1 McMaster University 762 Econometrics The Impact of Oil Price Shocks on the Canadian Economy Using a Vector Autoregression Philip Colussi June 2013 Abstract: The effect of oil prices on macroeconomic indicators has been developing considerable amounts of research since the formation of the Organization of Petroleum Exporting Countries (OPEC). Theoretical models indicate that increased oil prices lead to inflation and economic downturns. This paper uses Canadian data from 1983 to 2012 in an attempt to explore the effects of oil prices using three different vector autoregressions. The findings suggest that oil price shocks create increased prices and wages. Conversely, the results cannot reproduce a recession but in fact demonstrate an economic boom in some cases.
Transcript
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McMaster University
762 Econometrics
The Impact of Oil Price Shocks on the Canadian Economy
Using a Vector Autoregression
Philip Colussi
June 2013
Abstract: The effect of oil prices on macroeconomic indicators has been developing considerable amounts
of research since the formation of the Organization of Petroleum Exporting Countries (OPEC). Theoretical
models indicate that increased oil prices lead to inflation and economic downturns. This paper uses
Canadian data from 1983 to 2012 in an attempt to explore the effects of oil prices using three different
vector autoregressions. The findings suggest that oil price shocks create increased prices and wages.
Conversely, the results cannot reproduce a recession but in fact demonstrate an economic boom in some
cases.
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1. Introduction
The main economic issue that is being studied is the effect of oil price increases on
Canadian macroeconomic factors. Since oil prices have significantly increased during the
last decade, it may affect the economic situations of the countries that are based on the
use of large quantities of oil. This could lead to an increase in the demand for oil, creating
a decrease in the supply of oil, which could generate an increase in extraction costs,
refining costs and an increase of oil importations.
One of the main factors of motivation stems from the fact that Canada is one of the
world’s largest suppliers of oil. Therefore, the price of oil related products have a
significant impact on the Canadian economy. This paper strives to provide some insight
on the effects of oil price shocks on this small open economy.
The first vector autoregression (VAR) model follows Burbidge and Harrison’s 1984
seminal paper in depth using Canadian macroeconomic data from 1983 to 2012. It is
found that the shocks have a zero effect on prices and production. The second model
excludes the interest rate variable and the shocks increase consumer prices by a small
amount and initially decrease wages before increasing above its steady state and then
returning back to its original value. Furthermore, the economy shows signs of industrial
production growth. The last model in this paper includes the unemployment rate and
exports of petroleum products. Although it generates an increase in prices, it displays
ambiguous effects on industrial production. The findings also display ambiguous effects
on the unemployment rate, which could be from two opposing effects in the labour
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market. Lastly, exports respond with a small increase at the time of the shock and then a
slowly decreasing trend back to its original value.
The organization of this paper will proceed as follows. Section 2 will discuss the
previous literature that this paper is closely based upon. Section 3 will go on to discuss
the data that was used in this research. Section 4 provides the methodology that was
preformed using Stata Statistical Software (Stata). Section 5 will perform impulse
response functions (IRFs) on the three different sets of data and their results will be
interpreted. Section 6 briefly summarizes the main findings of the paper.
2. Literature Review
In the past, theoretical models generally predicted that oil price shocks led to an
increase in wages and prices and decreased real output. This paper follows similar
empirical research preformed by John Burbidge and Alan Harrison in 1984 where they 1
used a seven variable vector autoregression (VAR) for 5 countries including Canada,
U.S., Japan, Germany and the United Kingdom. The seven variables used included the
price of oil, total industrial production from the rest of the world other than the country
involved, industrial production in the domestic economy, interest rate, currency and
domestic deposit, average hourly earnings in manufacturing and the consumer price
index. Since their monthly data ranges from years 1961 to 1982 this research will extend
these variables from 1983 to 2012 for Canada only.
This paper will refer to Burbidge, John, and Alan Harrison. "Testing for the Effects of 1
Oil-Price Rises Using Vector Autoregressions." as Burbidge and Harrison (1984).
