Capstone Paper - Final version

Oil Price and Norwegian Foreign
Exchange Rate
Empirical analysis: 1999-2015
MATHIAS FJELDAVLIE MUNKEJORD
PACIFIC LUTHERAN UNIVERSITY
ECON 499 – FALL 2015

Oil Price and Norwegian FX Rate Mathias Fjeldavlie Munkejord 2
Introduction:
Oil is the most important natural resource in the global economy for both individuals and
businesses. The recent decrease in oil prices has significantly impacted the global economy, and
some countries that depend on petroleum exports hurt more than others and has experienced
currency depreciation as a result of the recent oil price plunge. Norway is a good example of this
reality, where the Norwegian citizens complain because the Norwegian Krone is at its weakest level
against the U.S. Dollar since 2002, which makes American products almost 30% more expensive
compared to the previous decade. For my capstone project I want to look at the relationship
between the oil price and the Norwegian exchange rate as it has never been more relevant seen
from a Norwegian economic perspective. The oil sector is facing tough times with substantial job
cutbacks and with 2016 being the first year ever where the Norwegian government has to sell
assets in order to finance next years’ national budget.
I will look at historical data from 1999-2015 to see if I can find a significant correlation
between the oil price and the currency value in a commodity-dependent economy such as Norway.
The specific question I will address is: “How does fluctuations in oil price correlate with the
Norwegian Krone´s performance in the foreign exchange market.” My dataset will include oil
prices, exchange rates between the Krone USD, GBP, SEK (Swedish Krone), and corresponding
output data. The Norwegian Krone was pegged against the Deutsche Mark and other major
currencies until 1999(Kleivset, 2012), and therefore data before 1999 will be excluded from my
research, and I will only focus on the period where the Krone has been a floating currency.
Based on theory and econometric analysis developed by the Norwegian economist Akram
(2004), I will examine the relationship. Even though Akram (2004) only could find a significant
relationship between oil prices and exchange rates when oil price was below 14USD per barrel,

newer data could lead to a different result. If a statistically significant relationship between oil
prices and the Norwegian Krone can be established, it will generate an interesting basis for
discussing how monetary policy makers in Norway can approach the issue of a downswing in the
economy caused by fluctuations in a commodity price. As of today over 30,000 jobs have been cut
in the Norwegian oil sector alone, and it will be interesting to see if this reality will disperse into
other sectors in the Norwegian economy. After many years with substantial growth in the oil
sector, Norway is now facing a new situation where the greatest contributing sector to the national
GDP has to be restructured.
2.1 – Norwegian economic development:
The Norwegian economy lagged far behind neighboring countries for a long time
period after they claimed independence from Sweden in 1905. However, over the following
100 years, the economic growth in Norway was nothing short of incredible. If we compare
the per capita GDP growth in Norway to the other countries used in my research, we see
that Norway was lagging behind until it discovered oil in 1969. Graph 1 presents the
substantial economic boom that was generated by the increase in oil production, which
shaped the Norwegian economy to be one of the wealthiest countries in the world. As of
2010 the Norwegian economy was the second best performing in the world, only behind
Luxembourg on per capita GDP rankings.
Graph 1: Real Capita GDP 1965-2010 for Norway, United States, Sweden & United Kingdom

Value added in Norway, measured as change in Domestic Product (GDP) has
increased by about 90 percent through the 1980s and 1990s, while by comparing with the
the EU, they experienced around 50 per cent growth, and in the USA about 70 percent over
the same period. The strong growth in Norway must be viewed, among other factors, in
connection with the development of the oil industry after the initial discovery of oil on the
Norwegian shelf at the end of the 1960s. From 1980 to 2000, oil production in terms of
number of barrels produced more than tippled, and Norway is now one of the world´s
biggest exporters of oil (Statistics Norway). At the same time the oil sectors demand for
goods and services from the mainland economy has grown substantially.
In a paper issued by the Norwegian government, Sigrid Russwurm (2001) explains
some of the most important underlying factors for the economic success Norway has
experienced in the modern era. She concludes that an important factor behind the economic
development in Norway has been due to the rapid growth in the oil sector. Erling Steigum
(2010) shares Russwurm´s (2001) view on Norwegian economic development. He also adds
that; “Wealth is a combination of luck and ability”. Even though the economy has grown
rapidly since the discovery of oil, it has also faced some issues regarding high unemployment
rates and inflation during the 1980´s and 1990´s. the Norwegian government in consultation
with central policy makers has been smart and proactive in Norway. This resulted in the
establishment of the Norwegian oil fund in the mid 1990´s. The first deposit was made to the
fund in 1996, and since then has the oil fund grown to the largest of its kind. The Norwegian
oil fund was in 2015 worth around $900 billion, which is placed in different financial and
non-financial assets around the world in order to spread risk.

