




































AGORA International Journal of Economical Sciences, http://univagora.ro/jour/index.php/aijes 

ISSN 2067-3310, E-ISSN 2067-7669 

Vol. 18, No. 2 (2024), pp. 50-62 

 

50 

 

ANALYZING THE DYNAMIC RELATIONSHIPS BETWEEN 

MACROECONOMIC VARIABLES AND GDP GROWTH IN 

ALBANIA: A PANEL DATA APPROACH 

 

L. BODURI, F. PIETRI 

 

Leonard Boduri¹, Fabian Pjetri² 

¹ Faculty of Economics, Business and Development, European University of Tirana, Albania 

E-mail: leonardboduri@gmail.com  

² Faculty of Economics, University Metropolitan Tirana, Albania 

E-mail: fpjetri@umt.edu.al  

 

Abstract: This study examines the impact of various macroeconomic factors on GDP 

growth in Albania during the period 1997-2023. Using a panel data approach, the analysis 

investigates the relationship between GDP growth and variables such as trade openness, 

inflation, government expenditure, domestic credit, population growth, final consumption 

expenditure, gross fixed capital formation, and the current account balance. The results 

indicate that government expenditure and trade openness are statistically significant 

determinants of GDP growth. Granger causality tests reveal that domestic credit and 

government expenditure Granger-cause the current account balance, while domestic credit 

and trade openness Granger-cause GDP growth. The findings suggest that policies aimed at 

increasing government spending and promoting trade integration can positively impact 

economic growth in Albania. However, further research is needed to explore the long-term 

effects of these variables and to address potential limitations of the study. 

Keywords: GDP Growth, Macroeconomic Factors, Trade Openness, Inflation, Government 

Spending, Domestic Credit, Final Consumption Expenditure, Gross Fixed Capital Formation. 

 

1. INTRODUCTION 

The relationship between macroeconomic variables and economic growth has garnered 

significant attention among economists. The study presented by the authors specifically 

examines the impact of several important factors such as trade openness, inflation, government 

spending, domestic credit to the private sector, population growth, final consumption 

expenditure, gross fixed capital formation and the balance of current account in the growth of 

GDP in Albania. Understanding these relationships is essential for policymakers seeking to 

implement effective strategies to foster sustainable economic development. Albania has 

experienced fluctuations in economic conditions during this 26-year period of time, which the 

authors have taken into consideration. Analysing the dynamics between these variables and 

GDP growth will provide valuable insights into the effectiveness of fiscal policies and 

monetary policies. By employing a panel data analysis using secondary statistical data provided 

by the World Bank Database. This research aims to isolate the individual effects of each 

variable on real GDP growth in Albania. 

 

mailto:leonardboduri@gmail.com
mailto:fpjetri@umt.edu.al


Leonard BODURI, Fabian PJETRI 

51 

 

2. LITERATURE REVIEW 

The relationship between various macroeconomic variables and economic growth has 

been the focus of extensive research, revealing a diverse range of findings. Trade openness, 

defined as the sum of exports and imports relative to GDP, is widely acknowledged as a 

significant driver of economic growth. Frankel and Romer (Frankel, 1999) demonstrate that 

increased trade openness enhances productivity and stimulates growth by facilitating access to 

international markets. Similarly, studies such as Rodriguez and Rodrik  (Rodriguez, 2001) 

emphasize the positive effects of trade on economic performance, particularly in developing 

countries. Conversely, inflation is frequently considered detrimental to economic growth. High 

inflation rates erode purchasing power and contribute to uncertainty, which can deter both 

investment and consumption (Cochrane, 2011). Additionally, inflation destabilizes financial 

markets, which poses risks for both borrowers and lenders, potentially undermining long-term 

economic growth (Boyd, Levine, and Smith, (Boyd, 2001); Fischer,  (Fischer, 1993). 

Government expenditure has a complex and dual impact on economic growth. Productive 

public spending on infrastructure and education has been shown to enhance growth potential, 

while excessive or poorly allocated spending can lead to inefficiencies and diminished 

economic performance Alesina and Tabellini, (Alesina, 1990); Barro, (Barro, 1979).  

The effectiveness of government expenditure on GDP growth is thus contingent upon 

its quality and efficiency. Domestic credit to the private sector (DCPS) is another critical factor 

influencing economic growth. (Levine, 2005)asserts that a robust financial system, which 

provides adequate credit to private enterprises, can significantly foster investment and spur 

economic activity. Conversely, a lack of credit availability can severely constrain growth, 

especially in developing economies (King, 1993). Population growth plays a dual role in 

influencing GDP growth. A growing population can increase the labor force, driving economic 

expansion if adequately trained and educated. However, rapid population growth without 

corresponding economic opportunities can strain resources and infrastructure, leading to 

potential adverse effects on economic performance (Bloom, 2004). Final consumption 

expenditure (FCE) represents a substantial portion of total economic activity and is a key driver 

of GDP growth. Increased consumption typically reflects rising household incomes and 

consumer confidence, which in turn can stimulate further economic growth (Kuznets, 1955). 

