This paper investigates how the presence of green credits in banks’ portfolios affects their profitability performance, providing evidence from banks operating in Vietnam.. Specifically,
Introduction
Recent research highlights the influence of green credit on the profitability of commercial banks, driven by a global shift towards environmental sustainability As awareness of ecological issues grows, there is an increasing demand for a sustainable economy that fosters development, enhances welfare, and preserves ecosystems (Sõderholm, p., 2020) In this context, green credit has emerged as a crucial policy instrument for environmental regulation, attracting significant interest from governments, businesses, and researchers worldwide (An, Y., Chen, Y., & Jin, M 2020).
Green credit, characterized by strict constraints or conditions pertaining to carbon emissions, has gained prominence as a tool for complementing sustainable economics
Green credit facilitates improved risk assessment and efficient capital allocation for environmentally friendly projects, motivating firms to invest in green initiatives By merging environmental protection with corporate social responsibility, green credit enables banks and enterprises to align their financial operations with sustainability objectives.
In Vietnam, an emerging Southeast Asian economy, banks are increasingly integrating green credits into their portfolios This shift involves adopting environmental criteria in lending decisions and embracing green financing practices, aligning with the nation's sustainable development goals.
While most research has concentrated on China, there is a notable gap in studies regarding Vietnam's context Existing findings indicate that green credit policies can enhance the profitability of commercial banks by boosting noninterest income and lowering nonperforming loan ratios Specifically, banks with lower nonperforming loan ratios experience a more substantial positive impact from these policies Additionally, regional urban and agricultural commercial banks show greater profit improvements from implementing green credit policies compared to larger national banks Conversely, some studies suggest that green credit may negatively affect overall profitability, with small and medium-sized banks facing more significant adverse impacts than their larger counterparts.
This study explores the relationship between green credit and banking profitability, highlighting that the impact can differ based on factors such as bank size, type, and specific policies In Vietnam, the effect of green credit initiatives on bank profitability remains under-researched, indicating a significant gap in understanding how these factors interact within the country's unique economic, environmental, and regulatory context Therefore, this research aims to provide empirical evidence on how green credits in banks' portfolios influence their profitability performance in Vietnam.
Our research group aims to explore the relationship between green credit integration and financial performance outcomes in the Vietnamese banking sector by analyzing financial data and performance indicators such as return on assets and return on equity The findings are expected to offer valuable insights for policymakers, stakeholders, and Vietnamese banks, enhancing strategic decision-making and promoting the adoption of sustainable finance practices Ultimately, this study seeks to support Vietnam's transition to a more sustainable and resilient financial system, aligning with global initiatives to combat climate change and foster sustainable development.
Our study employs a rigorous methodology to select the most suitable model through specification tests, including the Hausman test and the F test, ensuring both theoretical soundness and empirical validity After identifying the best-fitting model, we analyze how the inclusion of green credits in banks' portfolios influences the impact of specific bank determinants on profitability Additionally, we assess the presence of common statistical issues such as autocorrelation and heteroskedasticity, utilizing the Feasible Generalized Least Squares (FGLS) method for our analysis.
The analysis focuses on two crucial financial performance indicators: Return on Assets (ROA) and Return on Equity (ROE) By examining these indicators separately, we can better understand the influence of green credits on different facets of a bank's profitability This approach enables a more thorough evaluation of how green credits affect overall financial performance.
We employ STATA14, a robust statistical software package, to conduct a comprehensive analysis STATA provides an extensive array of features for data analysis and econometric modeling, making it an essential tool for our research.
Our methodology offers a thorough analysis of the impact of green credits on bank profitability By choosing the right model, tackling statistical challenges, and assessing two key financial performance indicators over a significant timeframe, we seek to provide valuable insights into the relationship between environmental sustainability and financial performance in the banking industry.
This paper comprises five key sections, beginning with an introduction followed by an exploration of the theoretical foundations of green banking and its relationship with bank profitability The methodology section details the variables and hypotheses formulated for the study Empirical findings are then presented, derived from a panel model analyzing the impact of green loans on profitability The paper concludes with implications and conclusions, while also addressing the research limitations.
Literature Review
Green Finance and Banking
Green finance plays a crucial role in enhancing ecological sustainability and addressing environmental damage, as highlighted by Zeng Hailiang, Wasim Iqbal, and colleagues (2023) This concept is driving significant investments in eco-friendly initiatives, reflecting the increasing global focus on environmental protection, climate change, and sustainable development Consequently, both governments and the scientific community are prioritizing green finance to foster a more sustainable future (Dr Trần Trung Kiên, 2023).