Norway has an open economy, with a per capita foreign trade that is one of the
highest in the world, in which 77 percent of the exports go to EU countries. The Nordic
countries, Great Britain and Germany are Norway's most important trading partners mostly
because Great Britain and Germany are major markets for Norwegian oil and gas (Statistics
Norway). Sweden is the country that Norway imports the most from. Exports of goods and
services accounted for 46 percent of the GDP in 2000, while imports accounted 31 percent.
Exports of oil and gas constituted 46 per cent of total exports (Statistics Norway). United
Kingdom, United States and Sweden are all countries closely tied to Norway, and, thus, will
be the countries I will focus on in my research and analysis.
2.2 – Commodity Dependent Economies:
Some countries depend on a commodity in their economy. Norway is one of them,
with a significant percentage of GDP coming from the oil-producing sector. A commodity-
dependent economy can be defined as a country in which the economy is highly dependent
on profits from producing and exporting this commodity. Hence, for a country that depends
on a commodity the price and value of that commodity will have great effects on the
financial results from that sector and on the national budget. Céspedes and Velasco (2012)
investigate how fluctuations in commodity prices affect macroeconomic performance, and
conclude that fluctuations in commodity prices for a country that depend on this commodity
often experience macroeconomic volatility. The monetary policy regime can play a crucial
role in terms of controlling the volatility related to commodity price fluctuations. Céspedes
and Velasco (2012) find that more flexible exchange rate regimes such as inflation-targeting,

tend to help reduce macroeconomic volatility in commodity exporting countries. We see
that Norway in 1999 implemented a flexible exchange rate regime with inflation-targeting
after a period with pegged exchange rate to the major traded currencies. This might have
been implemented in order to secure stable returns from the substantial increase in oil
production in Norway at the time.
With the oil production alone accounting for almost one fourth of the total value
creation in Norway as of 2012 it is easy to understand that a decrease in the oil price could
result in substantial decrease in terms of profits in the economy. (Norges Bank)
The recent drop in global oil prices has already started to raise fear in the Norwegian oil
sector, and projections show that oil prices could stay at relatively low levels for a longer
time period than first anticipated (Statistics Norway). Moderate economic performance
combined with low oil prices could mean that Norway are facing an uncertain future.
Nordbø and Stensland (2015) address this issue in their recent working paper, where they
claimthat a decrease in oil price could result in substantial decreases in revenue for the oil-
producing sector, but that the effects also will disperse into all companies delivering
products or services to the oil sector. As of 2014 around 300,000 jobs were related to the oil
activities in Norway, which is a 100% increase since 2000. (Statistics Norway)
With these facts stated we can derive the conclusion that Norway´s economy is
highly dependent on the commodity it produces, and further I will discuss how the exchange
rate also is relevant in this issue.

2.3 – Exchange rate determination:
When working with exchange rates, it is important to understand how exchange
rates are calculated. Two models have been considered for my research, namely the
monetary approach and the asset approach for exchange rate determination. The monetary
approach uses domestic and foreign monetary variables such as money supply and output,
assuming that the Purchasing Power Parity (PPP) condition holds. The asset approach is built
on the uncovered interest rate parity (UIP), and is more focused on interest rates in relation
to the exchange rate.
The monetary approach is one of the most widely used models over the past decade.
Meese & Rogoff (1988) were one of the first to estimate and test the robustness of this
approach for determining exchange rates. Their research presented that both real exchange
rate and real interest rates had the theoretically anticipated signs, which means that as the
real interest rate increases one could expect the real exchange rate to appreciate. However,
the relationships did not turn out statistically significant, so the model they used would not
be a good reference for forecasting real exchange rates.
After the publication of Meese & Rogoff (1988), a lot of research has been done on
the topic of exchange rates, and how they respond to different variables in the economy.
The importance of exchange rates is especially visible when looking at countries that engage
in international trade because a depreciation of the home currency could impact the
domestic balance of payments. The domestic current account is especially vulnerable in such
a situation because importing foreign goods is more expensive than before.

The monetary approach is widely used, and Rapach & Wohar (2002) utilized this
approach with data from 14 countries and related monetary data. They found that
monetary variables could explain fluctuations in the exchange rate, a result that is supported
by Akram (2004) in his working paper for the Norwegian Central Bank. My approach will
deviate a bit from the models used in these papers, as I will include oil price as a monetary
variable, and focus mostly on the impact this variable has on the exchange rate. Farooq
Akram has been studying the relationship between the exchange rate and oil price
extensively, and in 2000 he published a paper that found a significant relationship when the
oil price was below USD 14 and falling. He concluded that the relationship was non-linear,
and as the price of oil increased the oil price had an insignificant impact on the exchange
rate.
The interest rate differential and its impact on exchange rates is another way of
examining the relationships between economic variables and fluctuations in the value of
currencies. This approach has been taken by Norwegian economists such as Bjørk, Mork &
Uppstad (1998) and Kloster, Lokshall & Røisland (2007). They all found that the interest rate
differential was a key factor behind both appreciations and depreciations in the exchange
rate. They also found a significant tendency for a long- term decrease in the oil price to
result in a depreciation of the Norwegian krone exchange rate.
For commodity dependent economies, such as Norway with its significant oil
reserves, the exchange rate is especially important since fluctuations in the exchange rate
will impact the national economy in a significant way. Korhonen and Juurikkala (2007)
explore the factors behind equilibrium exchange rates in oil-dependent countries. Their
research presents another way to look for the relationship between exported commodity