Gross fixed capital formation (GFCF) is vital for capital accumulation and long-term economic 

growth. Higher levels of GFCF indicate that an economy is investing in its productive capacity, 

which is essential for sustainable growth (Ram, 1986). Lastly, the current account balance 

(CAB) serves as an important indicator of an economy's position in international trade.  

A balanced current account suggests sustainable economic practices, while persistent 

deficits may indicate structural economic issues that could hinder growth (Obsfeld, 1996). In 

the context of Albania, understanding the interactions between these variables and their 

collective impact on GDP growth is crucial for formulating effective fiscal policies aimed at 

ensuring sustainable economic development. By systematically analyzing the effects of trade 

openness, inflation, government expenditure, domestic credit to the private sector, population 

growth, final consumption expenditure, gross fixed capital formation, and the current account 

balance on GDP growth, this study endeavors to provide valuable insights for policymakers to 

enhance economic resilience and promote growth. 



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VARIABLES AND GDP GROWTH IN ALBANIA: A PANEL DATA APPROACH 

52 

 

3. METHODOLOGY 

3.1 Data 

The data for this study was obtained from the World Bank's World Development 

Indicators (WDI) database (WorldBank, 2024), covering the period from 1997 to 2023. The 

specific variables used include: GDP growth (GDPG): Measured as the annual growth rate of 

GDP. Current account balance (CAB): Represented as a percentage of GDP. Domestic credit 

to the private sector (DCPS): Measured as a percentage of GDP. Final consumption 

expenditure (FCE): As a percentage of GDP. Government Expenditure (GEXP): Total 

Government Spending as a percentage % of GDP. Gross Fixed Capital Formation (GFCF): As 

a percentage % of GDP. Inflation Rate (INFLATION): Measured as the percentage change in 

the Consumer Price Index (CPI). Population growth (POPG): Expressed as a growth rate. Trade 

Openness (TRADEOPEN): The sum of Exports and Imports as a percentage  of GDP. 

 

3.2 Econometric Model Specification 

This study aims to investigate the impact of these macroeconomic variables on GDP 

growth in Albania over the period from 1997 to 2023. The econometric model is specified as 

follows:GDPGt=β0+β1CABt+β2DCPSt+β3FCEt+β4GEXPt+β5GFCFt+β6INFLATIONt+β7

POPGt+β8TRADEOPENt+εtGDPG_t = \beta_0 + \beta_1 CAB_t + \beta_2 DCPS_t + \beta_3 

FCE_t + \beta_4 GEXP_t + \beta_5 GFCF_t + \beta_6 INFLATION_t + \beta_7 POPG_t + 

\beta_8 TRADEOPEN_t + \varepsilon_tGDPGt=β0+β1CABt+β2DCPSt+β3FCEt+β4GEXPt

+β5GFCFt+β6INFLATIONt+β7POPGt+β8TRADEOPENt+εt. 

Where: GDPG_t: Annual growth rate of GDP in year ttt. CAB_t: Current account 

balance as a percentage of GDP in year ttt. DCPS_t: Domestic credit to the private sector as a 

percentage of GDP in year ttt. FCE_t: Final consumption expenditure as a percentage of GDP 

in year ttt. GEXP_t: Government expenditure as a percentage of GDP in year ttt. GFCF_t: 

Gross fixed capital formation as a percentage of GDP in year ttt. INFLATION_t: Percentage 

change in the CPI in year ttt. POPG_t: Population growth rate in year ttt. TRADEOPEN_t: 

Trade openness as a percentage of GDP in year ttt. εt\varepsilon_tεt: Error term capturing other 

factors affecting GDP growth. 

 

Expected Signs of Coefficients 

β1\beta_1β1 (Current Account Balance): Expected positive; a surplus can enhance 

economic growth. β2\beta_2β2 (Domestic Credit to the Private Sector): Expected positive; 

more credit may stimulate business activity. β3\beta_3β3 (Final Consumption Expenditure): 

Expected positive; higher consumption can drive GDP growth. β4\beta_4β4 (Government 

Expenditure): Expected ambiguous; depends on the nature of spending. β5\beta_5β5 (Gross 

Fixed Capital Formation): Expected positive; investment in fixed assets can boost growth. 

β6\beta_6β6 (Inflation Rate): Expected negative; high inflation may deter investment and 

savings. β7\beta_7β7 (Population Growth): Expected positive; a growing population can 

contribute to labor supply and demand. β8\beta_8β8 (Trade Openness): Expected positive; 

greater openness can lead to improved economic efficiency and growth. 

