




































INDIAN JOURNAL OF FINANCE AND BANKING 10(1) (2022), 12-17 

12 

 

                   FINANCE AND BANKING 

                                                                  IJFB VOL 10 NO 1 (2022) P-ISSN 2574-6081  E-ISSN 2574-609X 
                                                  

        Available online at https://www.cribfb.com 
                                                                                                                                           Journal homepage: https://www.cribfb.com/journal/index.php/ijfb 

                                                                                                                                                                                                   Published by CRIBFB, USA 

EUROPEAN UNION AND THE UNITED STATES OF AMERICA: AN 

ECONOMETRIC INVESTIGATION ON THE PARADIGM SHIFT IN 

THE GDP’S GROWTH RATE TREND      

 
 Dibin Kodanghat Karuvalappil  (a)1   Archana Balakrishnan (b)   

 

(a) Assistant Professor, Berchmans Institute of Management Studies, S. B. College, India; E-mail: dibin1188@gmail.com 
(b) Assistant Professor, Department of Economics, Mannaniya College of Arts and Science, India; E-mail: archanabalakrishnan765@gmail.com 

 

 
A R T I C L E I N F O 

 
 

Article History: 
 

Received: 14th March 2022  

Accepted: 24th April 2022 
Online Publication: 30th April 2022 

 
Keywords: 

 
GDP Growth Rates, Regression, Granger 

Causality, Impulse Response 

 
JEL Classification Codes: 

 
      F40, F43, F44 

 

  
 

 
A B S T R A C T 

 
The recent war between Ukraine and Russia is yet another instance that emphasizes that economic 

overdependence may destroy the economic fabric of a nation. Taking this premise into consideration, this 

study aims to examine the long-term and short-term connection between the European Union and the 
United States GDP growth rates using tools like linear regression, Granger causality test, and impulse 

response function. Quarterly GDP figures of the European Union and the United States were taken for 

the period spanning 22 years, starting from quarter 2 of the financial year 1998-1999 to quarter 4 of the 

financial year 2018-2019. The Regression model and the Granger Causality test prove that the United 

States’ GDP growth rate is influenced by that of the European Union in the short-run as well as in the 

long-run, but the EU’s GDP is independent and does not follow the former. The possible explanation can 

be the trade surplus of the European Union over the United States in the recent past. Hence, the authors 
are of the opinion that a much more balanced trade between these two powerful economies would ensure 

the stabilization of the global trade and stability of the global power equation. 

 
 

© 2022 by the authors. Licensee CRIBFB, USA. This article is an open access article  distributed under 
the terms and conditions of the Creative Commons Attribution (CC BY) license 
(http://creativecommons.org/licenses/by/4.0/).  

 
                                                     

 

INTRODUCTION 

The world has seen a lot of examples that have proven the fact that trade and economic growth dictate the hierarchy of 

power. Gone are the days, when military power alone could make a nation a global leader. Now, the globe is driven by the 

quantum of trade and export advantage. For instance, the Chinese hegemony in global affairs is because of her strong trade 

policies and her having 15% of the overall global trade pie (China: The Rise of a Trade Titan | UNCTAD, n.d.). Likewise, 

with a nominal GDP of 22.89 trillion dollars, the United States of America’s economy is the largest in the world and is a 

leader in terms of armed power as well as representations in important global bodies. The European Union on the other hand 

is a powerful block that was established in the year 1993 after signing the Maastricht treaty. The nominal GDP of the 

European Union is 15 trillion dollars and they constitute 15 percent of the global trade. 

The archive collection of the European Union (Comission, 2008) that was released in 2008  quotes the following: 

The richness and diversity of American society owe a great deal of debt to the successive arrivals of immigrants from all 

over Europe over the past 500 years. This results in the degree to which Europeans and Americans share common values 

and maintain close cultural, economic, social, and political ties. Of course, this is reflected in the close transatlantic 

relationship. In addition, the USA has been a strong supporter of integration among European nations. The EU and the USA 

are major trading partners (taking goods and services together) and account for the world's largest trade relations: when 

combined, they make up about 40% of world trade. Transatlantic relations define the state of the global economy as the EU 

or USA and are a major trading and investment partner in almost every other country. 