and the real exchange rate. The real exchange rate (REER) can be defined as the nominal
exchange rate adjusted for price level differences between countries, where an increase in
the real exchange rate indicates a depreciated currency. Their econometric analysis of REER
in oil-producing economies finds a statistically significant positive effect on the real
exchange rate of these countries. The real exchange rate depends on macroeconomic
variables, and their conclusion states that the oil price drives many of these variables. Even
though the relationship between oil prices and exchange rates in oil producing economies
seems to be significant, no one has been able to present a clear linear relationship.
However, Korhonen and Juurikkala´s (2007) conclusion is supported by the findings of
Zalduendo (2006) for estimates in Venezuela, and Kalcheva and Oomes (2007) for estimates
in Russia.
2.4 – Oil Price:
The oil price is an important indicator of economic well-being in both the Norwegian
economy and the global economy in general. Lizardo and Mollick (2010) claims that the oil
price has to be blamed for economic recessions, financial crisis, increasing unemployment
rates and high inflation. Support of these claims can be found by looking at seminal papers
addressing these issues. Hamilton (1983) researched how oil was related to the American
economy and how oil price shocks have explained recessions where he found the price of oil
to be a significant contributing factor. Burbridge & Harrison (1984) did research on the
correlation between oil prices and price levels in the U.S., and concluded that the increases
in oil price was a driving factor behind the increased U.S. price level during 1970´s. Their
research also claims that price of oil influences the level of industrial production in countries

like U.S. and U.K. Research by Gisser and Goodwin (1986) and Louganini (1986) supports the
conclusions by Hamilton, Burbridge & Harrison, and further adds that the oil price has both
inflationary effects, and to some extent can explain variations in unemployment.
As explained above the petroleum prices has been researched extensively in
connection with important economic factors. The question that needs to be raised now is to
what extent the oil price can explain fluctuations in the value of currencies. Lizardo & Mollick
(2010) emphasizes that the link between currencies and oil price did not received a lot of
attention in published economic research, but some important studies exist and will be
discussed in the following section.
Golub (1983) and Krugman (1980) did both find that oil- exporting countries could
expect an appreciation of their currency when the oil price increased. Blomberg & Harris
(1995) argue that since crude oil is traded in USD regardless of where in the world it is
traded, a depreciation of the USD would increase purchasing power for international traders
(players) and increase the demand for oil. This would again lead to an increase in the price of
oil based on simple supply and demand theory.
Lizardo & Mollick (2010) provided evidence on how an increase in oil price led to
exchange rate appreciation for oil-exporting countries such as Russia, Canada and Mexico,
while it resulted in exchange rate depreciation for oil-importing countries such as Japan.
Further Lizardo & Mollick (2010) found that countries that had no exposure to oil trading,
such as Great Britain, experienced an appreciation of their exchange rate on the USD when
the oil price increased.
Norwegian economists also addressed the importance of oil price on financial
markets and currency valuation. Bjørk, Mork & Uppstad (1998) approached the issue by

using co-integration analysis combined with an error correction model in order to capture
long term effects of permanent oil price changes and the consequences of short term
fluctuations in the oil market. The monetary policy in Norway at that time was focusing on
keeping a relatively stable exchange rate, hence did they also focused on the interest rate
differential as an instrument used by the Norwegian central bank to stabilize the Norwegian
krone. Farooq Akram (2000) claims that a number of arguments can be put forward to
explain why the nominal exchange rate of an oil producing country may appreciate when oil
price rises, and vice versa. He especially emphasizes that higher oil prices could increase the
demand for the currency of an oil exporting country, resulting in an appreciation of that
currency relative to other currencies. These claims are supported by the economist
discussed earlier in this section.
The recent drop in oil prices is not an uncommon phenomenon when looking at the
past 15 years. Historical data tells us that oil prices has been trough multiple booms and
busts. The recent plunge in the global oil price can be explained by multiple factors, and
analysts agree that the oil market as of today is oversupplied. One of the greatest reasons
for this reality is OPECs unwillingness to stabilize the market and cut back on the production.
It does not help that china as the worlds largest oil importer might be in a way worse state
than predicted, and that Russia still keeps their pumps going despite the economic downturn
the country is facing. Graph 2 present the fluctuations in the oil price over the last 15 years.
Graph 2: Oil Price from 2000-2015 (source: research.stlousfed.org)