 

Model Estimation 



Leonard BODURI, Fabian PJETRI 

53 

 

Given the time-series nature of the data, the model will be estimated using an 

autoregressive distributed lag (ARDL) model or Vector Error Correction Model (VECM), 

depending on the results of stationarity tests. These methods allow us to capture both the short-

term and long-term dynamics between the variables. 

 

Stationarity and Cointegration Testing 

ADF Test: To assess the stationarity of individual variables. Johansen Cointegration 

Test: To determine if there is a long-run equilibrium relationship among the variables. 

 

Error Correction Model (ECM) Representation (if Cointegration Exists) 

ΔGDP_Growtht=α+∑i=1pβiΔXt−i+λECMt−1+εt\Delta GDP\_Growth_t = \alpha + 

\sum_{i=1}^{p} \beta_i \Delta X_{t-i} + \lambda ECM_{t-1} + \varepsilon_tΔGDP_Growtht

=α+i=1∑pβiΔXt−i+λECMt−1+εt 

Where: Δ\DeltaΔ represents first differences. ECMt−1ECM_{t-1}ECMt−1 is the 

lagged error correction term capturing the long-term relationship. XXX represents the 

independent variables.    This model will allow us to examine both the short-run fluctuations 

and the long-term relationship between public debt and GDP growth, accounting for other 

macroeconomic factors. 

 

3.3 Time-Series Analysis 

Before performing the regression analysis, it is crucial to assess the stationarity of the 

variables to avoid spurious regression results. The main variables used in this paper by the 

authors are: GDP growth (GDPG), Current account balance (CAB), Domestic credit to the 

private sector (DCPS), Final consumption expenditure (FCE), Government expenditure 

(GEXP), Gross fixed capital formation (GFCF), Inflation rate (INFLATION), Population 

growth (POPG), Trade openness (TRADEOPEN). Ensuring that these variables are 

stationary is essential for the validity of the regression analysis. 

Augmented Dickey-Fuller (ADF) Test. 

The Augmented Dickey-Fuller (ADF) test (Mushtaq, 2011) is used to determine the 

presence of unit roots in each time series, assessing whether the series is stationary. If a series 

is non-stationary at its level, we apply differencing until stationarity is achieved. In this case, 

the first difference of a series represents the change between the current and previous values. 

If necessary, further differencing (e.g., second differencing) can be applied to ensure 

stationarity. The general form of the ADF test equation is: 

ΔYt=α+βYt−1+∑i=1pγiΔYt−i+εt\Delta Y_t = \alpha + \beta Y_{t-1} + \sum_{i=1}^{p} 

\gamma_i \Delta Y_{t-i} + \varepsilon_tΔYt=α+βYt−1+i=1∑pγiΔYt−i+εt  

Where: ΔYt\Delta Y_tΔYt: First difference of the variable YYY at time ttt, α\alphaα: 

Intercept term, β\betaβ: Coefficient of the lagged level of YYY, γi\gamma_iγi: Coefficients of 

the lagged first differences, ppp: Lag order, εt\varepsilon_tεt: Error term. 

 

Hypothesis Testing 

Null Hypothesis (H₀): The series has a unit root (non-stationary). 

Alternative Hypothesis (H₁): The series is stationary. 



ANALYZING THE DYNAMIC RELATIONSHIPS BETWEEN MACROECONOMIC 

VARIABLES AND GDP GROWTH IN ALBANIA: A PANEL DATA APPROACH 

54 

 

If the ADF test statistic is significantly negative (i.e, smaller than the critical values), 

we reject the null hypothesis and conclude that the series is stationary. 

 

Table 1: Null Hypothesis: GDPG has a unit root. 

Null Hypothesis: GDPG has a unit root  

Exogenous: Constant   

Lag Length: 0 (Automatic - based on SIC, maxlag=6) 

   t-Statistic   Prob.* 

Augmented Dickey-Fuller test statistic -6.340664  0.0000 

Test critical values: 1% level  -3.711457  

 5% level  -2.981038  

 10% level  -2.629906  

*MacKinnon (1996) one-sided p-values.  

Augmented Dickey-Fuller Test Equation  

Dependent Variable: D(GDPG)  

Method: Least Squares   

Date: 11/05/24   Time: 01:20  

Sample (adjusted): 2 27   

Included observations: 26 after adjustments 

Variable Coefficient Std. Error t-Statistic Prob.   

GDPG(-1) -0.931678 0.146937 -6.340664 0.0000 

C 4.359202 0.874893 4.982558 0.0000 

R-squared 0.626192     Mean dependent var 0.552274 

Adjusted R-squared 0.610616     S.D. dependent var 5.199993 

S.E. of regression 3.244828     Akaike info criterion 5.265805 

Sum squared resid 252.6938     Schwarz criterion 5.362582 

Log likelihood -66.45547     Hannan-Quinn criter. 5.293673 

F-statistic 40.20402     Durbin-Watson stat 1.210973 

Prob(F-statistic) 0.000001    

Source: Data processed from World Bank Database using Econometric Software, EViews 13 (Nov 2024). 

