The effect of current low oil prices on the real GDP
of the Netherlands
Author: L. Hesse
Student number: 10265759 Thesis Supervisor: N. Ciurilă Finish date: 29/06/2015
University of Amsterdam Faculty of Economics and Business
BSc Economics & Business Bachelor Specialization Economics
2 PREFACE AND ACKNOWLEDGEMENTS
After taking two classes in macroeconomics, dr. K. Vermeylen inspired me to write my thesis on a macroeconomic subject. With the price of oil reaching surprisingly low levels and having multiple relatives working in the oil industry, the subject of my thesis was almost handed to me.
I would like to use the opportunity to thank my father and mother, dr. J.Hesse and M.Brummelkamp for proofreading my thesis. Furthermore, I would like to thank H.Hesse and A. Odenkirchen who supported me during the writing process of this thesis and helped proofreading as well.
Copyright Statement
The author has copyright of this thesis, which represents part of the author’s study program while at the Faculty of Economics and Business at the University of Amsterdam (FEB UvA). The views stated therein are those of the author and not necessarily those of the FEB UvA.
Non-plagiarism Statement
By submitting this thesis the author declares to have written this thesis completely by himself/herself, and not to have used sources other than the ones mentioned. All sources used, quotes and citations that were literally taken from publications, or that were in close accordance with the meaning of those publications, are indicated as such.
3 Table of Contents
Page
PREFACE AND ACKNOWLEDGEMENTS………...……….…2
TABLE OF CONTENTS………..….…….…3
ABSTRACT………...4
Introduction………..………...5
Chapter 1: Theoretical Analysis……….6
CHAPTER 2.1: Literature study………..…...6
CHAPTER 2.1.1: Linear models………..…...6
CHAPTER 2.1.1: Non-linear models………...8
CHAPTER 2.2: Case for the Netherlands………...………….….………..10
CHAPTER 2.3: Previous studies the Netherlands………..……….11
CHAPTER 3: Data………...………..…....13
CHAPTER 4: Methodology………...………..…...14
CHAPTER 5: Results and Analysis……….…………..………...15
CHAPTER 6: Concluding remarks……….19
CHAPTER 6.1: Conclusion………..………...19
CHAPTER 6.2: Discussion………..20
4 Abstract
In this thesis, the effect of the current low oil prices on the real GDP of the Netherlands is studied. To study this effect, two models are used. The first model that is studied is a linear model with five independent variables. The following five independent variables are used: real growth rate of the oil price, real growth rate GDP of Germany, growth real effective exchange rate and the lagged real growth rate of the GDP of Germany and the Netherlands. The second model studied is a non-linear model. The non-linear model uses the same independent variables with one modification. The variable that
measures the real growth rate of the oil price is replaced by an interaction term. This interaction term is created by multiplying the real growth rate of the oil price with a dummy variable that assumes value one (1) when the real growth rate of the oil price is positive and zero (0) if otherwise. This interaction term allows the possible asymmetric reaction of the real GDP of the Netherlands after an oil price shock to be tested. The results show a positive relationship between the real growth of the oil price and the real growth of the real GDP of the Netherlands in the first model. This positive relationship is also found between the interaction term and the growth of the real GDP of the Netherlands in the second model.
5 Introduction
According to the U.S Energy Information Administration, North Sea Brent Crude oil is considered a benchmark in the oil industry for light sweet crude (2014). Using the historical oil prices from the same source, it shows that on June 19th 2014 the price per barrel of Brent crude oil was just over US$115.
Directly following June 19th, the price of oil started to decline. Just before the end of 2014 the price of a barrel of Brent Crude oil dropped to just over $60.
According to The Economist (2014), the decrease in the oil price is found to be multi-factorial: weak economic activity causes lower oil demand, the increased efficiency in the use of oil and the growing demand for other types of fuel. Normally when the demand for oil is low, the OPEC decreases its production which stabilizes the oil price. After the OPEC meeting on November 27th 2014, no
statements were made about cutting down the daily production (Kent, 2014). Since the OPEC owns 81% of the world's crude oil reserves, the cartel has a large influence on the oil price (Opec.org, 2014). Currently, the oversupply is lowering the oil price even further. Due to the oversupply, countries that depend on oil imports can now obtain oil at lower prices.
Olah (2005) mentions that even though oil and gas are a nonrenewable source of energy, these sources will maintain their position as the most used form of energy until its depletion. Due to this fact, the price of oil is important to economies all around the world.
In this thesis, the effect of the current low oil prices on the growth of the real GDP of the
Netherlands is studied. Moreover, it is interesting to observe this effect since the country is considered a net importer of oil and net exporter of natural gas (Eia.gov, 2014).