My literature review reveals that a lot of research has been done on explaining how
monetary variables relates to fluctuations in financial sectors of the economy. Existing
results seem to have a certain common feature, namely that monetary variables and
monetary policy does influence the exchange rate. There are multiple approaches that can
be used in determining the exchange rate, but the one I will focus on is the monetary
approach, where the most important trading partners for Norway will be included as well as
the oil price. If a significant relationship can be established between the two, this paper can
contribute in explaining the recent depreciation of the Norwegian Krone.
3.1 – Model:
My research is focused on whether I can find a correlation between the oil price and
the Norwegian exchange rate. In order to do so I will use the monetary approach to
exchange rate. The monetary approach to exchange rates has been the dominant macro
model after the collapse of the Bretton Woods system, and is also a fairly easy approach to
understand. It conceives the exchange rate as the relative price of two monies, where the
price is turned into a function of the relative supply and demand for those monies (Lizardo &
Mollick, 2010).
A few assumptions have to hold under the monetary approach. The time period is
assumed to be long enough for full price adjustment and that the absolute purchasing power
parity (PPP) holds. The concept of PPP explains the theory that any given commodity will
have the same price anywhere in the world when measured in the same currency. This is
also referred to as the law of one price (LOOP), a theory many believe operates if markets

are working well both nationally and internationally (Appleyard, Field &Cobb 2010). For
example, if a loaf of bread costs $4,50 in the U.S and £3 in the United Kingdom, then the
exchange rate should be equal to $4,50 divided by £3, or $1,50/£. The PPP can also be
generalized over many goods and be written as:
𝑷𝑷𝑷 𝑨𝒃𝒔𝒐𝒍𝒖𝒕𝒆 =
𝑷𝒓𝒊𝒄𝒆 𝑳𝒆𝒗𝒆𝒍 𝑼𝑺
𝑷𝒓𝒊𝒄𝒆 𝑳𝒆𝒗𝒆𝒍 𝑼𝑲
(𝟏)
The monetary approach focuses on monetary variables such as money supply and
national output. The equation for money supply in the home country, given that PPP holds
can be written:
𝑴𝑺 𝒂 = 𝑲 𝒂 𝑷 𝒂 𝒀 𝒂 (𝟐)
Where MSa= money supply in country A, Pa= Price level in country A, Ya= Real income
in country A, and Ka= a constant term including all other influences on money demand in
country A in addition to Pa and Ya.
Since the absolute version of PPP is assumed to hold in this model, we end up with
an equation that has good explanatory capabilities between the monetary variables and the
exchange rate, E. Equation (3) explains the impact on changes in monetary variables for both
the home country and the foreign country:
𝑬 =
𝑲 𝒃 𝒀 𝒃 𝑴𝑺 𝒂
𝑲 𝒂 𝒀 𝒂 𝑴𝑺 𝒃
(𝟑)
In equation 3, the variable E represent the exchange rate, K reflects sensitivity of real
money demand to income. Where a $5 increase in real income will raise real money demand
by K*5 real dollars. K should be less then one in most cases. Y is the real income in country A
and B, and MS is the money supply in country A and B respectively. The model above

explains a specific relationship between the variables and the exchange rate E. If money
supply in the home country increases by 10%, holding all else constant, the exchange rate
will increase by 10%. A 10% increase in the exchange rate tells us that it takes 10% more
units of currency A to buy one unit of currency B. An increase in the exchange rate A/B will
therefore be a depreciation of currency A. The relationship works backwards for money
supply in the foreign country. A 10% increase in money supply in country B, ceteris paribus,
will lead to a proportional fall in E, or an appreciation of the home currency.
From these relationships we can se that the monetary approach is focused on
changes in relative money supply, and we can also derive that if real income in country A
increases, their currency will appreciate. So a faster growing economy will experience an
appreciation of the currency. After some rearranging, we derive a simpler expression of the
monetary approach that can be written as:
𝑬 =
𝑴𝑺 𝑵𝑶𝑹
𝑲 𝑵𝑶𝑹 ∗ 𝑮𝑫𝑷 𝑵𝑶𝑹
∗
𝑲 𝑼𝑺 ∗ 𝑮𝑫𝑷 𝑼𝑺
𝑴𝑺 𝑼𝑺
∗ 𝑶𝒊𝒍 𝑷𝒓𝒊𝒄𝒆 (𝟒)
Where NOR and US stand for Norway and United States, respectively.
Equation (4) explains the absolute relationship between the variables included in the
monetary approach. For my research I will look at the money supply and output differentials
for Norway and the foreign countries, hence I will adjust the model further by taking the
natural logarithm of all the variables including the oil price that was added in equation (4) by
multiplication, an approach used by Lizardo & Mollick (2010). When taking the natural
logarithm of equation (4) the following equation is generated:
𝒆 = 𝒍𝒏𝑴𝑺 𝑵𝑶𝑹 − ( 𝒍𝒏𝑲 𝑵𝑶𝑹 + 𝒍𝒏𝑮𝑫𝑷 𝑵𝑶𝑹) + ( 𝒍𝒏𝑲 𝑼𝑺 + 𝒍𝒏𝑮𝑫𝑷 𝑼𝑺) − 𝒍𝒏𝑴𝑺 𝑼𝑺 + 𝒍𝒐𝒈𝑶𝒊𝒍 𝒑𝒓𝒊𝒄𝒆 (𝟓)