 

The results from the Augmented Dickey-Fuller (ADF) test indicate that the variable 

GDPG is likely stationary. Here's a breakdown of the test results: 

ADF test statistic: The value of -6.340664 is significantly less than the critical values 

at all levels (1%, 5%, and 10%), suggesting that we can reject the null hypothesis of a unit root. 

This indicates that GDPG is stationary. P-value: The p-value of 0.0000 is much less than 0.05, 

providing strong evidence against the null hypothesis of a unit root. Regression output: The 

coefficient on the lagged GDPG term (GDPG(-1)) is negative and significant, suggesting that 

there is a strong negative relationship between GDPG and its lagged values. This is consistent 

with the stationarity of GDPG. The analysis suggests that the variable GDPG is stationary, 

which means it can be directly used in time series models without requiring any differencing. 

This is a good sign for your analysis, as stationary variables are more suitable for modeling and 

forecasting. 

 

4. REGRESSION ANALYSIS 

The primary objective of the regression analysis is to quantitatively assess the influence 

of key economic variables on GDP growth (GDPG) in Albania. By employing a multiple linear 

regression model, we aim to determine the magnitude and statistical significance of the 

relationships between GDP growth and variables such as public debt, investment rate, inflation, 



Leonard BODURI, Fabian PJETRI 

55 

 

and trade openness. The multiple linear regression model can be expressed as follows:    

GDPGt=β0+β1⋅CABt+β2⋅DCPSt+β3⋅FCEt+β4⋅GEXPt+β5⋅GFCFt+β6⋅INFLATIONt+β7⋅PO

PGt+β8⋅TRADEOPENt+εtGDPG_t = \beta_0 + \beta_1 \cdot CAB_t + \beta_2 \cdot DCPS_t 

+ \beta_3 \cdot FCE_t + \beta_4 \cdot GEXP_t + \beta_5 \cdot GFCF_t + \beta_6 \cdot 

INFLATION_t + \beta_7 \cdot POPG_t + \beta_8 \cdot TRADEOPEN_t + 

\varepsilon_tGDPGt=β0+β1⋅CABt+β2⋅DCPSt+β3⋅FCEt+β4⋅GEXPt+β5⋅GFCFt+β6

⋅INFLATIONt+β7⋅POPGt+β8⋅TRADEOPENt+εt. Where: GDPG_t: GDP growth at time ttt 

(dependent variable), CAB_t: Current account balance at time ttt, DCPS_t: Domestic credit to 

the private sector at time ttt, FCE_t: Final consumption expenditure at time ttt, GEXP_t: 

Government expenditure as a percentage of GDP at time ttt, GFCF_t: Gross fixed capital 

formation at time ttt, INFLATION_t: Inflation rate at time ttt, POPG_t: Population growth at 

time ttt, TRADEOPEN_t: Trade openness at time ttt, β0\beta_0β0: Intercept (constant term), 

β1,β2,...,β8\beta_1, \beta_2, ..., \beta_8β1,β2,...,β8Coefficients of the independent variables, 

representing their respective effects on GDP growth, εt\varepsilon_tεt: Error term, capturing 

unobserved factors that influence GDP growth. This model posits that GDP growth is a linear 

function of the independent variables, where each coefficient βi\beta_iβi indicates the marginal 

impact of the corresponding variable on GDP growth. For example: β1\beta_1β1 measures the 

effect of the current account balance on GDP growth. β2\beta_2β2 reflects the contribution of 

domestic credit to the private sector. β3\beta_3β3 captures the effect of final consumption 

expenditure on economic growth. The error term εt\varepsilon_tεt accounts for any 

unexplained variation in GDP growth that is not captured by the included variables. This model 

allows us to assess which factors play the most significant role in driving economic growth in 

Albania and identify potential policy areas for improvement. 

 

Table 2: Method: Least Squares. 

Dependent Variable: GDPG   

Method: Least Squares   

Date: 11/05/24   Time: 01:45  

Sample: 1 27    

Included observations: 27   

Variable Coefficient Std. Error t-Statistic Prob.   

C -14.52817 14.82079 -0.980256 0.3400 

CAB 0.487309 0.253959 1.918847 0.0710 

DCPS 0.001918 0.124172 0.015447 0.9878 

FCE 0.532604 0.265559 2.005596 0.0602 

GEXP 0.897604 0.325787 2.755192 0.0130 

GFCF -0.181954 0.159444 -1.141178 0.2688 

INFLATION -0.226011 0.174002 -1.298896 0.2104 

POPG -2.122212 1.883771 -1.126576 0.2747 

TRADEOPEN 0.003717 0.163104 0.022788 0.9821 

R-squared 0.780750     Mean dependent var 4.062137 

Adjusted R-squared 0.683305     S.D. dependent var 4.332646 

S.E. of regression 2.438223     Akaike info criterion 4.881618 

Sum squared resid 107.0088     Schwarz criterion 5.313563 

Log likelihood -56.90184     Hannan-Quinn criter. 5.010058 

F-statistic 8.012255     Durbin-Watson stat 2.130772 

Prob(F-statistic) 0.000133    

Source: Data processed from World Bank Database using Econometric Software, EViews 13 (Nov 2024). 