In the first chapter, the theoretical analysis is discussed. Firstly, previous articles are discussed. Brown and Yücel (1999) and Hamilton (1983) used a linear model to study the effect of oil price shocks on the GDP. Jiménez-Rodríguez and Sánchez (2006) and Lardic and Mignon (2005) used a asymmetric model. Furthermore, the driving forces that influence the real price of oil is studied (Kilian, 2009). Secondly, the case of the Netherlands is discussed. The domestic oil demand and supply is studied. Thirdly, Lardic and Mignon (2005) have investigated the various effects of oil price shocks in several European countries, including the Netherlands. Using this model, a prediction of the change in the real GDP of the Netherlands after a decrease in the oil price is made. The outcome is then compared with the predictions made by the Centraal Planbureau (CPB) and De Nederlandsche Bank (DNB). In the second chapter, the data used in this thesis is presented. In the third chapter, the method of investigation is further elaborated. In this thesis, two models are discussed. The first model that is
6 tested, is a linear model with five independent variables: real growth rate of the oil price, real growth rate GDP of Germany, growth real effective exchange rate and the lagged real growth rate of the GDP of Germany and the Netherlands. The second model is a non-linear model. Although this model uses the same variables as the linear model, the variable that measures the real growth of the oil price is multiplied with a dummy variable. This dummy variable assumes value one (1) when the real growth rate of the oil price is positive and zero (0) If otherwise. In the third chapter, the results are discussed. In the fourth chapter, the concluding remarks are given.
2.1 Literature study
The effects of oil price shocks on the GDP of various countries has been discussed using two different models. Hamilton (1983) and Brown and Yücel (1999) used a linear model to estimate change of the GDP after an oil price shock. A linear model implies that an oil price increase by X% would have an equal but opposite effect on the GDP compared with an X% decrease of the oil price. In contrast with the previous articles mentioned, Lardic and Mignon (2005) and Jiménez-Rodríguez and Sánchez (2006) used a different approach. In these studies, a non-linear model was used. Using a non-linear model assumes an asymmetric effect of oil price shocks on the GDP. Firstly, the articles by Hamilton (1983) and Brown and Yücel (1999) will be analyzed. Secondly, the non-linear models of Jiménez-Rodríguez and Sánchez (2006) and Lardic and Mignon (2005) will be analyzed subsequently. Kilian’s (2009) paper will be studied to disentangle the oil price and research the effect of changing oil prices on the real GDP of the U.S.
2.1.1 Linear models
Hamilton (1983) studied the potential relationship between recessions and preceding spikes in the oil price in the U.S. According to the author, between World War II and 1972, all but one recession was preceded by a large increase in the oil price. The following three hypotheses were set (1983, p.230):
1. The correlation between the recessions and the oil price is a historical coincidence.
2. The correlation is the result of a third explanatory variable that caused the increase in the oil price and the recessions.
3. At least some of the recessions were partly caused by an exogenous increase in the oil price. To study the role of the oil price, Hamilton used a six-variable system. This system was used by Sims (1980) to represent macroeconomic reality. The following variables were used: real GNP,
7 unemployment, implicit price deflator for nonfarm business income, hourly compensation per worker, import prices and a single series M1. The M1 variable is added as a representation of the financial sector.
Increased oil prices found between 1948 and 1972 seemed to be followed by reductions of the real GNP growth (Hamilton, 1983, p.237). This could not have been anticipated by changes in output, prices or the money supply. The statistical regularity observed is unlikely to have resulted from random correlation between the series. Furthermore, the author could not find any indication of a third
explanatory variable that caused the correlation between the oil price and the real GNP. Based on these findings the first and the second hypothesis were rejected.
Brown and Yücel (1999) studied how oil price shocks move through the transmission channels of the U.S economy and affect the real GDP and price level. This effect was observed while keeping the federal funds rate constant and when allowed to float. According to Bernanke, Gertler and Watson (1997), the real GDP of the U.S responds differently after an oil price shock if the federal funds rate is kept constant or when allowed to float.
Brown and Yücel (1999, p.17) explained how the oil price affects the real GDP of the U.S. Higher energy prices resulting from an increase in the oil price will cause a shift in the production function and thereby causing a decreased output. When the output decreases, ceteris paribus, this causes an excess demand of goods and will increase the interest rate. The resulting decrease in output and increase of the interest rate will result in a decreased demand for real cash balances. Assuming a nominal quantity of money, the price level will increase. To study the effect of oil price shocks on the real GDP, a vector autoregressive (VAR) model was used using seven variables: real GDP, a commodity price index, the GDP deflator, oil prices, the federal funds rate, and short- and long-term interest rates. To investigate the dynamic effects of oil price shocks on the GDP of the U.S, impulse response functions were used (1999, p.18).