The variable K is a constant rather than a time variant in this model, which means
that it cannot be included in the estimation equation as I work with time series data in this
capstone paper. When K is dropped from the model, and the model is rearranged further in
order to estimate the money supply and output differential for home and foreign country,
estimation equation (6) is generated. Equation (6) is a replica of the one used by by Lizardo
& Mollick (2010). Their composite model was also aimed to test the relationship my paper
intends to, however, their dataset consisted of a different time period as well as different
countries.
The benefit of having the estimation equation in logarithmic form is that the results
can be interpreted as percentage change, such as a 1% increase in the oil price will
correspond in a certain percentage change in the exchange rate, e. When rearranging
equation (5) with the aim of estimating the relationship between money supply differential,
output differential and oil price on the foreign exchange rate we derive the following
estimation equation:
𝒆 = 𝜷𝟎 + 𝜷𝟏( 𝒎 − 𝒎 ∗) + 𝜷𝟐( 𝒚 − 𝒚 ∗) + 𝜷𝟑𝒍𝒐𝒈𝑶𝒊𝒍+ 𝒖 (𝟔)
Equation (6) presents (m-m*) as the money supply differential in logarithmic form,
(y-y*) is the output differential in logarithmic form, logOil is the logarithm of the real oil
price and e is the logarithm of the nominal exchange rate. For the money supply I will use
M1 for each country, and for the output I will use industrial production. The oil price is the
price for one barrel of crude oil as a monthly average. As the price of oil increases, and the
price of oil is denominated in US dollars, the US money supply will increase relative to the

Norwegian money supply. This will, as explained above, result in a depreciation of the US
dollar relative to the Norwegian krone. Lizardo & Mollick (2010) found that the 𝛽3 should be
positive for oil exporting countries since an increase in e means a depreciation of the USD
relative to the foreign currency. Hence, more USD has to be paid for each barrel of oil.
However, in my research I am using the Norwegian Krone as the benchmark currency, hence
I am expecting a negative coefficient on the oil price as this would mean that an increase in
oil price would result in an appreciation of the Norwegian Krone relative to foreign
currencies.
By looking at graph 3 one could say that the NOK/USD exchange rate and the oil price
seems to have a perfectly negative relationship, where a low oil price is consistent with a
high exchange rate, and vice versa.
Conclusions from earlier studies of the relationship are split, but I hope that a
significant relationship can be established for the relationship by utilizing the monetary
approach and also incorporate the oil price.
Graph 3: Oil Price and NOK/USD exchange rate 1999-2015 (research.stlousfed.org)

3.2: DATA
The data I will use in order to test my model is gathered from St. Louis Federal
Reserve (also called FRED) and involve the money supply from Norway, United States,
Sweden, Denmark and Germany(euro-zone) together with industrial production from the
same countries and for the oil price I will use the price per barrel of crude oil as mentioned
earlier. Before I start to run regressions for analytical purposes I have to modify the variables
slightly in order to run the regression as stated in the model section.
I will have to generate new variables that represents the logarithmic differential
between the money supply and industrial output for Norway and the foreign countries.
(m-m*) and (y-y*) respectively. I will also generate logarithmic variables for the exchange
rates and the oil price. I do this in order to get more normally distributed variables, and it
also enables me to see the percentage change impact on the different variables. When
expressed in logarithmic form, a 1% change in for example the money supply differential will
increase or decrease the exchange rate by a certain percentage given by the coefficient on
the specific variable.
The following table presents some descriptive statistics from my time series dataset.
All variables are observed monthly from 1999-2015:
Data retrieved from: https://research.stlouisfed.org
Variable Observations: Mean: Std. Dev: Minimum: Maximum:
NOK/GBP 186 11.02444 1.44675 8.607 13.6376
NOK/SEK 186 87.60742 4.436414 79.11 99.49
NOK/USD 186 6.696975 1.12064 5.0546 9.3613
Oil Price 186 63.5071 29.44867 19.39 133.88

By looking at the minimum and maximum values from the descriptive statistics table
above, we can observe that both exchange rates and oil price has experienced major
fluctuations over the 15-year time period I am focusing on in my paper. For additional
descriptive statistics, see the appendix.
3.3 – Initial Results:
When I ran the first regression of the monetary approach, these initial results were
generated for the relationship of NOK/USD, NOK/SEK and NOK/GBP exchange rates and the
relevant independent variables:
Variables: NOK/USD NOK/SEK NOK/GBP
Money Supply -0,1130645 -0,2466841 0,4521856
Output -0,1044759 -0,205999 -0,1111683
Oil Price -0,2691326 -0,0265537 -0,1873946
Constant 5,215184 4,421817 3,286315
R2 0,8226 0,1395 0,683
Adj. R2 0,8197 0,1293 0,6778
Observations 186 186 186
The initial results suggest that there is a negative relationship between oil price and
all the exchange rates. A one percent increase in the oil price result in a .269 percent
decrease in NOK/USD exchange rate which by economic theory explains an appreciation of
the Norwegian krone against the US dollar. All variables were statistically significant at all
relevant levels except the output variable for NOK/USD and NOK/GBP exchange rate.
All variables except the money supply differential for USD and SEK exhibit the signs
expected by economic theory. The absolute monetary model presented earlier in this paper