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VARIABLES AND GDP GROWTH IN ALBANIA: A PANEL DATA APPROACH 

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The model's R-squared value is approximately 0.780, indicating that about 78.0% of 

the variability in GDP growth can be explained by the independent variables included in the 

model. The Adjusted R-squared of 0.683 suggests that when adjusting for the number of 

predictors, about 68.3% of the variability is explained. 

 

4.1.1 Significant Variables. 

GEXP (Government Expenditure): The coefficient for government expenditure is 

0.8976, with a t-statistic of 2.7552 and a p-value of 0.0130, indicating it is statistically 

significant at the 5% level. This suggests that an increase in government spending is associated 

with higher GDP growth, highlighting its positive impact on economic activity. FCE (Final 

Consumption Expenditure): The coefficient is 0.5326, with a t-statistic of 2.0056 and a p-value 

of 0.0602, indicating it is marginally significant at the 10% level. This implies that higher final 

consumption expenditure may contribute positively to GDP growth, supporting economic 

theory. CAB (Current Account Balance): The coefficient is 0.4873, with a t-statistic of 1.9188 

and a p-value of 0.0710, suggesting it is marginally significant at the 10% level. This indicates 

that a better current account balance may be associated with GDP growth. 

 

4.1.2 Non-Significant Variables. 

DCPS (Domestic Credit to Private Sector): The coefficient is 0.0019, with a t-statistic 

of 0.0154 and a p-value of 0.9878, indicating it is not statistically significant. GFCF (Gross 

Fixed Capital Formation): The coefficient is -0.1820, with a t-statistic of -1.1412 and a p-value 

of 0.2688, suggesting no significant relationship with GDP growth. INFLATION: The 

coefficient is -0.2260, with a t-statistic of -1.2989 and a p-value of 0.2104, indicating it does 

not significantly affect GDP growth. POPG (Population Growth): The coefficient is -2.1222, 

with a t-statistic of -1.1266 and a p-value of 0.2747, showing no significant impact on GDP 

growth. TRADEOPEN (Trade Openness): The coefficient is 0.0037, with a t-statistic of 0.0228 

and a p-value of 0.9821, suggesting it is not statistically significant. 

 

4.2 Model Fit and Diagnostics. 

The R-squared value of 0.7808 indicates that approximately 78.1% of the variability in 

GDP growth is explained by the included variables. The Adjusted R-squared of 0.6833 

indicates that about 68.3% of the variability is explained when adjusting for the number of 

predictors. The F-statistic of 8.0123 with a p-value of 0.000133 indicates that the overall model 

is statistically significant, meaning at least one of the predictors significantly relates to GDP 

growth. The Durbin-Watson statistic of 2.1308 suggests that there is no significant 

autocorrelation in the residuals, which is a positive sign for model validity. Policy Implications. 

The significant relationship between government expenditure and GDP growth suggests that 

policymakers in Albania should prioritize fiscal policies that enhance public spending to 

stimulate economic growth. Additionally, the positive association between final consumption 

expenditure and GDP indicates that consumer spending plays a crucial role in economic 

activity. Further Research. The results highlight the need for further investigation into the 

dynamics of non-significant variables, such as domestic credit and trade openness, to explore 

their potential indirect effects on GDP growth. Expanding the dataset or examining different 



Leonard BODURI, Fabian PJETRI 

57 

 

time periods may provide deeper insights into these relationships. In summary, while 

government expenditure is a significant predictor of GDP growth, further exploration of other 

factors is essential for a comprehensive understanding of the Albanian economy's dynamics. 

 

4.3  VAR Model. 

VAR models are a popular method for multivariate time series, such as the one in this 

study. These results are from a Vector Autoregression (VAR) model, which is a type of time 

series model used to analyze dynamic relationships between multiple variables. 