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Impulse responses are then computed to show how the system of equations of each
country responds to a positive shock in the price of oil. From here, there is a historical
decomposition to determine what part of the cyclical fluctuations in prices and real output
came from the two different oil price shocks that occurred in 1973-1974 and 1979-1980.
One on their methods of interpreting the results is by performing Granger-causality
tests. Therefore, they developed an F-test where the null hypothesis that all lags of a
particular variable in each equation is zero. Moreover, a small p-value means that one
would reject the null that one variable does not cause another variable implying there is
Granger-causality. They find that the price of oil influences the consumer price index in
all countries except for Canada. Additionally, oil prices also influences wages in all
countries and affects international industrial production directly and domestic industrial
production indirectly.
Another way to look at the results is by focusing on the impulse responses from a one
standard deviation shock to PF. This method entails causal ordering of the variables in the
system, which will account for contemporaneous correlation. At one end of the ordering
an innovation is allowed to influence all variables contemporaneously, at the other
extreme, an innovation in the series influences only itself contemporaneously. PF is
placed at the top of the ordering to allow maximum opportunity to influence CPI and IP.
The IRFs show that from an oil price shock, oil prices and international industrial
production die out almost completely after three years. Furthermore, there is an increase
in wages and prices in all countries with the largest response coming from Canadian data.
In addition, the response of IP for Canada is quite minimal. Essentially, increases in the
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price of oil exert both the inflationary and to a lesser extent recessionary pressures so
frequently predicted by theoretical models of oil price shocks.
Recently, there has been more empirical research examining the effects of
macroeconomic variables on oil price shocks. Sahbi Farhani in 2012 , uses a VAR model 2
to evaluate the impact of oil price increases on economic growth in the U.S. His
motivation comes from the fact that oil constitutes an important source of energy and
represents an essential factor, which spurs the development in the economic sectors such
as electricity and transportation as well as noneconomic sectors such as military services.
The VAR model that he proposes tries to analyze the relationship between GDP
growth rate, oil price, inflation rate, unemployment rate and exports of petroleum
products. Therefore, it takes into account five variables represented by a series covering
quarterly data from 1960 to 2009. This paper adds in two new variables to the analysis
that was not previously mentioned in Burbidge and Harrison (1984). That is, the
unemployment rate and exports of petroleum products. This paper will add these two
variables into the system in replace of the interest rate and international industrial
production after the original VAR.
Farhani (2012) uses a Granger-causality test to conclude that GDP is caused only by
unemployment rate and exports of petroleum products and oil prices are only affected by
inflation. Therefore, he concludes that there is no direct relationship between GDP and oil
This paper will refer to Farhani, Sahbi. "Impact of Oil Price Increases on U.S. 2
Economic Growth: Causality Analysis and Study of the Weakening Effects in Relationship." as Farhani (2012).
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prices. Alternately, his IRFs show that a one standard deviation shock to the oil price
decreases the GDP growth rate 5 quarters after the shock.
3. Data Used
This section will define the first seven macroeconomic aggregates that are used in the
VAR analysis similar to the Burbidge and Harrison (1984) paper. All of the variables
below range from March 1983 to July 2012. First, oil prices (PF) were calculated using
real crude oil prices for West Texas Intermediate (WTI) in $CA per barrel by taking the
nominal crude price and dividing by the consumer price index. The source of the crude
oil price data is the World Bank website cited at the end of this paper. Figure 1 presents
an empirical time plot of PF in Canada from 1983 to 2012. Although the price stays
constant between 1986 and 1998, there is an increasing trend from 1999 to 2007 with
large price shock around 2008 and 2009.