The above-given extract showcases the historical relationship between these power blocs. However, the latest 

economic figures (BEA, 2021) of the United States showcase a changing trend. For instance, the goods and services deficit 

                                                      
1Corresponding author: ORCID ID: 0000-0002-8692-9990 

© 2022 by the authors. Hosting by CRIBFB. Peer review under responsibility of CRIBFB, USA.  
https://doi.org/10.46281/ijfb.v10i1.1705 

 

 
To cite this article: Karuvalappil, D. K., & Balakrishnan, A. (2022). EUROPEAN UNION AND THE UNITED STATES OF AMERICA: AN 

ECONOMETRIC INVESTIGATION ON THE PARADIGM SHIFT IN THE GDP’S GROWTH RATE TREND. Indian Journal of Finance and Banking, 

10(1), 12-17. https://doi.org/10.46281/ijfb.v10i1.1705 

http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://doi.org/10.46281/ijfb.v10i1.1705
https://orcid.org/0000-0002-8692-9990
https://orcid.org/0000-0002-6819-0651


Karuvalappil & Balakrishnan, Indian Journal of Finance and Banking 10(1) (2022), 12-17 

 

13 

of the United States in 2020 stood at $678.7 billion and was the highest since 2008. With 2.1 trillion dollars, her export 

figures were also in decline. Above all, the 2020 deficit with the European Union ($182.2 billion) was the highest on record 

and is the core reason behind the current research. This research is an attempt to understand the changing dynamics in GDP 

of the two of the most important and powerful blocs in the world; The United States of America and the European Union 

(hereafter called the US and EU respectively) 

 

The Below Discussed Related Literature Throws Light on Similar Areas of Research 

Bywalec (2020)  aims to identify and evaluate trade exchange between the European Union and India, as well as provide an 

indicator of the process' primary factors. The findings suggest that trade between the EU and India is very crucial for India, 

as exports to the EU account for roughly 17-20% of overall Indian exports. Konovalova and Ushanov (2019) examines the 

trade and economic ties between the United States and the European Union. The purpose of this paper was to identify the 

key qualities and characteristics of this collaboration. The research led to the coining of the term "economic anamorphosis," 

whose concept is the critical dominance of the weight and share of a limited list of partners in a system of bilateral 

cooperation, when one of the partners is regional economic integration, and these determined limited lists of countries attract 

the largest share of trade, capital, human, and other flows. According to Cabedo (2017), the E.U. and the United States 

account for over 60% of global GDP, and the E.U.'s investment in the United States is more than 8 times that of China and 

India combined. While the United States invests more than three times as much in the European Union as it does in the rest 

of Asia. The article went on to say that the trade flow between the EU and the US accounts for one-third of all global trade. 

In their study, Hussain and Haque (2016) concluded that there is a link between foreign direct investments, trade, and 

Bangladesh's per capita GDP growth rate. The research was carried out using annual time series data from 1973 to 2014. 

The Vector Error Correction Model (VECM) research revealed that these variables have a long-term association. The 

researchers ran a few post-estimation diagnostic tests to see if the VECM model was valid, and discovered that the regression 

residuals had a normal distribution and no auto-correlation. The variables of trade and foreign investment had a considerable 

impact on the GDP per capita growth rate, according to the findings. 

 Golinelli and Parigi (2014) suggested a straightforward approach for examining monthly estimates of quarterly 

world GDP and trade short-run views. Through bridge models, it combines high-frequency data from emerging and 

advanced nations to explain quarterly national accounts variables. In their study, Abosedra et al. (2020) used a portfolio 

technique to look at GDP growth volatility spillovers across 120 countries from 1960 to 2017. A study was done to discover 

the sources of growth volatility dynamics in the world in terms of volatility proportions from and to others using a spillover 

index based on variance decompositions under a vector autoregressive framework. They discovered that high-income 

growth nations were net transmitters of growth volatility, while low-income growth countries were net recipients. 

The literature relating to the GDP-based econometric study on EU and USA was not much to be seen which makes 

this study relatively unique. From the literature, it could be seen that most of the studies in the related area make use of tools 

like the Granger causality test and regression to find out the GDP-based relationship. This study has developed the following 

hypotheses using similar techniques: 

 

H1a: EU’s long-term quarterly GDP growth rates can be explained using US’s quarterly GDP growth rates. 

H1b: US’s long-term quarterly GDP growth rates can be explained using EU’s quarterly GDP growth rates. 

H2a: EU’s short-term quarterly GDP growth rates can be explained using US’s quarterly GDP growth rates. 