Green finance refers to the diversification of financial products and services offered by financial institutions aimed at promoting sustainable development and supporting green growth by effectively reducing greenhouse gas emissions and environmental pollution (UN Environment, 2018) In recent decades, it has gained traction in the banking sector as a strategy to safeguard both banks and society from potential future economic challenges, such as climate change, financial instability, and social unrest (Ziolo et al., 2019).
Green banking is a mission-driven approach that prioritizes clean energy financing and climate change mitigation over profit maximization (Coalition for Green Capital, 2020) This emerging trend sees banks realigning their investment strategies towards sustainable technologies and eco-friendly initiatives (Julia & Kassim, 2020) Defined by Lalon (2015), green banking encompasses any banking practice that provides ecological benefits, transforming traditional banks into green banks focused on environmental improvement This involves adopting inclusive banking practices that foster significant economic development while promoting sustainability Bihari (2011) highlights that green banking encourages social responsibility by requiring banks to assess the environmental impact of projects before funding them Ultimately, green banking shifts the focus from "profit only" to "profit with responsibility," as emphasized by Tara et al., who note the importance of financing industries that support diverse environmental protection efforts (Kanak Tara; Saumya Singh; Rilesh Kumar, 2015).
Green Credit Concept
Green credits refer to financial support from the banking sector for environmentally friendly production and business initiatives that minimize risks and promote ecological sustainability These credits play a vital role in safeguarding the environment and contributing to the overall protection of the planet's ecology.
Green credit represents a strategic financial solution employed by the industry to address global environmental and social issues As highlighted by Yuming and David (2021), it serves as a vital financial instrument in promoting sustainable finance and development, reflecting a commitment to sustainability (O V Cheberyako et al., 2021; Bao, J.).
The green credit policy serves as a vital financial instrument aimed at achieving "carbon peaking" and "carbon neutrality" objectives by providing essential financial support for businesses' green growth Its main focus is to mitigate pollution emissions from businesses Recent studies primarily concentrate on the risks associated with the implementation of green credit policies and their resultant effects.
The green credit policy addresses the increasing shortage of green capital by reallocating funds from heavily polluting industries to environmentally friendly businesses This shift enhances energy efficiency and strengthens environmental governance Additionally, the policy fosters innovation and modernizes industrial structures, leading to improved environmental quality and increased green total factor productivity.
Green credit policies at the micro level enhance financial institutions' ability to monitor fund recipients and improve their financial and environmental performance, thereby contributing to sustainable development However, the implementation of these policies has not significantly reduced the non-performing loan ratio and has negatively affected banks' return on equity Consequently, while there are essential requirements for executing green credit policies, their overall impact may not be entirely beneficial for corporate development.
2.3 Theoretical Fra tn ework and Hypotheses Development
2.3.1 The theoretical framework for the effect of the green credits on bank’s profitability'
Competitive strategy theory posits that green credit allows commercial banks to capitalize on the opportunities presented by green economic growth, thereby creating new profit avenues and enhancing their competitive advantage (Hart, 1995) By leveraging green financing, these banks can not only increase their assets but also strengthen their reputations in the market (Bukhari et al., 2019; Yonghui et al., 2022).
Stakeholder theory emphasizes that companies should prioritize not only shareholder wealth but also the interests of various stakeholders It advocates for businesses to assess the social impacts of their decisions alongside financial performance By achieving positive environmental outcomes and practicing effective environmental management, companies can enhance stakeholder expectations and bolster their brand image.
Environmental risk management theory posits that the default risk associated with environmental challenges is now a fundamental component of banking risk When debtors face environmental risks, it can lead to defaults that affect commercial banks through their lending activities Consequently, it is imperative for commercial banks to effectively manage environmental risk to safeguard their financial stability.
2.3.2 The empirical evidence on the effect of the presence of green credits on bank’s profitability
Research indicates a positive relationship between green credit policies and bank performance, with studies showing that green banking enhances liquidity and capital adequacy, ultimately boosting profitability Green credits not only improve financial performance but also serve as a revenue source by supporting various business functions such as project management and fund allocation, which attracts intermediary businesses Implementing these policies can lead to increased noninterest revenue and reduced nonperforming loan ratios, as banks with higher green lending ratios tend to experience lower nonperforming loans Additionally, adopting environmental practices and equator principles enhances liquidity and profitability, reflecting a bank's commitment to social responsibility and enabling the provision of higher-quality products and services for long-term gains.