While allowing the federal funds rate to float, Brown and Yücel (1999, p.21) concluded that an oil price shock causes a decline in the real GDP and increases the federal funds rate, interest rates and price level. The nominal GDP remains relatively constant since the decrease of the real GDP is of similar magnitude as the increase of the deflator. Compared to a floating federal funds rate, keeping the federal funds rate at a fixed level after an oil price shock will result in higher nominal GDP, real GDP, commodity prices and price level. Since a linear relationship is used between the oil price and the real GDP, a decrease in the oil price would result in the opposite effect described above but with the same magnitude.
8 2.1.2 Non-linear models
The use of a non-linear model was earlier described Lardic and Mignon (2005). In this study, the long-term relationship between the oil price and the GDP of 12 European countries, including the
Netherlands, is discussed (p.3910). Firstly, the transmission channel is discusses. A decrease in the oil price signals that there is an oil oversupply. This leads to an increase in potential output. As a result, the production costs decrease and growth of output and productivity increases. Secondly, as a result of a decrease in oil price the terms of trade of net importing countries improves. The terms of trade of net exporting countries worsens since the price of their export product decreases. Wealth is redistributed from the oil exporting country to the oil importing country. Thirdly, a decrease in the oil price could create a decrease in money demand due to the real balance effect. When monetary authorities fail to adapt to a decrease in money demand, the interest rate will decrease and the growth of the economy increases. Fourthly, as mentioned earlier, a decrease in the oil price will lead to a decrease in inflation. Fifthly, consumption, investment, and stock prices react positively to a decreased oil price. Consumption increases due to a positive relation to income. Investment increases since firms have lower costs.
Lardic and Mignon (2005, p.3914) conclude that the GDP reacts asymmetrically to shocks in the oil price. The recorrellationship between the oil price and the GDP is negative. Possible explanations for this phenomenon could be monetary policy or asymmetry in the prices of petroleum products. In the first explanation of monetary policy, it is assumed that prices are nominally sticky downward. An increase in the oil price will lead to losses in the GDP if authorities do not keep nominal GDP constant through unexpected inflation. If the oil price decreases, real wages must increase to clear the market. Therefore, the effect of monetary policy is asymmetric after an oil price shock. The second explanation is based on the asymmetric response of petroleum products after an oil price shock (Bacon, 1991). The price of gasoline increases faster after an oil price increase compared to a decrease of the gasoline price after an oil price decrease.
The effect of oil price shocks on the real GDP of some OECD countries was studied by Jiménez-Rodríguez and Sánchez (2006). In this article, the distinction between net importers and net exporters of oil is made. The transmission channels described in this article cover both the supply side effects and the demand side effects (p.201). The supply side effects are related to crude oil since oil still is a common input product in production. An increase in the oil price would therefore increase the production costs, which results in a decreased output. The demand side effects caused by oil price shocks affect
9 affected by oil price shocks. Furthermore, positive oil price shocks have an adverse impact on
investments since this will results in higher company costs.
To test the effect of oil prices on the real GDP, a VAR regression was used with the following set of variables: real GDP, real effective exchange rate, real oil price, real wage, inflation and the short and long-term interest rate (2006, p.203). Jiménez-Rodríguez and Sánchez (2006, p.224) found that the oil price is negatively related to the real GDP of oil importing countries. Also, it is found that the effect of the oil price on the real GDP is asymmetric. A positive oil price shock will cause a contraction of the real GDP of oil importing countries whereas a negative oil price shock will cause an expansion. However, the contraction will be larger compared to the expansion in absolute terms. The asymmetric reaction is described in the dispersion hypothesis by Lilien (1982). This hypothesis states that the adjustment costs are the result from the reallocation of resources. According to this hypothesis, an increase in the oil price would result in a contraction of industries that use oil in their production process. The opposite effect would occur when the oil price would decrease. Nonetheless, industries that are more energy-efficient will expand when oil prices increase compared to energy-intensive industries. Again, the opposite effect would occur when the oil price would decrease. Given that the short run cost of reallocating resources between energy-efficient and energy-intensive industries is high, the
readjustment between the industries generates a loss in the production. In case of an increase of the oil price, this readjustment increases economic loss. When the oil price decreases, the positive effect on the output is reduced by the readjustment. This explains the asymmetric effect of a change in the oil price on the real GDP.