H2b: US’s short-term quarterly GDP growth rates can be explained using EU’s quarterly GDP growth rates. 

 

METHODS 

Data Description 

The study is secondary in nature and the data used for the study was collected from the World Banks' official website. The 

quarterly GDP growth rate figures of the U.S. and E.U. for 20 years from 1998 June to 2019 June comprising 85 

observations were used for the analysis. 

 

Regression 

The data fulfilled the criteria of stationarity, and hence the simple linear regression model has been used to estimate the 

effect of E.U's GDP growth rate on the U.S. The regression model was primarily used to explain the dependence of the U.S. 

Economy's GDP growth rate on E.U. 

 

Granger Causality Test 

Granger said in 1969 that a time series Yt produces another time series Xt, which can be anticipated knowing Yt's and Xt's 

prior values. The F-statistics aid in the interpretation of causality data. The Granger causality test utilised in this study is 

based on the VAR framework, and the methodologies used follow Granger (1969) and Engle and Granger's protocols (1987). 

 

Impulse Response Function 

In Econometric analysis, the impulse response is a crucial technique that uses Vector autoregressive models. This response 

allows you to trace the impact of a shock on the independent variable to the dependent variable, making it a useful tool for 

economic analysis. The Impulse Response Function (IRF) and Variance Decomposition are two prominent methods for 

depicting the dynamic behaviour of a VAR model and identifying the causes of variability. Pesaran and Shin (1998) 

proposed generalised impulse response functions, which were proven to be more effective. As a result, the generalised 

impulse response function is used to assess the dynamic behaviour of the variables in this study. 



Karuvalappil & Balakrishnan, Indian Journal of Finance and Banking 10(1) (2022), 12-17 

 

14 

DISCUSSION 

Stationarity Test 

To check for the long-term relationship using the regression model, it is essential to ensure the stationarity of the data. The 

Augmented Dickey-Fuller Test (ADF) shows that both U.S. and EU GDP data points are stationary at level. 

 

Table 1. Test for Stationarity 

Null Hypothesis: EU_GDP has a unit root                                                                                         

Augmented Dickey-Fuller test statistic t-Statistic: Prob.* 

Test critical values: 1% level: -3.510259 -4.013220  0.0022 

                                5% level: -2.896346   

                              10% level: -2.585396   

 

Null Hypothesis: US_GDP has a unit root 

 
Augmented Dickey-Fuller test statistic t-Statistic: Prob.* 

Test critical values: 1% level: -3.510259 -6.260726  0.0022 

                                5% level: -2.896346   

                              10% level: -2.585396   

                                               Inference at I(0) 

                                       Source: Authors data, EViews output 

 

Regression Model 

Now that the essential condition of stationarity has been fulfilled, it is crucial to run the regression model to test for the long-

term relationship between the variables. The regression model results show that the E.U's growth figures explain the U.S. 

GDP growth rate. The researcher could not build a regression model that explained the E.U.'s GDP growth rates using the 

US GDP figures. 

  

 Y US_GDPt =  a+bX EU_GDPt + 𝜺𝒕                                                                                      (1) 

 

Table 2. Test for Regression 

     

R-squared 0.395497     Mean dependent var 0.761961 

Adjusted R-squared 0.388214     S.D. dependent var 0.800184 

S.E. of regression 0.625878     Akaike info criterion 1.923927 

Sum squared resid 32.51308     Schwarz criterion 1.981401 

Log likelihood -79.76690     Hannan-Quinn criter. 1.947045 

F-statistic 54.30282     Durbin-Watson stat 1.864780 

Prob(F-statistic) 0.000000    

                                                   Source: Authors data, EViews output 

 

Residual Diagnostic Test 

The Regression model cannot be considered successful without running the residual diagnostic tests. Hence, the Breusch-

Godfrey Serial Correlation L.M. Test, Breusch-Pagan-Godfrey test for checking the heteroskedasticity, and the residual 

normality test was also run for checking the normality principle. The model has successfully met all of the above-given 

conditions. 