The Canadian industrial production variable (IP) was taken from the OECD Stat
website. Third, the industrial production of OECD countries except for Canada (IPROW)
was calculated by taking the total OECD industrial production index from the OECD Stat
website and removing the Canadian component. This was added to capture demand
influences that might operate through changes in domestic exports. Fourth, the interest 3
rate (R) is the three-month yield on Canadian Treasury Bills retrieved from the Bank of
Canada. Fifth, currency outside banks and chartered bank deposits (M) also came from
the Bank of Canada. Sixth, average hourly earnings for hourly-paid employees including
Burbidge and Harrison (1984) p.4613
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overtime for selected industries (W) came from Statistics Canada . Lastly, the consumer 4
price index variable (CPI) was also from Statistics Canada. Figures 2-7 displays the time
plots of these variables. The primary objectives of interest are the effects of PF on CPI
and IP. 5
After the original analysis there will be two variables substituted for R and IPROW to
get further macroeconomic interpretation. The first added variable will be the official
unemployment rate (UNRATE) from Statistics Canada. The second added variable is the
export of crude oil (EXPOR), equivalent in thousands of cubic meters also coming from
Statistics Canada. The time plots of these two variables are displayed in Figures 8 and 9.
To hold consistency with the previous literature, it is important to note that before the
software analysis, all of these series except R and UNRATE were first logged.
Furthermore, seasonal frequencies were removed by twelfth-differencing, resulting in a
loss of one year of data. Again, this was done for all variables expect R and UNRATE.
From here, we can now discuss the details of the VAR process.
4. Methodology
There are many reasons why VAR was chosen as the main econometric model in this
research. VAR models generalize the one-variable autoregression models by allowing for
more than one evolving variable. Therefore, each of the 9 variables has an equation
Table 281-0031 was used for 1991-2012 data and Table 281-0004 was used for 4
1993-1990. Overlapping data for 1991-2000 was used to adjust table 281-0004 to be consistent with table 281-0031.
Burbidge and Harrison (1984) p.4615
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explaining its evolution based on its own lags and the lags of the other model’s variables.
A good feature of VAR is that given all the information produced by the model, there is
not much decision making required for the researcher. The only decisions to be made are
which variables to put in the model and the number of lags. The latter will be selected
using Stata later in this section.
Although VAR is the most preferred statistical model for our analysis, there are
disadvantages inherent in using this method. First, each equation has a large number of
right hand side variables and coefficients. This number equals the number of lags chosen
times the number of variables in the model. Second, because of the presence of
multicollinearity between the variables in the model, there are larger than average
standard errors present. Third, as a result of large standard errors, this leads to large IRF
confidence intervals. Fourth, the model assumes that a change in epsilon of one variable
at a certain time has no effect on other estimates even though they are allowed to be
correlated in the data. However, this problem can be avoided by imposing structure on
the VAR by causal ordering, which will be discussed more later in this section.
The next issue of focus is the number of lags to assign for the VAR. By definition,
this model must have the same number of lags for each of the variables that were listed
above. Burbidge and Harrison (1984) used a likelihood ratio test to find that little was
gained by allowing more than 4 lags in their model. Intuitively, choosing 4 or 8 makes
sense because quarterly data is being examined. For the data in this paper, lag length was
decided using the varwle and varlmar command in Stata.
The varwle command reports Wald tests of the hypothesis that the endogenous
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variables at the given lag are jointly zero for each equation and for all equations jointly.
Using the original variables from the Burbidge and Harrison (1984) paper, this section
only looks at the null hypothesis that all of the coefficients at lag length 4 and 8 equal
zero. Therefore, it will be testing 49 restrictions with 49 degrees of freedom. Tables 1 and
2 display the Stata output for lag lengths 4 and 8 respectively. The output indicates that
both lag lengths strongly reject the null hypothesis. Therefore, the remaining analysis will
follow Burbidge and Harrison (1984) and use 4 lags for this examination.
Another specification test looks at the presence of autocorrelation between the
variable equations in the VAR. The command varlmar tests the null hypothesis that there
is no autocorrelation in any of the 7 equations at a given lag with 7 degrees of freedom.
Table 3 of test statistics shows small P-values, which is a rejection of the null hypothesis.
Therefore, evidence of autocorrelation in the variables is present and one could impose
the possibility of added extra lags in future research.
The last part of this section will perform Granger causality tests between the
variables in question. The command vargranger will perform causality tests to know find
variables are caused by other variables. The null hypothesis of the rejection of the
causality test will be acceptable when the P-value is higher than 5% significance level.