 

Table 3:  Vector Autoregression Estimates. 
Vector Autoregression Estimates 

Date: 11/05/24   Time: 02:15  

Sample (adjusted): 3 27  

Included observations: 25 after adjustments 

Standard errors in ( ) & t-statistics in [ ] 

 GDPG   

GDPG(-1) -0.623281   

  (0.14067)   

 [-4.43084]   

GDPG(-2) -0.313280   

  (0.07346)   

 [-4.26440]   

C -45.03738   

  (9.24472)   

 [-4.87169]   

CAB -0.206349   

  (0.18104)   

 [-1.13980]   

DCPS -0.461302   

  (0.09496)   

 [-4.85790]   

FCE -0.399823   

  (0.18118)   

 [-2.20674]   

GEXP  1.199383   

  (0.24124)   

 [ 4.97179]   

GFCF -0.223691   

  (0.09690)   

 [-2.30844]   

INFLATION -0.125073   

  (0.20363)   

 [-0.61422]   

POPG -5.244338   

  (1.09203)   

 [-4.80237]   

TRADEOPEN  0.489246   

  (0.10734)   

 [ 4.55807]   

R-squared  0.915674   

Adj. R-squared  0.855442   

Sum sq. resids  19.96013   



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S.E. equation  1.194037   

F-statistic  15.20228   

Log likelihood -32.65922   

Akaike AIC  3.492738   

Schwarz SC  4.029043   

Mean dependent  4.470730   

S.D. dependent  3.140479   

Source: Data processed from World Bank Database using Econometric Software, EViews 13 (Nov 2024). 

 

The results from the Vector Autoregression (VAR) model provide valuable insights 

into the dynamics of GDP growth (GDPG) in relation to several macroeconomic variables. 

Here are the key observations:   

Lagged GDP Growth: The coefficients for GDPG(-1) and GDPG(-2) are both negative 

and statistically significant, indicating that previous GDP growth rates negatively influence 

current growth. This suggests a persistence in growth rates, potentially reflecting adjustment 

processes in the economy.  

Government Expenditure (GEXP): The positive coefficient for GEXP (1.199) is 

significant, suggesting that increased government spending is associated with higher GDP 

growth. This supports the notion that productive government investment can stimulate 

economic activity.  

Domestic Credit to the Private Sector (DCPS): The negative coefficient (-0.461) and 

significant t-statistic indicate that higher domestic credit can have a negative effect on GDP 

growth. This could imply issues with credit allocation or inefficiencies in how credit is utilized 

in the economy.  

Final Consumption Expenditure (FCE): The negative coefficient (-0.399) for FCE 

suggests that increases in final consumption do not necessarily lead to GDP growth, potentially 

reflecting crowding-out effects where consumption detracts from investment.  

Gross Fixed Capital Formation (GFCF): The negative impact of GFCF (-0.223) is 

statistically significant, indicating that higher fixed capital investment may not correlate with 

GDP growth under the current conditions, possibly due to misallocation or inefficiencies. 

Inflation: The coefficient for inflation (-0.125) is not statistically significant, suggesting that 

its immediate effect on GDP growth may be negligible in the short term.  

Population Growth (POPG) : The highly negative coefficient (-5.244) shows that 

population growth has a significant negative impact on GDP growth, which could imply that 

without adequate economic opportunities, a growing population may lead to resource strain.  

Trade Openness (TRADEOPEN): The positive coefficient (0.489) indicates that 

increased trade openness positively influences GDP growth, aligning with literature that 

emphasizes the benefits of integration into global markets. Model Fit: The R-squared value of 

0.916 suggests that the model explains a substantial portion of the variance in GDP growth, 

and the F-statistic of 15.202 indicates that the overall model is statistically significant.  

Overall, these results highlight the complex interplay between macroeconomic factors 

and GDP growth in Albania, with government expenditure and trade openness emerging as 

significant positive drivers, while high domestic credit, population growth, and fixed capital 

formation present challenges to growth. 

 



Leonard BODURI, Fabian PJETRI 

59 

 

4.4 Granger casualty test 

To examine the direction of causality among the variables, we perform Granger 

causality tests (Lopez, 2018). These tests assess whether the past values of one time series 

provide useful information for forecasting another, offering insights into the predictive 

relationships between variables. 

 

Table 4: Pairwise Granger Causality Tests. 

Pairwise Granger Causality Tests 

Date: 11/05/24   Time: 02:25 

Sample: 1 27  

Lags: 2   

 Null Hypothesis: Obs F-Statistic Prob.  