 

Table 3. Serial Correlation Test and Heteroskedasticity Test 

 
Breusch-Godfrey Serial Correlation LM Test: 

Null hypothesis: No serial correlation at up to 2 lags 

Particulars F-statistic Probability 

F-statistic 0.316873     Prob. F (2,81) 0.7293 

Obs*R-squared 0.659879     Prob. Chi-Square (2) 0.7190 

Heteroskedasticity Test: Breusch-Pagan-Godfrey 

Null hypothesis: Homoskedasticity  

F-statistic 0.919345     Prob. F (1,83) 0.3404 

Obs*R-squared 0.931184     Prob. Chi-Square (1) 0.3346 

Scaled explained SS 0.902871     Prob. Chi-Square (1) 0.3420 

                                Source: Authors data, EViews output 

 

Variable Coefficient Std. Error t-Statistic Prob. 

EU 2.728743 0.370298 7.369045 0.0000 

C 0.387076 0.084833 4.562828 0.0000 



Karuvalappil & Balakrishnan, Indian Journal of Finance and Banking 10(1) (2022), 12-17 

 

15 

Normality Test 

         
               Figure 1. Normality test (residuals)                                Figure 2. Line plots of variables studied over the period 

            

              The regression model has fulfilled all the necessary conditions like normality, stationarity, serial correlation, and 

homoskedasticity. The model states that 40% of the U.S. economy's growth can be explained using the GDP growth rates 

of E.U. 

             Since the late 1800s, the U.S. has been treated as a World Power, and the economic size stood at 21 trillion dollars 

(World Bank, 2022). These data emphasize her economic supremacy, but overdependence on a particular regional bloc 

alone for economic activities may threaten economic superiority in the long run. The study has duly explained that these 

two economies move in tandem in the long run. The primary reason for this could be the ever-growing trade relations. The 

U.S. government data states that the U.S. goods and services trade with the E.U. 27 totaled an estimated $1.1 trillion in 

2019. The Exports accounted for $468 billion, and imports were a whopping $598 billion. U.S. goods imports from the E.U. 

27 totaled $452.0 billion in 2019, up 6.0 percent ($25.8 billion) from 2018 and up 93 percent from 2009. U.S. imports from 

the E.U. 27 account for 18.1 percent of overall U.S. imports in 2019. The U.S. goods and services trade deficit with the E.U. 

27 was $130 billion in 2019. U.S. Goods trade (exports plus imports) with the E.U. 27 was $720 billion in 2019. Goods 

exports were $268 billion; goods imports totaled $452 billion. The U.S. goods trade deficit with The E.U. 27 was $184 

billion in 2019 (European Union, 2020). These figures substantiate the views given earlier that claimed the U.S.'s 

overdependence on a particular regional block. Trade relations can be a two-edged sword; overdependence on a specific 

block or nation may be undue leverage to other countries or trade blocs. 

 

VAR Granger Causality Test 

As the variables were found to have a long-term relationship, the VAR-based Granger Causality Test was run to 

understand the short-run relationships between the GDP growth rates. The variables were found stationary at the level. A 

bi-directional granger causality test was carried out using the Granger causality in a VAR environment. 

         

∆𝐔𝐒 𝑮𝑫𝑷𝒕 =  µ + ∑ 𝜶𝒊
𝒏
𝒊=𝟏 ∆𝑼𝑺 𝑮𝑫𝑷𝒕−𝟏 + ∑ 𝜷𝒋

𝒏
𝒋=𝟏 ∆𝑬𝑼𝑮𝑫𝑷𝒋−𝟏

+  𝜺𝒕                          (2)                   

  

Table 4. Granger Causality Test 

 
VAR Granger Causality Block Exogeneity Wald Tests 
Sample: 1998 Q2 -2019 Q2 

Included observations: 84 

 
Dependent Variable: US GDP 

Variable Chi-square value Degree of Freedom Probability 

EU_GDP 21.3667 1 0.0000 

Dependent Variable: EU GDP 

US_GDP 0.6949 1 0.4045 

                                               Source: Authors data, EViews output 

Table 5. VAR Granger Causality test (Sub-periods)  