According to Kilian (2009, pp. 1053-1055), the direction of change in real GDP after an oil price shock is not always clear. What causes the change in the oil price is important. Kilian (2009, p.1054) distinguishes three demand and supply shocks that are seen as key determinants of the real oil price. These determinants are oil supply shocks, aggregate demand shocks and precautionary demand shocks. Oil supply shocks are defined as a shock to the current availability of crude oil reserves. Aggregate demand shocks are shocks to the current demand for crude oil that are driven by fluctuations in the global business cycle. Precautionary demand shocks are shocks driven by changing precautionary
demand for oil. Changing precautionary demand is caused by uncertainty about future oil supply relative to expected demand.
Kilian (2009, p.1054) states that oil supply shocks have been studied extensively and do not explain the majority of the fluctuations in the oil price. The role of demand shocks are more important, of which the role of aggregate demand shocks have been extensively researched. However, Kilian (2009,
10 p.1054) takes note that quantifying the role of precautionary demand shocks is extremely difficult as expectation shifts underlying precautionary demand shocks are not observable. As a result
precautionary demand shocks can supersede the influence that aggregate demand shocks normally have on oil prices and thus real GDP. This causes asymmetric responses of the real GDP after oil price shocks.
2.2 Case for the Netherlands
As formulated in the theoretical analysis, the decrease in oil price has a different effect on the real GDP depending on whether a country is a net importer or net exporter of oil. According to the U.S Energy Information Administration, the Netherlands produced 58.77 thousand barrels of oil per day in 2013 (Eia.gov, 2014). Of this total, 21.99 thousand barrels per day is crude oil. Domestic consumption was measured at 987.4 thousand barrels per day. Consumption minus production equals the import. This gives the estimated import of 928.67 thousand barrels of oil per day. It can be concluded that even though the Netherlands is an oil producer, the country is a net importer.
The U.S Energy Information Administration mentions as well that the Netherlands owns one of the ten largest natural gas field in the world. According to the same source, the country produced 3051.96 billion cubic feet of natural gas in 2013. Domestic consumption was measured at 1637.17 billion cubic feet. This results in a net export of 1422.28 billion cubic feet. The net export of natural gas is investigated because of the possible relationship between the price of natural gas and the price of oil. If this relationship exists, the decrease in oil price would not necessarily mean growth of the GDP of The Netherlands. This is due to the loss in revenues from the export of natural gas.
Brown and Yücel (2008) researched the relationship between the price of crude oil and the price of natural gas. The results show that such a relationship exists. Brown and Yücel (2008, pp. 57-59) explain this relationship by looking at the long-term coefficient between natural gas and oil from their econometric model. Brown and Yücel specify two models, one with and one without exogenous
variables. The coefficient used is based on the average of the two coefficients of the oil price from table two. The coefficient used here has a value of approximately 0.9. The interpretation of this value is that when the oil price changes by 1%, the price of gas will change by 0.9% in the long-run. These coefficients are generally consistent and therefore Brown and Yücel conclude that natural gas prices are anchored in the movement of crude oil prices. The short-run dynamics show to be more complex (2008, p.59).
11 If the oil price decreases, Jiménez-Rodríguez and Sánchez (2006, p.201) have shown that net importers of oil will benefit. Given the information from the article of Brown and Yücel (2008, p.57), the price of natural gas will follow the direction of the oil price in the long-run. The Netherlands is
considered a net importer of oil but a net exporter of natural gas (Eia.gov, 2014).
2.3 Previous studies: The Netherlands
As mentioned earlier, Lardic and Mignon (2005) have researched the long-term relationship between oil prices and the GDP of 12 European countries including the Netherlands. The authors put forward that an asymmetric reaction of GDP after a change in the oil price exists (p.3914). Two auxiliary models were used to demonstrate this:
(1) 𝐺𝐷𝑃𝑖− +Δ𝐺𝐷𝑃𝑖+= 𝛼−+ 𝛽−𝑂𝐼𝐿−+ 𝜀𝑖
(2) 𝐺𝐷𝑃𝑖++Δ𝐺𝐷𝑃𝑖−= 𝛼++ 𝛽+𝑂𝐼𝐿++ 𝜀𝑖
The first equation was constructed to show the effect on GDP when the oil price would decrease. Lardic and Mignon also discuss the effect of an increased oil price on the GDP (2005). A decrease in oil price would cause the opposite effect. According to the formula, when the oil price decreases this will result in an positive change in the GDP, if the coefficient β is positive. The second equation shows the effect on the GDP when the oil price would increase. When the oil price increases, the change in GDP will be negative, again only when the β is positive . The values of alpha and beta for the Netherlands are given in table 6 and 7 on page 3914 in the article of Lardic and Mignon (2005). For equation one, the value of alpha and beta for the Netherlands were correspondingly -0.038 and 0.016. For equation two, the alpha was measured at -0.291 and the beta at 0.159. Computing these results in formula 1 and 2, the following result was obtained:
i. 𝐺𝐷𝑃𝑖− +Δ𝐺𝐷𝑃𝑖+= −0.038 + 0.016 𝑂𝐼𝐿𝑖−+ 𝜀𝑖
ii. 𝐺𝐷𝑃𝑖++Δ𝐺𝐷𝑃𝑖−= −0.291 + 0.159 𝑂𝐼𝐿𝑖++ 𝜀𝑖
Alpha is the intersect term and the beta determines the slope. Especially the beta is interesting. As one could see from the formulas, the reaction after an increase in the oil price has a larger effect on the GDP of the Netherlands compared to an equal decrease of the oil price. The beta in equation one is almost ten times smaller compared to the beta of equation 2. This supports the theory of an asymmetric reaction of GDP on oil price shocks (Lardic & Mignon, 2005, p.3914).