Again, the relationship between PF on CPI and IP are the main points of interest here.
Our results indicate that CPI does in fact Granger cause PF. Table 4 displays the
P-values of the F-tests that all lags of CPI are zero for the dependent variable PF. The
value in the first row of the table indicates a rejection of the null hypothesis that all lags
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of CPI are zero. Therefore, we can conclude that CPI Granger-causes PF. This result goes
against the same test that was preformed in Burbidge and Harrison (1984). In their
analysis, they concluded that PF drives CPI in all countries except Canada. In other
words, they recorded a P-value that was larger than their chosen significance level.
However, in Farhani (2012), his test concludes that oil prices are in fact caused only by
inflation.
The fifth row of Table 4 gives the P-value of the null hypothesis that the lags of IP
are zero for the PF equation. This value may or may not reject the null hypothesis
depending on the significance value that is chosen. At the 5% significance level the P-
value reported is larger and we could not reject the null hypothesis. However, at the 10%
significance level this value would reject that IP does not Granger-cause PF. In Burbidge
and Harrison (1984), they report a relatively large P-value for the same Granger causality
test. Moreover, Farhani (2012) reports a rejection of the null hypothesis, suggesting no
evidence that IP drives PF at all.
5. Impulse Responses
Now that the methodology and specification tests have been discussed and
interpreted, we are ready to shock PF by one standard deviation so that we can analyze
the responses of the remaining variables. The impulse response functions help to describe
the impact of the exogenous variable on the endogeous variables after the shock and how
many periods must be passed to be completely ignored.
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Since it was shown that there may be correlation between variable disturbances, the
VAR shock to one variable affects the other variables at the same time. Hence, the
interpretation inherent in the partial derivative of ‘holding all other variables constant’ is
not plausible. Therefore, we must orthogonalize the disturbances via the Cholesky
decomposition. To do this, causal ordering of the variables is preformed before the
impulse responses similar to the Burbidge and Harrison (1984) paper. Accordingly, the
variable ordering inputted in Stata will be PF, IPROW, R, M, W, CPI, and IP. Intially, PF
will be shocked and the succeeding variables will be effected by the residual of the
previous shock. From here, the variable will respond with the impact of the shock
decreasing as one moves down the order.
It is also important to note that Burbidge and Harrison (1984), before constructing
IRFs, converted their VAR model into its vector moving-average representation. The
VAR model must first be converted in order to shock the model and this can be done
similar to converting a single autoregressive model into its moving-average
representation. However, this process does not have to be done in this paper because
Stata is able to do this automatically before shocking the variables in question.
Figure 10 shows the orthogonalized impulse response functions from a shock to PF
on the rest of the variables in the model. The graphs show that there are no responses
from any of the variables except for R. Counter-intuitively, the model is showing that a
shock to the oil price has zero effects on any macroeconomic variables apart from for the
interest rate, which increases by 0.5 standard deviations and then returns back to its
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steady state after 10 quarters. This does not have much economic meaning because the 6
other macroeconomic indicators should be responding to this type of shock. Therefore,
the next analysis will look at the same experiment minus the R variable.
Using the same process above less the interest rate variable provides intuitive results
that one can compare to the previous literature. Figure 11 provides panels of IRFs for
CPI, IP, IPROW, M and W. The results indicate that the largest effect comes from CPI.
The graph shows an increase in prices for about 3 quarters and then settles down to its
steady state value and decreases even further after 7 quarters. The results further indicate
that oil price shocks increase wages, domestic production and international production.
However, these increases are not as pronounced as the CPI variable. Lastly, the response
of currency and deposits is quite small.
Compared to the results reported by Burbidge and Harrison (1984), there are many
similarities and differences. In both experiments, an increase in the price of oil exerts
inflationary attributes that are frequently predicted by models of oil price shocks. The
price of oil and inflation are often seen as being connected in a cause and effect
relationship. As oil prices move up or down, inflation follows in the same direction. A
reason for this is that that oil is a major input in the economy and it is used in critical
activities such as fueling transportation and heating homes. Therefore, if input costs rise,
so should the cost of end products as some or all of the cost is passed over to the
consumer.