Dependent 

Variable 

Sub-Period Ch-sq Probability 

US GDP 1998, Q2 -2002, Q2 0.8400 0.6570 

EU GDP 1998, Q2 -2002, Q2 0.4283 0.8072 

US GDP 2002, Q3-2006, Q3 0.4596 0.4978 

EU GDP 2002, Q3-2006, Q3 0.3229 0.5698 

US GDP 2006, Q4 – 2010, Q4 26.0158 0.0000* 

EU GDP 2006, Q4 – 2010, Q4 2.9547 0.3987 

US GDP 2011, Q1 – 2015, Q1 0.6484 0.8853 

EU GDP 2011, Q1 – 2015, Q1 4.4173 0.2198 

US GDP 2015, Q2 – 2019, Q2 0.2665 0.6057 

EU GDP 2015, Q2 – 2019, Q2 0.3811 0.5370 

                     Source: Authors data, EViews output 

0

1

2

3

4

5

6

7

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

Series: Residuals

Sample 1 85

Observations 85

Mean      -2.84e-16

Median   0.000944

Maximum  1.631899

Minimum -1.764631

Std. Dev.   0.622142

Skewness   0.012408

Kurtosis   3.033771

Jarque-Bera  0.006220

Probabil ity  0.996895 

Series: Residuals

Sample 1 85

Observations 85

Mean      -2.84e-16

Median   0.000944

Maximum  1.631899

Minimum -1.764631

Std. Dev.   0.622142

Skewness   0.012408

Kurtosis   3.033771

Jarque-Bera  0.006220

Probabil ity  0.996895 

-100
10

1 7 13 19253137 43495561677379 85

EU and US GDP's 
Quarterly Growth Rate 

(1998-2019)

US GDP Indian GDP



Karuvalappil & Balakrishnan, Indian Journal of Finance and Banking 10(1) (2022), 12-17 

 

16 

             

              The granger causality test had proved the short-run relationship between the above-stated variables when taken as 

a whole. However, the results turned out a bit different when the short-term split-ups of the periods were used for 

understanding the short-term relationships of GDP figures. The granger causality test was run for five different split-ups:- 

F.Y.1998-2002, F.Y.2002-2006, FY.2006-2010, F.Y.2011-2015, and F.Y.2015-2019 and proves that baring in the 2006-

2010 sub-period wherein US GDP had depended on the E.U.'s GDP, the GDP growth rates do not explain each other. 

 

Impulse Response Function 

The impulse response function was done to determine the U. S’s GDP growth response to a unit of risk or shock in E. U’s 

GDP growth rate. 

 
Figure 3. Impulse Response Graph 

               

              One standard deviation shock in E.U.'s GDP growth rate causes an increase in the GDP rates of the U.S. The growth 

peaks at period two (0.8) and then declines gradually becomes close to zero by the 9th period. Hence, the shock to E.U.'s 

GDP may positively impact the U.S.'s GDP growth rate in the Short-run and Long-run. 

 

CONCLUSION AND FURTHER SCOPE OF THE STUDY 

The research tries to talk about the interdependence of the two of the World's most significant trade blocs, the United States 

of America and the European Union. The study has pointed out that the E.U’s GDP can explain the U.S. economy's GDP 

growth rates. The possible reason is the trade volume between these two trade blocs and the trade surplus position of the 

E.U. Regression analysis, the Granger Causality test, and the Impulse response function had duly substantiated the research 

with positive findings. As previously mentioned, Global trade can be a double-edged sword, and the U.S. is found dependent 

on a block like E.U. on trade Hence, it is advisable to either bring down the trade deficit or diversify the scope of trade by 

searching for other trade blocs so that the U.S. remains economically consistent and stable in the long run. Else, other 

economic giants like China may overtake the U.S. as an economic power that would change the World's power equation. 

The author feels that the World is not ready to accept this change. Further studies in this area should include more economic 

indicators like unemployment rates, central bank policy rates, and inflation and build an econometrics-based model that 

fully clarifies the interdependence aspect. 

 

 

 

Author Contributions: Conceptualization, D.K.K. and A.B.; Data Curation, D.K.K. and A.B.; Methodology, D.K.K.; Validation, A.B.; Visualization, 
D.K.K.; Formal Analysis, D.K.K. and A.B.; Investigation, D.K.K.; Resources, D.K.K.; Writing – Original Draft, A.B.; Writing – Review & Editing, 

D.K.K.; Supervision, D.K.K.; Software, D.K.K.; Project Administration, A.B.; Funding Acquisition, D.K.K., and A.B. Authors have read and agreed to 
the published version of the manuscript. 

Institutional Review Board Statement: Ethical review and approval were waived for this study, due to that the research does not deal with vulnerable 
groups or sensitive issues. 

Funding: The authors received no direct funding for this research. 

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 

Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available 
due to restrictions. 

Conflicts of Interest: The authors declare no conflict of interest.  

 

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