12 According to the U.S Energy Information Agency, the average price for a barrel of Brent Crude oil in 2014 was $99.54 (Eia.gov). If this price of $99.54 per barrel of Brent Crude oil is set as a benchmark for the prediction model of Lardic and Mignon, the change in GDP of The Netherlands can be calculated when compared to the expected price of one barrel of Brent Crude oil in 2015 (2005, p.3912). The
average price of one barrel of Brent Crude oil in 2015 is expected to be $68.08, according to the U.S Energy Information Agency. With this forecast, the model of Lardic and Mignon can make a prediction about the change of GDP of The Netherlands.
𝐺𝐷𝑃𝑖− +Δ𝐺𝐷𝑃𝑖+= −0.038 + 0.016 ∗ 𝑂𝐼𝐿𝑖−+ 𝜀𝑖
𝐺𝐷𝑃𝑖− +Δ𝐺𝐷𝑃𝑖+= −0.038 + 0.016 ∗ 31.460 + 𝜀𝑖
𝐺𝐷𝑃𝑖− +Δ𝐺𝐷𝑃𝑖+= 0.465%
The difference in the average oil price is $31.46. Using the model of Lardic and Mignon, the expected GDP of The Netherlands will increase by 0.465% due to the decrease in the oil price. This result creates the hypothesis for this research: a decrease in the oil price will lead to an increase in the real GDP of the Netherlands.
According to the CPB Netherlands, Bureau for Economic Policy Analysis, the expected growth of GDP in 2015 will be 0.25% (Centraal Planbureau, 2014). The CPB mentions two reasons that explain the increased growth of GDP. Firstly, the level of consumption increases even further. This is mostly due to a higher real disposable income. Secondly, the GDP of The Netherlands is affected by external influences. The CBS mentions the depreciation of the euro, but also decreased import prices. The net effect is positive. According to the same source, the decreased import prices are a result of a decrease in the oil price and other natural resources (2014, p.8).
De Nederlandsche Bank has made a prediction on the growth of the GDP of the Netherlands as well (Economische Ontwikkelingen en Vooruitzichten, 2015). This financial institution has predicted a nominal GDP growth of 1.2% in the year 2015.The same article mentions a predicted inflation rate of 0.7%, which would result in a 0.5% real GDP growth in 2015. De Nederlandsche Bank argues that the high oil supply decreases the oil price. This pushes the inflation rate down and increases the purchasing power. This contributes to a higher real GDP growth.
13 3. Data
The data used in this thesis runs from the second quarter of 1996 until the fourth quarter of 2014. Real growth GDP the Netherlands
The growth rate of the real GDP of the Netherlands is retrieved from the OECD database (Stats.oecd.org, 2015). The data is given as a quarterly growth rate and is seasonally adjusted. Since the real growth rate is used in this data base, the other variables used in in this thesis will be given in real terms as well. Data for other variables are transformed into quarterly data using Excel to be able to compare results. Inflation
The inflation rate used is retrieved from Eurostat (ec.europa.eu, 2015). Available data on the inflation rate for the Netherlands is recorded since the second quarter of 1996. This is also the reason for the starting point of the data used in this thesis. The inflation rate is converted into a quarterly rate. Real growth oil price
The historical Brent crude spot prices are retrieved from the U.S Energy Information Agency (eia.gov, 2014). The price per barrel is given in dollars and converted from monthly into quarterly prices. The quarterly data is then used to calculate the growth rate. The quarterly inflation rate is deducted to get the real growth of the oil price.
Real growth GDP Germany
The growth rate of the real GDP of Germany is retrieved from the OECD database (Stats.oecd.org, 2015). The data is given as a quarterly growth rate and is seasonally adjusted.