explains that when a country´s money supply increases relative to a foreign country one
should expect a depreciation of the home country currency in the long run. My initial results
do not support this theory; one explanation could be that the PPP doesn’t hold because of
barriers to trade such as transaction cost etc. The R2 explains how much of the variation in
the exchange rate are covered by the variables in the regression. A R-squared(R2) of 0.8226
tells us that 82.26% of the variation in the NOK/USD exchange rate is a result of the
independent variables in my model, an acceptable level in econometric analysis.
3.4 – Testing for Unit Root, Cointegration, Heteroskedasticity & Serial Correlation
Since I am working with time series data there are some tests that have to be
conducted as time series data tend to exhibit time trends. If variables trend together it can
reveal underlying relationships, and the results will end up being biased. By performing a
few tests that are common for time series data, these trends can be removed. For the sake
of the length of this paper will only test results from the NOK/USD result be discussed, the
same tests for the other exchange rates can be found in the appendix. The following section
explains why the tests are important, how to conduct them and the related results.
Test for stationary variables:
A time series is stationary if its mean and variance are constant over time. The main
reason why it is important to know whether a time series is stationary or non-stationary
before regression analysis is that there is danger of obtaining apparently significant
regression results from unrelated data when non-stationary time series are used in
regression analysis. Such regressions are said to be spurious. One of the most commonly

used methods is the Augmented Dickey-Fuller (ADF) test for unit root. Where the H0= unit
root. If I fail to reject the H0 hypothesis the variable will be stationary, called an AR (1)
variable. The null hypothesis (H0) will be rejected if the test statistic is greater (in
absolute value) than the critical values related to the 1,5 and 10% level of significance. The
following table presents the results that where generated from the ADF-test on the
NOK/USD relationship:
The results from the Dickey-Fuller (ADF) test tells me that I fail to reject the null
hypothesis for all the variables in this regression since the test statistics are smaller (in
absolute value) than the critical values. This is true for all the variables. The following
table presents the ADF test results for the first differenced variables:
When taking the first difference of the variables, the result commonly rejects the null
hypothesis of unit root since the test statistic is greater than the critical values. This result
Variable Test statistic 1% critical value 5% critical value 10% critical value
Log NOK/USD FX -1.190 -3.482 -2.884 -2.574
Log MS (NOR-US) -0.931 -3.482 -2.884 -2.574
Log IP (NOR-US) -1.610 -3.482 -2.884 -2.574
log Oil Price -1.868 -3.482 -2.884 -2.574
Variable Test statistic
1% Critical
value
5% critical
value
10% critical
value
Log NOK/USD
FX
-8.656 -2.589 -1.950 -1.616
Log MS (NOR-
US)
-14.500 -2.589 -1.950 -1.616
Log IP (NOR-US) -18.625 -2.589 -1.950 -1.616
Log Oil Price -8.656 -2.589 -1.950 -1.616

tells me that all variables are stationary in first differenced form. Economic theory calls these
kind of variables integrated of order one, or I (1).
Since the variables only become stationary when first differenced, tests for
cointegration will be necessary. With cointegration present in the regression it will cause a
spurious regression. Cointegration could be explained simply by using the example of a
drunk man and a dog. If the dog walks away from its owner, the owner tends to follow the
same direction. So cointegration explains how two variables are moving in the same
direction, resulting (as mentioned) in a spurious regression that will not be any good for
forecasting or analyzing. If the first differenced variables don’t show signs of cointegration it
will still be possible to analyze the regression without being afraid of biased results.
A test that is commonly used for cointegration testing is the Engel-Granger test. The
first step of this test is to run the basic regression and then generate a separate variable for
the error term followed by a ADF test on the error term. The results of the tests are listed
below.
1. The first step is toperform the originalregression.
2. Then I generated the residual fromtheregression andperformedtheEngel-Granger test ontheresiduals to test for
cointegrationin the regression
NOK/USD coefficient STD. Error T-Value P-value # Obs. R^2 Adj. R^2
MS (NOR-US) -0.1130645 0.396538 -2.85 0.005 184 0.8226 0.8197
IP (NOR-US) -0.1044759 0.1019793 -1.02 0.307
Oil Price -0.2691326 0.249354 -10.79 0.000
Variable Coefficient Std. Error T-statistic P-Value
L1. -0.0959049 0.0290141 -3.31 0.001
LD -0.1894631 0.0722732 2.62 0.009