Research indicates an inverse relationship between green credit and banks' profitability, highlighting that rising operating expenses negatively impact financial performance Comparative analyses reveal that banks offering green credits experience lower profitability than those that do not Furthermore, empirical studies have shown that green loans adversely affect banks' return on assets, reinforcing the notion of their detrimental effect on financial performance.
To address the challenges of green credit, it is essential to compensate for the low interest rates and high risks associated with it (Wang & Zhang, 2014) Scholars argue that banks must enhance their ability to evaluate environmental and social risks and opportunities to increase their green credit ratios (Chang & Sam, 2015) However, the mixed outcomes and insufficient empirical research on the financial risks of green credit highlight a significant gap in the existing literature.
Theoritical Framework and Hypotheses Development
to bank size, liquidity, capital adequacy Our model includes the presence of green credits in banks portfolio as a moderator:
Therefore, in line with prior literature, the effects of green credits on banks1 profitability are hypothesized as follows:
Hl: The presence of green credits affects the influences of bank-specific determinants on a bank’s ROA.
H2: The presence of green credits affects the influences of bank-specific determinants on a bank’s ROE.
Data and Methodology
Research Model
In this study, we have adapted the research model from Mirovic et al (2023) to better align with our objectives, ensuring it is tailored for our specific analysis.
ROAit=0+1 SIZEit+2LDRit+3CAit+4GCit+it
ROEit=0+lSIZEit+2LDRit+3CAit+4GCit+it
ROA: Return on assets ROE: Return on equity SIZE: Bank size
LDR: Bank liquidity ratio CA: Bank capital adequacy
GC: Dummy variable - green credits
GC = 0 if banks without green credits GC = 1 if banks with green credits i: Bank, t: Year, it: Error of model
In this study, we utilize specification tests, including the Hausman test and the F test, to identify the most suitable model, ensuring both theoretical robustness and empirical validity After developing the optimal model, we will explore how incorporating green credits into bank portfolios influences the impact of various bank-specific factors on profitability This investigation is crucial as it sheds light on the relationship between environmental and financial variables within banking institutions.
In the next phase of our analysis, we will evaluate our model for two common statistical issues: autocorrelation and heteroskedasticity Autocorrelation refers to the relationship between error components in a time series, while heteroskedasticity indicates that the variance of error terms changes across different observations.
Neglecting these issues can lead to biased and inefficient parameter estimates To mitigate these problems, we utilize the Feasible Generalized Least Squares (FGLS) method, a statistical technique that accounts for the structure of error terms By adjusting the regression model accordingly, FGLS enhances the reliability and accuracy of predictions.
We conduct separate analyses for two crucial financial performance indicators: Return on Assets (ROA) and Return on Equity (ROE) This distinction enhances our understanding of how green credits impact different aspects of a bank's profitability ROA measures a bank's efficiency in generating profits from its assets, while ROE evaluates profitability in relation to shareholder equity By individually assessing both metrics, we can deliver a more comprehensive evaluation of the influence of green credits on financial performance.
Our research spans from 2015 to 2022, offering valuable insights into the long-term effects of integrating green credits into bank portfolios This seven-year timeframe provides an extensive dataset, enabling us to identify trends and shifts over the years.
Results and Discussions
Descriptive statistics results
Mean Standard Deviation Min Max
Table 1 presents the descriptive statistics of the variables analyzed The findings reveal that the average profitability of 30 banks in Vietnam, assessed through Return on Assets (ROA) and Return on Equity (ROE), was 0.90708% and 10.39465%, respectively, during the period from 2015 to 2022.
In Vietnam, banks significantly increase their leverage by taking on more debt relative to their total assets, which leads to a higher Return on Equity (ROE) compared to Return on Assets (ROA).
Furthermore, selected banks had an average liquidity of 0.788335, which was smaller than 1 implies that the total loans of banks in Vietnam are usually smaller than total deposits.
Mean capital adequacy was 6.2001% However currently, the bank’s capital adequacy in Vietnam is calculated according to Circular 41 in 2016 approaching international standards Basel II, which is set at a minimum of 8%.