 DCPS does not Granger Cause CAB  25  5.59981 0.0117 

 CAB does not Granger Cause DCPS  0.67835 0.5188 

 FCE does not Granger Cause CAB  25  0.41622 0.6651 

 CAB does not Granger Cause FCE  1.78393 0.1937 

 GDPG does not Granger Cause CAB  25  0.45147 0.6430 

 CAB does not Granger Cause GDPG  1.15504 0.3352 

 GEXP does not Granger Cause CAB  25  6.73216 0.0058 

 CAB does not Granger Cause GEXP  0.64294 0.5363 

 GFCF does not Granger Cause CAB  25  1.75646 0.1983 

 CAB does not Granger Cause GFCF  2.84306 0.0819 

 INFLATION does not Granger Cause CAB  25  0.04883 0.9525 

 CAB does not Granger Cause INFLATION  0.58834 0.5646 

 POPG does not Granger Cause CAB  25  0.24999 0.7812 

 CAB does not Granger Cause POPG  0.79031 0.4674 

 TRADEOPEN does not Granger Cause CAB  25  0.01329 0.9868 

 CAB does not Granger Cause TRADEOPEN  0.00872 0.9913 

 FCE does not Granger Cause DCPS  25  0.53969 0.5912 

 DCPS does not Granger Cause FCE  3.76079 0.0411 

 GDPG does not Granger Cause DCPS  25  0.27067 0.7656 

 DCPS does not Granger Cause GDPG  6.78899 0.0056 

 GEXP does not Granger Cause DCPS  25  1.34596 0.2829 

 DCPS does not Granger Cause GEXP  4.53606 0.0237 

 GFCF does not Granger Cause DCPS  25  1.96304 0.1666 

 DCPS does not Granger Cause GFCF  9.88816 0.0010 

 INFLATION does not Granger Cause DCPS  25  0.55772 0.5812 

 DCPS does not Granger Cause INFLATION  0.27064 0.7656 

 POPG does not Granger Cause DCPS  25  0.73279 0.4930 

 DCPS does not Granger Cause POPG  0.17729 0.8388 

 TRADEOPEN does not Granger Cause DCPS  25  2.55571 0.1027 

 DCPS does not Granger Cause TRADEOPEN  1.50146 0.2469 

 GDPG does not Granger Cause FCE  25  0.26264 0.7716 

 FCE does not Granger Cause GDPG  0.60083 0.5580 

 GEXP does not Granger Cause FCE  25  0.24847 0.7824 

 FCE does not Granger Cause GEXP  0.49219 0.6185 

 GFCF does not Granger Cause FCE  25  5.12571 0.0160 

 FCE does not Granger Cause GFCF  1.32938 0.2870 

 INFLATION does not Granger Cause FCE  25  0.35366 0.7064 

 FCE does not Granger Cause INFLATION  0.76395 0.4789 

 POPG does not Granger Cause FCE  25  0.70865 0.5043 

 FCE does not Granger Cause POPG  0.22839 0.7979 

 TRADEOPEN does not Granger Cause FCE  25  1.87841 0.1788 



ANALYZING THE DYNAMIC RELATIONSHIPS BETWEEN MACROECONOMIC 

VARIABLES AND GDP GROWTH IN ALBANIA: A PANEL DATA APPROACH 

60 

 

 FCE does not Granger Cause TRADEOPEN  0.62627 0.5447 

 GEXP does not Granger Cause GDPG  25  2.04622 0.1554 

 GDPG does not Granger Cause GEXP  1.71115 0.2061 

 GFCF does not Granger Cause GDPG  25  3.17864 0.0633 

 GDPG does not Granger Cause GFCF  3.46465 0.0511 

 INFLATION does not Granger Cause GDPG  25  2.35894 0.1203 

 GDPG does not Granger Cause INFLATION  4.88221 0.0188 

 POPG does not Granger Cause GDPG  25  1.20360 0.3209 

 GDPG does not Granger Cause POPG  0.07095 0.9317 

 TRADEOPEN does not Granger Cause GDPG  25  6.80379 0.0056 

 GDPG does not Granger Cause TRADEOPEN  0.03006 0.9704 

 GFCF does not Granger Cause GEXP  25  2.22589 0.1340 

 GEXP does not Granger Cause GFCF  1.67348 0.2128 

 INFLATION does not Granger Cause GEXP  25  0.11561 0.8914 

 GEXP does not Granger Cause INFLATION  0.30004 0.7441 

 POPG does not Granger Cause GEXP  25  0.92812 0.4117 

 GEXP does not Granger Cause POPG  0.87645 0.4316 

 TRADEOPEN does not Granger Cause GEXP  25  0.11619 0.8909 

 GEXP does not Granger Cause TRADEOPEN  0.95634 0.4012 

 INFLATION does not Granger Cause GFCF  25  6.80298 0.0056 

 GFCF does not Granger Cause INFLATION  1.27408 0.3014 

 POPG does not Granger Cause GFCF  25  0.46819 0.6328 

 GFCF does not Granger Cause POPG  0.47910 0.6263 

 TRADEOPEN does not Granger Cause GFCF  25  7.77968 0.0032 

 GFCF does not Granger Cause TRADEOPEN  0.07896 0.9244 

 POPG does not Granger Cause INFLATION  25  22.5465 7.E-06 

 INFLATION does not Granger Cause POPG  0.05735 0.9444 

 TRADEOPEN does not Granger Cause INFLATION  25  3.22437 0.0611 

 INFLATION does not Granger Cause TRADEOPEN  1.18507 0.3263 

 TRADEOPEN does not Granger Cause POPG  25  0.45611 0.6402 

 POPG does not Granger Cause TRADEOPEN  0.76895 0.4767 

Source: Data processed from World Bank Database using Econometric Software, EViews 13 (Nov 2024). 