Growth real effective exchange rate
The real effective exchange rate is retrieved from the Bank for International Settlements (bis.org, 2015). The data provided is monthly. This is converted into quarterly data and turned into a growth rate. Lagged real growth GDP of the Netherlands
The chosen lag for this variable is one period, equal to one quarter. Real growth GDP of the Germany
The lag for this variable is equal to one quarter. Cross Variable
The cross variable is a multiplication of two variables. The first variable needed is a dummy variable. This dummy variable can either take value 1 or 0. Value 1 is assigned if the real growth rate of the oil price is positive. Value 0 is assigned when the real growth rate of the oil price is less or equal to 0. The dummy variable is multiplied with the variable that measures the real growth rate of the oil price.
14 4. Methodology
The effect of current low oil prices on the growth of the real GDP of the Netherlands will be researched using two models. The first model is a linear model as used in the article of Brown & Yücel (1999). The second model will include a dummy variable that will differentiate between an oil price increase or decrease (Jiménez-Rodríguez & Sánchez, 2006). Therefore, the second model is able to test for an asymmetric reaction of the real GDP of the Netherlands after an oil price increase or decrease.
The first model will take the following linear form:
𝐺𝐷𝑃𝑁𝑖= 𝛽0+ 𝛽1∗ 𝑅𝐺𝑂𝑃𝑖+ 𝛽2∗ 𝐺𝐷𝑃𝐺𝑖+ 𝛽3∗ 𝐺𝑟𝑜𝑤𝑡ℎ𝑅𝐸𝐸𝑅𝑖+ 𝛽4∗ 𝐿𝐺𝐷𝑃𝑁𝑖−1+ 𝛽5∗ 𝐿𝐺𝐷𝑃𝐺𝑖−1
+ 𝜀𝑖 (1)
In this model, the growth rate of the real GDP of the Netherlands is the dependent variable. The dependent variable is explained by five independent variables: real growth rate of the oil price (RGOP), growth rate of the real GDP of Germany (GDPG), growth rate of the real exchange rate (REER), the lagged growth rate of the real GDP of the Netherlands (LGDPN) and the lagged growth rate of the real GDP of Germany (LGDPG).
The first independent variable is the real growth rate of the oil price. The second independent variable is the real GDP growth of Germany (GDPG), expressed as a percentage. According to
Government.nl (2015), Germany is the most important trade partner of the Netherlands. The variable is added to compensate for economic fluctuations. When the economy of the world is worsening, the GDP of a country will decrease. Since less oil is needed when economic activity decreases, the price of oil will decrease (The Economist, 2014). Due to this result, one might draw the wrong conclusion when only regressing the growth rate of the oil price on the growth rate of the real GDP of the Netherlands.
The third independent variable is the growth rate of the real effective exchange rate, expressed as a percentage. In the case of the Netherlands, the real effective exchange rate measures the euro against a basket of other currencies. In case of an increasing real effective exchange rate, the
competitiveness of the euro will decrease since exports to countries outside the euro zone will become more expensive. Eventually, this will result in a decrease in the net export of countries in the euro zone (Maeso-Fernandez, Osbat & Schnatz, 2002).
The forth and the fifth independent variables are lagged variables of the real growth rate of the GDP of the Netherlands and Gerrmany. The lagged variables are added since it is expected that the growth rate of the real GDP of the Netherlands and Germany is highly determined by its previous level. Not including these lagged variables could lead to omitted variable bias.
15 The second model is non-linear. The difference in effect of an oil price increase or decrease can be researched:
𝐺𝐷𝑃𝑁𝑖 = 𝛽0+ 𝛽1∗ 𝐶𝑟𝑜𝑠𝑠𝑉𝑎𝑟𝑖+ 𝛽2∗ 𝐺𝐷𝑃𝐺𝑖+ 𝛽3∗ 𝐺𝑟𝑜𝑤𝑡ℎ𝑅𝐸𝐸𝑅𝑖+ 𝛽4∗ 𝐿𝐺𝐷𝑃𝑁𝑖−1+ 𝛽5
∗ 𝐿𝐺𝐷𝑃𝐺𝑖−1+ 𝜀𝑖 (2)
Compared to model 1, one modification has been made. The RGOP variable has been replaced by the
CrossVar variable. The latter variable makes model 2 a non-linear model. The possible asymmetric
reaction of the real GDP of the Netherlands after an oil price shock can be tested.
The statistical software used during this thesis is STATA. A regression output will be generated using the first and second model. The p-value of the independent variables will be used to conclude if a variable is significant using a significance level of 10%. The residuals of model 1 and 2 will be tested for autocorrelation with the independent variables. A Breusch-Pagan test will determine if the residuals are homoscedastic or heteroscedastic. This test uses a Chi-squared distribution with five degrees of freedom in the first and second model. The Breusch-Godfrey test is used to test for autocorrelation. This test uses a Chi-squared distribution as well .