Multicollinearity test by VIF
Table 2 presents the results of the multicollinearity test using the Variance Inflation Factor (VIF) A VIF value exceeding 4 or a tolerance below 0.25 suggests potential multicollinearity, warranting further investigation If the VIF surpasses 10 or tolerance falls below 0.1, significant multicollinearity is present and requires correction However, the results in Table 2 indicate that since the VIF is less than 4, multicollinearity does not exist in this model.
Panel unit root tests
Table 3 - Panel Unit Root Test (Fisher-Type Test)
Modified Inv. Inverse Chi- Inverse Inverse
Chi-Squared Squared (P) Normal (Z) Logit (L)
Table 3 presents the panel unit root test results for the variables The findings indicate that both LDR and CA significantly reject the null hypothesis, suggesting that all panels are free from unit roots, with p-values below 0.05 This indicates that the estimated panels are stationary at the level.
The results of the unit root tests indicate that the null hypothesis cannot be rejected, as all panel IP values exceed 0.05 This outcome may be attributed to the relatively small scale of the observations.
Regression results
Table 4 - Regression Results of bank’s ROA with moderation
(0 - Banks without green credits, 1 - Banks with green credits)
ROA Coefficient Prob Coefficient Prob Coefficient Prob.
*** Significant at the 1 percent level.
** Significant at the 5 percent level.
* Significant at the 10 percent level.
Table 5 - Regression results of bank’s ROE with moderation
(0 - Banks without green credits, 1 - Banks with green credits)
*** Significant at the 1 percent level.
** Significant at the 5 percent level.
* Significant at the 10 percent level.
Table 4 and 5 show the regression results of ROA and ROE using Pooled OLS, Fixed-
Effects Model and Random-Effects Model.
F-test test to check if FEM or Pooled OLS is more suitable
Model Specification (ROA) Prob > F = 0.0000 FEM is more suitable
Model Specification (ROE) Prob > F = 0.0000 FEM is more suitable
The F-test results indicate a value of 0.000, suggesting that the Fixed-Effects Model is more appropriate than the Pooled OLS Model for estimating the profitability of banks with moderation.
Hausman test to check if the FEM or REM is more suitable
Model Specification (ROA) Chi2(6) = 80.00 FEM is more suitable
Model Specification (ROE) Chi2(6) = 24.02 FEM is more suitable
The results of the Hausman test, as presented in Table 7, indicate that the p-values are below 0.05, suggesting that a Fixed-Effects Model is appropriate for analyzing banks' profitability with moderation Consequently, the Fixed-Effects Model emerges as the most suitable choice for this research.
A utocorrelation test
Perform a Wooldridge test to check the phenomenon of autocorrelation in the model: HO: Autocorrelation does not exist in this model.
Hl: Autocorrelation exists in this model.
Table 8 shows the result of Wooldrige test Because the p-value = 0.000 < 0.05, we reject hypothesis HO and accept hypothesis H1 Thus, the Autocorrelation exists in these models.
Heteroskedasticity test
The Fixed-Effects Model has been determined to be the most appropriate model based on the conducted tests To assess the presence of heteroskedasticity within the fixed-effects regression model, the author performed a Modified Wald test.
HO: Heteroskedasticity does not exist in this model.
Hl: Heteroskedasticity exists in this model.
Modified Wald test Results Conclusion
Model Specification (ROA) Chi2(31 ) = 597.21 Hctcroskcdasticity exists
Table 9 shows the result of Modified Wald test Because the p-value = 0.000 < 0.05, we reject hypothesis HO and accept hypothesis Hl Thus, the Heteroskedasticity exists in these models.
Fix Heteroskedasticity and Autocorrelation using FGLS Method
Table 10 - Comparison using FEM and FGLS to regress bank’s ROA with moderation
(0 - Banks without green credits, 1 - Banks with green credits)
Variables Coefficient Prob Coefficient Prob.
** Significant at the 5 percent level.
* Significant at the 10 percent level.
As can be seen in Table 10, the obtained results showed that the bank’s size significantly positively affects ROA at both bank groups, (since p-values’ both groups
The loan-to-deposit ratio, a key indicator of a bank's liquidity, shows that the inclusion of green loans in a bank's portfolio mitigates its impact on the bank's return on assets (ROA) While this ratio positively influences ROA across both bank categories, its effect is notably significant for banks that do not offer green loans.