Here we look at the p-value for the lagged residual variable L1. When the p-value
approach is used we will be able to reject the H0= cointegration if the p-value is smaller than
the 5% t-critical level. Here we see that the p-value is 0.001 which mean that I reject the null
hypothesis, and conclude that the residuals don’t show signs of cointegration. Since the
variables were stationary in first differenced form, and now turned out not to be
cointegrated I will be able to use the regression results for analytical purposes. In addition to
unit root and cointegration, I also need to check for heteroskedasticity and serial correlation.
When error terms from different time periods are correlated, we say that the error
term is serially correlated. Serial correlation occurs in time-series when the errors associated
with a given time period carry over into future time periods. For example, if we are
predicting exchange rate growth, an overestimate in one year is likely to lead to
overestimates in succeeding years. Serial correlation will not affect the unbiasedness or
consistency of regression estimators, but it does affect their efficiency. With positive serial
correlation, the estimators of standard errors will be smaller than the true errors. This will
lead to the conclusion that the parameter estimators are more precise than they really are.
The Breusch-Godfrey test is used to test for serial correlation in a time series
regression. When I tested the NOK/USD regression for serial correlation in the residuals the
following results were generated:
Breusch-Godfrey test for Serial Correlation
Lags Chi-Squared DF Prob. > Chi-Squared
1 156,407 1 0.0000
H0 = no serial correlation

What I am looking for here is the P-value related to the Chi-squared distribution.
Here we have H0= no serial correlation, and with a probability of 0.000 I reject the null
hypothesis since the p-value is smaller than any critical value of interest (1%, 5%, 10%). By
rejecting the H0 I conclude that my time series regression does show signs of serial
correlation. Serial correlation can be corrected by using the Newey-West estimation
method. This method corrects for the serial correlation and allows us to correctly interpret
the regression results. Another feature of the Newey-West function is that it corrects for
heteroskedasticity. Heteroskedasticity is often not the main issue in time series regression
analysis, but it is important to be aware of what it means and how it is relevant. If a variable
involves heteroskedasticity it basically means that there are variations in the error terms
over the data set. In order for the F-test and T-test to be justified in a regression,
heteroskedasticity can’t be present. So with the Newey-West results I do not have to be
concerned about heteroskedasticity.
The results generated from the Newey-West estimations for all three exchange rate
relationships are explained in the following table:
Variable NOK/USD P-value NOK/SEK P-value NOK/GBP P-value
Money
Supply -0.1130645 0.000 -0.2466841 0.000 0.4521856 0.000
Output -0.1144759 0.234 -0.205999 0.008 -0.1111683 0.616
Oil Price -0.2691326 0.000 -0.0265537 0.066 -0.1873946 0.000
Constant
5.215184 0.000 4.421817 0.000 3.286315 0.000

The results generated from the Newey-West estimation yields stronger evidence of
statistical significance than the initial regression discussed earlier in this paper. As we can
see from the table above, industrial production is still not statistically significant at any level
for both USD and GBP exchange rate, but the p-value has decreased from .307 to .234 for
USD, so the corrections I have done made it more fitted to the regression. The money supply
and oil price variables are still statistically significant at all levels, and therefore results from
these can be analyzed. The coefficient on the logarithmic oil price variable has the same
negative sign as in the initial model, and a 1% increase in the oil price will according to my
monetary approach regression correspond in a .269% appreciation of the Norwegian krone
against the U.S. Dollar. After conducting tests for the most common issues with time series, I
am able to rely on the results generated above, and can conclude that the oil price has a
significantly negative impact on all the exchange rates used in my research.
4.1 Conclusion:
This paper was initially aimed at empirically testing how fluctuations in the oil price
impacts value of the Norwegian Krone, measured against important trading partner’s
currencies. Through econometric analysis I was able to generate a result that can be used for
analytical purposes. I succeeded in finding a statistically significant negative relationship
between the oil price and the Norwegian exchange rate, and the negative relationship is
supported by multiple economists discussed in the literature review section of this capstone
paper. The negative relationship suggested by my results highlights the importance of being
able to find alternative revenue streams in countries that heavily depends on a commodity,

as volatility always will be present. 25% of total value creation in Norway is generated in the
petroleum sector, and when knowing that oil price is a main driver behind fluctuations in the
value of Norwegian currency, policy makers in Norway are facing a new situation where the
focus should be aimed at preventing economic downswings caused by decreasing oil prices
in the future. The substantial cutbacks from the petroleum sector we witness today could be
the beginning of a new era for the Norwegian economy. One reason is that the oil-era one
day will be over, and secondly because the world economy jointly focuses more heavily on
renewable energy resources.
Further research on the political aspect linked to the recent downswing in Norwegian
petroleum sector would be an interesting approach to compliment the research presented
in this capstone paper.