5. Results and Analysis
The results for model 1 are summarized in the following table: Table1
Variable name Coefficient t-value p-value
RGOP . 0071338 1.82 0.072
GDPG . 3403936 4.79 0.000
GrowthREER −.0315925 −0.71 0.482
LGDPN . 1008783 4.28 0.000
LGDPG . 0842516 0.14 0.887
The results from table 1 show three significant variables and two non-significant variables. RGOP, GDPG and LGDPN are significant. Even though these variables are significant, the positive sign of the RGOP variable is noteworthy. The positive coefficient indicates that an increase in the oil price would stimulate the growth of the real GDP of the Netherlands. This contradicts with previous studies conducted by Jiménez-Rodríguez and Sánchez (2006) and Lardic and Mignon (2005) where a decrease in the oil price leads to an increase of the real GDP for oil importing countries.
The positive coefficient of the RGOP variable may be caused by the relationship between the oil price and the price of natural gas. This positive relationship between the two natural resources was
16 investigated by Brown and Yücel (2008). Since the Netherlands is an exporter of natural gas, decreasing oil prices will lead to decreasing natural gas prices and harm the growth of the real GDP of the
Netherlands.
Another explanation for the positive coefficient may be caused by the reduced revenues of energy companies (Postma, 2014). In 2014, Shell was the largest company in the world ranked by revenue (Statista, 2015). Since Shell is a Dutch company, it contributes to the GDP of the Netherlands. Due to large revenue losses caused by decreased oil prices, the effect on the GDP of the Netherlands is negative and may contribute to the positive coefficient of the RGOP variable.
The two insignificant variables are GrowthREER and LGDPG. The coefficient of the GrowthREER variable is negative which is in line with theory. An increasing exchange rate decreases the
competitiveness of the euro, leading to decreasing exports which will decrease the GDP of the
Netherlands (Maeso-Fernandez, Osbat & Schnatz, 2002). The insignificance of this variable is remarkable since the same variable is used in the research of Jiménez-Rodríguez and Sánchez and was found
significant (2006, p.217).
The second insignificant variable is LGDPG. A possible explanation is that the growth of the real GDP of Germany in the previous quarter has no significant effect on the growth of the real GDP of the Netherlands is the current quarter.
The last part of the analysis of model 1, is to analyze the residual. The correlation between the residual and the five independent variables is presented in the following output:
Table 2
Residual RGOP GDPG GrowthREER LGDPN LGDPG
Residual 1.0000 RGOP 0.3862 0.0007 1.0000 GDPG 0.8227 0.0000 0.2672 0.0205 1.0000 GrowthREER −0.2642 0.0229 −0.2672 0.9242 −0.1905 0.1016 1.0000 LGDPN 0.8118 0.0000 0.0974 0.4092 0.3867 0.0007 −0.1647 0.1609 1.0000 LGDPG 0.6234 0.0000 0.0917 0.4368 0.3774 0.0009 −0.2788 0.0161 0.6488 0.0000 1.0000
17 Examining the p-values of the pairwise correlation between the residual and the five independent variables, it shows that the correlation is significant.
After examining the correlation between the residual and the independent variables, the residual is tested on homoscedasticity. This is tested using a Breusch-Pagan test. The test gives the following result:
𝐶ℎ𝑖2(5) = 3.39 𝑃𝑟𝑜𝑏 > 𝐶ℎ𝑖2= 0.6402
The null hypothesis in this test assumes homoscedasticity. The result from the Breusch-Pagan test shows a p-value of 0.6402. Therefore, the null hypothesis cannot be rejected. It can be concluded that the residual is homoscedastic.
The Breusch-Godfrey test is used to test for autocorrelation. The test uses one lag and therefore 1 degree of freedom. The test gives the following result:
𝐶ℎ𝑖2(1) = 1.764
𝑃𝑟𝑜𝑏 > 𝐶ℎ𝑖2= 0.1842
The null hypothesis assumes no serial correlation. In this test, the null hypothesis is not rejected.
The results from model 2 are summarized in the following table: Table 3
Variable name Coefficient t-value p-value
CrossVar .0116191 1.80 0.077
GDPG .3542898 5.09 0.000
GrowthREER −.0252325 −0.56 0.574
LGDPN .4092843 4.03 0.000
LGDPG .0405784 0.54 0.592
The results show that three variables are significant: CrossVar, GDPG and LGDPN. Compared with the results from table 1, GDPG and LGDPG have remained significant. Most interesting is the CrossVar variable. This interaction term is significant which estimates an asymmetric reaction of the real GDP growth rate of the Netherlands after a change in the oil price. However, the sign of the CrossVar variable is positive which contradicts with previous research conducted by Jiménez-Rodríguez and Sánchez
18 (2006) and Lardic and Mignon (2005). Again, this positive sign may be caused by the same reasons mentioned earlier that caused the sign of the RGOP variable to be positive.