Capital adequacy has a notably positive effect on Return on Assets (ROA) for both groups of banks Notably, banks that offer green credits demonstrate a superior performance in ROA compared to those that do not engage in green lending.
Table 11 - Comparison using FEM and FGLS to regress bank's ROE with moderation
(0 - Banks without green credits, 1 - Banks with green credits)
Variables Coefficient Prob Coefficient Prob.
*** Significant at the 1 percent level.
** Significant at the 5 percent level.
* Significant at the 10 percent level.
The results in Table 11 reveal that, regardless of whether banks provide green credits, their size has a significant positive effect on Return on Equity (ROE) In contrast, capital adequacy does not show a significant impact, with one coefficient being negative for banks without green credits and the other positive for those offering green credits.
Green loans in a bank's portfolio moderate the impact of the loan-to-deposit ratio, a key indicator of liquidity, on the bank's return on equity (ROE) While this ratio positively influences ROE for both types of banks, its significance is particularly pronounced in banks that do not offer green credits.
Tables 10 and 11 reveal that the inclusion of green credits significantly impacts the relationship between bank-specific determinants and a bank's Return on Assets (ROA) and Return on Equity (ROE) Specifically, the integration of green credits into a bank's portfolio leads to a notable negative effect on how liquidity influences both ROA and ROE.
Table 12 - The Results of Sub-Hypotheses
Conclusions and Implications
Implications
This study reveals that the inclusion of green credits in a bank's portfolio significantly impacts profitability, providing crucial insights for banks in Vietnam By recognizing these implications, banks can effectively utilize green credit policies to boost their financial performance and contribute to sustainable development.
This research examines the impact of green credits in bank portfolios on profitability, specifically through the metrics of Return on Assets (ROA) and Return on Equity (ROE) The study selects independent variables such as bank size, liquidity, and capital adequacy based on insights from prior research.
The presence of green credits in banks' portfolios, though seemingly minor, positively influences the relationship between bank size and capital adequacy, as evidenced by differing coefficients across bank groups By adopting environmentally friendly practices, banks can enhance their non-financial performance, including reputation and customer loyalty, ultimately boosting their competitive advantage.
The study reveals that the inclusion of green credits in bank portfolios positively influences profitability by mitigating the effect of the loans-to-deposit ratio on both Return on Assets (ROA) and Return on Equity (ROE) By allocating funds to environmentally friendly projects when approving green credits, banks alter the relationship between their liquidity ratios and overall profitability.
Our research indicates that the positive impact of loans-to-deposit ratios is significant primarily for banks that do not offer green credits This may be attributed to government support through tax incentives and stable commitments for businesses investing in environmentally focused projects Banks that provide long-term capital at preferential interest rates may experience reduced profitability The significant relationship between loans-to-deposit ratios and bank profitability is evident only in institutions lacking green credit options, suggesting that banks should adopt green credit policies to alleviate liquidity risks and enhance profitability.
In summary, integrating green credits into a bank's portfolio can greatly impact profitability and performance Vietnamese banks can utilize this study's insights to guide strategic decisions and improve financial outcomes while supporting sustainable development By adopting green credit policies, establishing strong risk assessment frameworks, collaborating with eco-friendly businesses, enhancing transparency, and investing in employee training, banks can emerge as leaders in sustainable finance, attract socially responsible investors, and play a vital role in environmental preservation.
Limitations
This study has several limitations, including its reliance on data from only 30 banks listed with the State Bank of Vietnam between 2015 and 2022 The sample size is also quite small, comprising just 240 observations Additionally, the dummy variable for Green Credits was derived solely from the analysis of banks' annual reports, as the concept of green credits is still relatively new, which may lead to potential inaccuracies.
Despite its limitations, this study significantly contributes to the literature on green banking It employs a quantitative research design, suggesting that future research should gather more in-depth data from respondents, potentially including banks in Asia and globally To address these limitations, additional studies are essential.
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- Part 1: Experiences From Some Countries.
SUB ROA ROE SIZE LDR CA
Variable Obs Mean std Dev Min Max
Appendix 2 - Multicollinearity test by VIF vif
SIZE CA LDR Mean VIF
Appendix 3 - Panel unit root tests
xtunitroot fisher SIZE, dfuller lags(0) could not compute test for panel 13
Fisher-type unit-root test for SIZE
Based on augmented Dickey-Fuller tests
Ho: All panels contain unit roots Number of panels = 31 Ha: At least one panel is stationary Avg number of periods = 7.74
AR parameter: Panel-specific Asymptotics: T -> Infinity
Drift term: Not included ADF regressions: 0 lags
Modified inv chi-squared Pm -0.4732 0.6820 p statistic requires number of panels to be finite.
