
-

Magnitude

of

the

Initial

response

(Max

Value)

(Local

Oil

Price

Changes)

G
-

Magnitude

of

the

Second

Response

(Max

Value)

(Local

Oil

Price

Changes)

H
-

Length

of

Disruption

of

the

Local

Oil

Price

Shock


Anal yzi ng t he Macr oeconomi c Ef f ect s of Oi l Pr i ce Changes i n t he Phi l i ppi nes


Although oil price shocks were found to be
disruptive, regulating the oil downstream
industry could create more distortions.


Since the said shock is temporary, the
government can implement short
-
term
intervention, catered specifically to
particular sector.

Anal yzi ng t he Macr oeconomi c Ef f ect s of Oi l Pr i ce Changes i n t he Phi l i ppi nes


As a long term solution, the
government should promote the
investment on energy
-
efficient
technology/capital and the
production of indigenous energy
sources.

Anal yzi ng t he Macr oeconomi c Ef f ect s of Oi l Pr i ce Changes i n t he Phi l i ppi nes


Non
-
availability of quarterly data for
oil consumption


The use of diesel oil price

Thank you very much!

End of presentation