Note that the grey shaded area represents a 95% confidence interval.6
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Conversely, the previous literature, including Farhani (2012) was able to reproduce
recessionary features of the economy with a decrease in production while the current
analysis exhibited an economic boom. Furthermore, both of the models produced
relatively weak effects on currency and bank deposits. In the next section, the last
experiment will entail with two new substituted variables.
The model will now introduce the unemployment rate and exports of petroleum
products to examine their behaviour from shocks to the price of oil. It is necessary to
choose the an ordering for the variables in the system, since the method of
orthogonalization involves the assignment of contemporaneous correlation to a particular
series. Therefore, the 5 variable VAR model will now be ordered as PF, EXPOR,
UNRATE, CPI, and IP to allow for contemporaneous correlation. Next, we will interpret
the IRFs with the newly added variables.
Figure 12 shows a panel of a one standard deviation shock to PF for the remaining
4 variables. Similar to the previous exercise, an increase in the price of oil causes
inflationary effects. As a result the CPI variable increases immediately at the time of the
shock for 3 quarters and remains above its steady state value for another 2 quarters before
slowly decreasing back to its initial value. Moreover, industrial production seems
unaffected from this shock. However, the graph displays a very wide confidence interval,
which could suggest that an oil price shock has increasingly ambiguous effects on IP as
time moves on. In addition, EXPOR shows a small increase at the time of the shock and
then a slowly decreasing trend back to its original value. This suggests that when oil
prices increase there is an increase in the exports of petroleum products that slowly
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decreases as the economy finds alternative energy substitutes. Lastly, UNRATE displays
zero effects on a shock to PF with a very wide confidence interval. Intuitively, this could
be suggesting that the unemployment rate is unaffected from oil price increases. This
could come from the fact that there are two opposing effects that could occur in the job
market from oil price increases. When the price of oil increases, more labour is needed to
extract the more valuable product. On the other hand, the demand for oil decreases and
less extraction is needed as buyers move towards alternative substitutes.
Compared to the previous literature, there is still an inflationary effect from an
increase in oil prices. However, there are still no recessionary effects from an oil price
shock that we see in theoretical models or in Burbidge and Harrison (1984). Then again,
this exercise did not provide an increase in industrial production that was seen in the
previous IRFs. This model was able to replicate the small increase in exports of
petroleum products and then a slow decrease back to steady state that was also produced
in Farhani (2012). In addition, similar to the previous literature, the unemployment rate is
unresponsive to oil price shocks in the economy.
6. Conclusion
Oil constitutes an important source of energy and represents an essential factor that
prompts development in economic sectors and noneconomic sectors. This paper closely
follows empirical research from Burbidge and Harrison (1983) and Farhani (2012) to
analyze the effects on oil price shocks on macroeconomic variables. Canadian data from
1983 to 2012 is used in an attempt to explore the effects of oil prices shocks from three
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different VAR models. The main findings of this analysis suggest that oil price shocks
create increased prices and wages. Conversely, the results cannot reproduce a recession
but in fact demonstrate an economic boom in some cases.
There are many other avenues available to explore in this subject. For example, using
a different set of lags could provide a different set of results. However, Burbidge and
Harrison found that this did not affect their overall findings. Another area for future
research would be the selection of variables in the VAR. For instance, this research
briefly looked at the effects of exports of petroleum products from oil price increases.
Conversely, one can add in imports of petroleum products to study the connection
between the two. In addition, one can also add government spending into the equation to
observe how the trade balance alters with respect to oil price shocks. Of course, one can
also study the cross-country implications by replicating this experiment using a different
country altogether.
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References
Farhani, Sahbi. "Impact of Oil Price Increases on U.S. Economic Growth: Causality
Analysis and Study of the Weakening Effects in Relationship." International
Journal of Energy Economics and Policy 2 (2012): 108-122.
Burbidge, John, and Alan Harrison. "Testing for the Effects of Oil-Price Rises Using
Vector Autoregressions." International Economic Review 25.2 (1984).