Other statistics are suitable for finite or infinite number of panels.
xtunitroot fisher LDR, dfuller lags(0) could not compute test for panel 13
Fisher-type unit-root test for LDR
Based on augmented Dickey-Fuller tests
Ho: All panels contain unit roots
Ha: At least one panel is stationary
Number of panels = 31 Avg number of periods = 7.74
ADF regressions: 0 lags Statistic p-value
Modified inv chi-squared Pm 7.1178 0.0000 p statistic requires number of panels to be finite.
Other statistics are suitable for finite or infinite number of panels.
xtunitroot fisher CA, dfuller lags(O) could not compute test for panel 13
Fisher-type unit-root test for CA
Based on augmented Dickey-Fuller tests
Ho: All panels contain unit roots
Ha: At least one panel is stationary
Number of panels = 31 Avg number of periods = 7.74
AR parameter: Panel-specific Asymptotics: T -> Infinity
Not included ADF regressions: 0 lags
121.2977 -2.9015 -3.3479 Modified inv chi-squared Pm 5.5957
0.0000 0.0019 0.0005 0.0000 p statistic requires number of panels to be finite.
Other statistics are suitable for finite or infinite number of panels.
reg ROA GC#c.SIZE GC#c.LDR GC#c.CA
ROA Coef std Err t p>ltl [95% Conf Interval]
reg ROE GC#c.SIZE GC#c.LDR GC#c.CA
Sou rce ss df MS
Prob > F R-squa red Adj R-squared Root MSE
Coef std Err t p>ltl 195% Conf Interval]
xtreg ROA GC#c.SIZE GC#c.LDR GC#c.CA, fe
Number of obs Number of groups
Obs per group: min = avg = max = 8 corr(u_i, Xb) -0.7038
Coef std Err t p>ltl [95% Conf Interval]
00911288 00272388 91798378 (fraction of variance due to u_i)
xtreg ROE GC#c.SIZE GC#c.LDR GC#c.CA, fe
Number of obs Number of groups
Obs per group: min = avg max 8 corr(u_i, Xb) -0.6791
ROE Coef 5td Err t p>lt 1 [95% Conf Interval!
07182456 03100938 84288781 (fraction of variance due to u_i)
xtreg ROA GC#c.SIZE GC#c.LDR GC#c.CA, re
Number of obs Number of groups
Obs per group: min = avg = max = 8 corr(u_i, X) 0 (assumed)
ROA Coef std Err z P>IN [95% Conf Interval]
00403143.00272388.68656876 (fraction of variance due to u_i)
xtreg ROE GC#c.SIZE GC#c.LDR GC#c.CA, re
R-sq: within = 0.4781 between = 0.4027 overall = 0.3961 corr(u_i, X) =0 (assumed)
Number of obs = 240 Number of groups = 31
Obs per group: min = 1 avg = 7.7 max = 8
ROE Coef std Err z p> 1 z 1 [95% Conf Interval!
0399819.03100938.6244019 (fraction of variance due to u_i)
1 0903831 1100567 -.0196736 ■ b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(6) = (b-B) ' Í (V_b-V_Br (-1)] (b-B)
Prob>chi2 = 0.0000(V_b-V_B is not positive definite)
0066876 0072144 0268015 b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(6) = (b-B)'HV_b-V_B)A(-l)J(b-B)
Prob>chi2 = 0.0005 (V_b-V_B is not positive definite)
ROA xtserial ROA SIZE LDR CA
Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation
ROE xtserial ROE SIZE LDR CA
Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation
Modified Wald test for groupwise heteroskedasticity in fixed effect regression model
Modified Wald test for groupwise heteroskedasticity in fixed effect regression model
Appendix 10 - Using FGLS to fix Autocorrelation and Heteroskedasticity
xtgls ROE GC#c.SIZE GC#c.LDR GC#c.CA, p(h) c(a)
(note: 1 observations dropped because only 1 obs in group)
Cross-sectional time-series FGLS regression
Correlation: common AR(1) coefficient for all panels (0.6846)
Number of obs = 239 Number of groups - 30 Obs per group: min = 7 avg = 7.966667 max = 8