 

The results of the pairwise Granger causality tests indicate significant relationships 

among the variables under consideration, providing valuable insights into the dynamics of 

economic factors.  

Domestic Credit and Current Account Balance: The results reveal that domestic credit 

(DCPS) Granger causes the current account balance (CAB) (F-statistic = 5.59981, p = 0.0117), 

suggesting that fluctuations in domestic credit may influence the current account. Conversely, 

CAB does not Granger cause DCPS (p = 0.5188), indicating a unidirectional relationship.   

Government Expenditure and Current Account Balance: Similarly, government 

expenditure (GEXP) is found to Granger cause CAB (F-statistic = 6.73216, p = 0.0058), 

reinforcing the notion that government spending decisions have significant implications for the 

current account. The reverse direction (CAB → GEXP) is not significant (p = 0.5363). Gross 

Fixed Capital Formation and Current Account Balance: Although GFCF does not Granger 

cause CAB (p = 0.1983), the reverse causality (CAB → GFCF) approaches significance (p = 

0.0819), suggesting a potential link that merits further investigation.  

Inflation and Current Account Balance: The lack of significant Granger causality in 

both directions between inflation and CAB (p = 0.9525 for inflation → CAB; p = 0.5646 for 



Leonard BODURI, Fabian PJETRI 

61 

 

CAB → inflation) implies that inflation may not be a leading factor for the current account 

balance.  

Population Growth and Current Account Balance: The tests indicate no significant 

Granger causality between population growth (POPG) and CAB in either direction, suggesting 

that demographic factors may not be directly influencing the current account in this context.  

Trade Openness and Current Account Balance: Trade openness (TRADEOPEN) does 

not Granger cause CAB (p = 0.9868), nor does CAB Granger cause TRADEOPEN (p = 

0.9913), highlighting a lack of causal relationship. Domestic Credit and Final Consumption 

Expenditure: The tests show that final consumption expenditure (FCE) does not Granger cause 

DCPS (p = 0.5912), but there is a significant causal link in the opposite direction (DCPS → 

FCE) (F-statistic = 3.76079, p = 0.0411).  

GDP Growth and Domestic Credit: A significant causal relationship is observed where 

DCPS Granger causes GDP growth (GDPG) (F-statistic = 6.78899, p = 0.0056), indicating that 

domestic credit plays a role in influencing economic growth. Inflation and GDP Growth: The 

causality tests reveal that inflation Granger causes GDP growth (p = 0.0188), suggesting that 

inflationary pressures may impact economic growth, while the reverse does not hold (GDPG 

→ INFLATION, p = 0.1203).  

Trade Openness and GDP Growth: Notably, trade openness Granger causes GDP 

growth (F-statistic = 6.80379, p = 0.0056), indicating that increased integration into the global 

economy may positively influence economic growth.  

Overall, the Granger causality tests provide insights into the directional relationships 

among economic variables, highlighting significant causal links, particularly from domestic 

credit and government expenditure to the current account balance and GDP growth. These 

findings underscore the importance of fiscal and monetary policies in shaping economic 

outcomes and warrant further research to explore the underlying mechanisms driving these 

relationships. 

 

5. CONCLUSIONS 

This study investigated the impact of various macroeconomic factors on GDP growth 

in Albania. The empirical analysis, employing a panel data approach, revealed that government 

expenditure and trade openness are statistically significant determinants of GDP growth. These 

findings align with economic theory, suggesting that increased government spending and trade 

integration can stimulate economic activity. 

However, the impact of other variables, such as domestic credit, final consumption 

expenditure, gross fixed capital formation, inflation, population growth, and the current 

account balance, was found to be statistically insignificant in the short run. This suggests that 

while these factors may have long-term implications for economic growth, their immediate 

impact might be less pronounced. 

The Granger causality tests provided additional insights into the dynamic relationships 

between these variables. The findings indicate that domestic credit and government 

expenditure Granger-cause the current account balance, suggesting that these factors can 

influence the country's external balance. Additionally, domestic credit and trade openness were 

found to Granger-cause GDP growth, highlighting their role in driving economic activity. 



ANALYZING THE DYNAMIC RELATIONSHIPS BETWEEN MACROECONOMIC 

VARIABLES AND GDP GROWTH IN ALBANIA: A PANEL DATA APPROACH 

62 

 

The analysis used in this article is based on a specific time period and data set, and the 

findings may not be generalizable to other contexts. Furthermore, model specification and the 

inclusion of additional variables can potentially affect the results. 

Future research may consider expanding the sample period, incorporating additional 

variables, and employing more advanced econometric techniques to provide a more 

comprehensive understanding of the determinants of GDP growth in Albania. Additionally, 

exploring the long-term effects of these variables and their potential non-linear relationships 

could yield further insights. 

 

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