The last part of the analysis of model 2, is to analyze the residual. The correlation between the residual and the five independent variables is presented in the following table:
Table 4
Residual CrossVar GDPG GrowthREER LGDPN LGDPG
Residual 1.0000 CrossVar 0.2693 0.0203 1.0000 GDPG 0.8231 0.0000 0.1184 0.0205 1.0000 GrowthREER −0.2643 0.0229 −0.0121 0.9181 −0.1905 0.1016 1.0000 LGDPN 0.8110 0.0000 0.0463 0.6954 0.3867 0.0007 −0.1647 0.1609 1.0000 LGDPG 0.6236 0.0000 −0.1035 0.3801 0.3774 0.0009 −0.2788 0.0161 0.6488 0.0000 1.0000
Examining the p-values of the pairwise correlation between the residual and the six independent variables, it shows that the correlation is significant.
Using a Breusch-Pagan test, the residual is tested for homoscedasticity. The following result is obtained:
𝐶ℎ𝑖2(5) = 4.96 𝑃𝑟𝑜𝑏 > 𝐶ℎ𝑖2= 0.4208
The result from the Breusch-Pagan test shows a p-value of 0.2899. Therefore, the null hypothesis cannot be rejected. It can be concluded that the residual is homoscedastic.
To test for autocorrelation, the Breusch-Godfrey test is used with 1 lag and therefore 1 degree of freedom. The result of the test is summarized below:
𝐶ℎ𝑖2(1) = 1.326
𝑃𝑟𝑜𝑏 > 𝐶ℎ𝑖2= 0.2495
19 6.1 Conclusion
In this thesis, the effect of current low oil prices on the real GDP of the Netherlands is studied. Previous research indicated that there is a relationship between the oil price and the real GDP. Most recent research has found that the reaction of the real GDP of a country after an oil price shock is asymmetric. The decrease in the real GDP after an oil price increase is larger compared to the increase of the real GDP after an equal oil price decrease.
The reaction of an oil price shock on the real GDP of a country depends on whether the country is a net importer or net exporter of oil. When the oil price decreases, welfare is redistributed from oil exporting countries to oil importing countries. The Netherlands is a net importer of oil but net exporter of natural gas. Based on previous research, decreasing oil prices have a positive effect on the real GDP of net oil importing countries. Both linear and non-linear models were used to research the effect of oil price shocks on the real GDP of the Netherlands.
The first model that was tested was a linear model. In this model, five independent variables were used: RGOP, GDPG, REER, LGDPN, LGDPG. The results showed that RGOP, GDPG and LGDPN were significant. Noteworthy was the positive coefficient of the RGOP variable. This positive coefficient contradicts with previous studies. Possible explanations for the positive coefficient could be explained by the positive relationship between the price of oil and natural gas. Since the Netherlands is a net exporter of natural gas, decreasing oil prices will lead to a decrease in the price of natural gas and harm the growth of the real GDP of the Netherlands. Another explanation that may cause the positive coefficient is the decrease in revenues of energy companies. Shell is a company that contributes to the GDP of the Netherlands and is one of the largest companies in the world. Due to large revenue losses caused by decreased oil prices, the effect on the GDP of the Netherlands is negative and may contribute to the positive coefficient of the RGOP variable.
The second model was a non-linear model. This model used the same independent variables as the linear model with one alteration. The RGOP variable was replaced by the CrossVar variable. The results showed three significant variables: CrossVar, GDPG, and LGDPN. Like the RGOP variable, the
CrossVar variable has a positive sign. Again, this result contradicts previous research. The same
explanations for the positive coefficient of the RGOP variable hold for the CrossVar variable. There is sufficient evidence to support an asymmetric reaction of the real GDP of the Netherlands after an oil price shock.
20 Both linear and non-linear models show a significant relationship between the change in the oil price and the change in real GDP of the Netherlands. Therefore, it cannot be said whether the linear model performs better or worse compared to the non-linear model.
6.2 Discussion
In this thesis, two models were used to study the relationship between the oil price and the real GDP of the Netherlands. The found relationship between the oil price and real GDP of the Netherlands is positive, which contradicts with previous research. Given the possible explanations for the positive relationship in the conclusion, it would still be interesting to use a different regression method. In other articles, VAR models were used. It would be interesting to repeat this research using this technique, while also checking for exogeneity of the independent variables.
Even though the results contradict previous studies, this research was conducted while experiencing strong and persistent decreased oil prices. This might cause different results compared with previous studies. Therefore, more research is needed.
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