Work Referenced
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Econometrics Journal, Vol.7, No. 2, 476-504
Akram, Qaisar. (2000) “When does the oil price affect the Norwegian exchange
rate?”. Research Department, Norges Bank.
Blomberg, S. B. & Harris, E. S. (1995). “The commodity-consumer price connection:
fact or fable”? Economic Policy Review. 1
Bjørk, Line & Mork, A. Knut & Uppstad, H. Bernt. (1998). “Påvirkes kursen på norske
kroner av verdensprisen på råolje?”. Norsk Økonomisk Tidsskrift. Vol.1-33.
Bjørnland, H.C. (2009). “Oil price shocks and stock market booms in an oil exporting
country”. Scottish Journal of Political Economy. 232-254
Burbridge, J. & Harrison, A. (1984) “Testing for the effects of oil-price rises using vector
autoregression. International Economic Review, Vol. 25, 459-484.
Gisser, M. & Goodwin, T.H. (1986). “Crude oil and the macroeconomy: Tests of some
popular notions. Journal of Money, Credit, and Banking. Vol 18, 95-103.
Golub, S. S.”Oil prices and exchange rates”. The Economic Journal, (1983) 576-593.
Hamilton, J. D. (1983): Oil and the macroeconomy since World War II. Journal of
Political Economy, 21, 228-248.
Hill, C. R & Griffiths, E. William& Lim, C. Guay. (2011). “Principles of Econometrics”.
Fourth Edition. John Wiley & Sons Inc. (p.335-395)
Kalcheva, Katerina and Oomes, Nienke (2007), “Dutch Disease: Does Russia Have the
Symptoms?” forthcoming BOFIT Discussion Paper 6/2007.
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documentation paper on Norges Bank and monetary policy”. Norges Bank´s bicentenary project.
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Occasional paper. Norges Bank. Vol. 31.
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Lizardo, R. A. & Mollick, A. V. (2010). “Oil price fluctuations and US dollar exchange
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Working paper nr 4, Norges Bank.
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Data retrieved from: https://research.stlouisfed.org

Appendix:
Test for Serial Correlation NOK/SEK:
Breusch-
Godfrey
LM
test for
autocorrelation
lags(p) chi2 df Prob > chi2
1 161.421 1 0.0000
H0: no serial correlation
Test for Serial Correlation NOK/GBP:
Breusch-
Godfrey
LM
test for
autocorrelation
lags(p) chi2 df Prob > chi2
1 166.433 1 0.0000
H0: no serial correlation
 Both exchange rates exhibit signs of serial correlation as we reject the H0 of no serial
correlation in both cases. Hence further action, as discussed in the results section of
this paper, will be necessary in order to generate unbiased results that can be trusted
and analyzed.
Test for stationarity – (NOK/SEK) & (NOK/GBP)
Variable Test Statistic
1% Critical
Value 5% Critical Value 10% Critical Value
log NOK/SEK FX -2.071 -3.482 -2.884 -2.574
Log MS NOK/SEK -1.382 -3.482 -2.884 -2.574
Log IP NOK/SEK -3.012 -3.482 -2.884 -2.574
Log Oil Price SWE -1.868 -3.482 -2.884 -2.574
Log NOK/GBP FX -1.235 -3.482 -2.884 -2.574
Log MS NOK/GBP -1.874 -3.482 -2.884 -2.574
Log IP NOK/GBP -5.191 -3.482 -2.884 -2.574
Log Oil Price UK -1.868 -3.482 -2.884 -2.574
 When testing for stationarity only output variable in both exchange rates are
significant (greater than any critical value), so need to take first difference and test
again.

Test for stationarity on First Differenced Variables:
Variable Test Statistic
1% Critical
Value 5% Critical Value
10% Critical
Value
log NOK/SEK FX -10.755 -2.589 -1.95 -1.616
Log MS
NOK/SEK -17.508 -2.589 -1.95 -1.616
Log IP NOK/SEK -17.797 -2.589 -1.95 -1.616
Log Oil Price
SWE -9.713 -2.589 -1.95 -1.616
Log NOK/GBP FX -11.038 -2.589 -1.95 -1.616
Log MS
NOK/GBP -16.636 -2.589 -1.95 -1.616
Log IP NOK/GBP -18.896 -2.589 -1.95 -1.616
Log Oil Price UK -9.713 -2.589 -1.95 -1.616
 When first differenced, all variables exhibit test statistics greater than the respective
1% critical values. This means that all variables are AR (1). So both exchange rates
will be suited for running the Newey West estimation as presented in the results
section of this paper.
Additional Descriptive statistics
Variable Obs Mean Std. Dev. Min Max
Money Supply
(NOR-US)
186 19.74673 .1797134 19.48142 20.1181
Money Supply
(NOR-UK)
186 -.3031217 .0685066 -.4296932 -.1399994
Money Supply
(NOR-SWE)
186 -.6875366 .0792685 -.8112049 -.5208645
Output (NOR-
US)
186 .0991488 .1114652 -.1401052 .3063626
Output (NOR-
UK)
186 .0311902 .0439245 -.1039081 .1381326
Output
(NOR-SWE)
186 .0627297 .09581 -.1467066 .2557259
log NOK/GBP 186 2.391394 .133058 2.152576 2.612831
log NOK/SEK 186 4.471603 .0502665 4.370839 4.600057
log NOK(USD 186 1.888734 .1583778 1.620299 2.236584
log Oil Price 186 4.025661 .5251023 2.964757 4.896944

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