The overall objective of the thesis is to study the impact of corporate governance on risks

and financial performance of commercial banks in Vietnam in the period from 2011 to

2017 and at the same time the thesis also study Interactive relationship between risks and

financial performance in the context of corporate governance. To verify the hypotheses

proposed in Chapter 3, after the process of analyzing and testing errors of the model, the

thesis uses regression model and GLS method for the dependent variable of financial

performance (measured by ROA, ROE and NIM) and SGMM method for the dependent

variable of bank risk (measured by Z-Score and NPL). At the same time, the thesis uses

regression model and GLS method to test the relationship between risks and financial

performance of commercial banks in Vietnam

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Economic
growth
Bank risk
Z-Score
NPL
ROA
ROE
NIM
Financial
11
Figure 3.1. Research process
Research issues
Impact of Corporate Governance on risks and financial efficiency
Objectives of the study
- Testing the impact of CG on risks of commercial banks in Vietnam.
- Testing the impact of CG on financial efficiency of commercial banks in Vietnam.
- Proposing a number of policy implications to improve the CG management capacity,
limit risks and improve financial efficiency of commercial banks in Vietnam.
- Theoretical foundations of CG, risks and financial efficiency
- Research overview of the impact of CG on risks and financial efficiency of banks
Conclusions and recommendations
Results and discussions
Using the SGMM method for goal 1 Using GLS method for goal 2
Research Methods
Collecting and processing data, analyzing and estimating models
Study gaps and analytical framework
Proposing an experimental research model
12
3.2. Research data
The study uses secondary data with a data sample of 29 commercial banks in Vietnam
from 2011-2017. As of December 31, 2017, according to the State Bank’s statistics, the
number of commercial banks is 35 banks (including 7 State-owned commercial banks and
28 joint-stock commercial banks). The total assets of 35 commercial banks as of December
31, 2017 are VND 8,598,594 billion, while the total assets of the 29 commercial banks
used by the author as of December 31, 2017 are VND 7,761,728 billion, accounting for
90.3% of total assets of commercial banks. Thus, 29 commercial banks are selected by the
author which ensure to represent commercial banks in Vietnam (Appendix 1).
In which, the State-owned commercial banks include 7 banks: Vietnam Bank for
Agriculture and Rural Development, Vietnam Joint Stock Commercial Bank for Industry
and Trade (CTG), Joint Stock Commercial Bank for Foreign Trade of Vietnam (VCB),
Joint Stock Commercial Bank for Investment and Development of Vietnam (BID),
Vietnam Construction Commercial Joint Stock Bank, Global Petro Sole Member Limited
Commercial Bank, Ocean Limited Company. In the sample, due to limited information, the
state-owned commercial banks are included 3 banks by the author: CTG, VCB and BID.
The remaining 26 banks of the sample belong to joint stock commercial banks. According
to updated data as of December 31, 2017, the number of banks listed on HOSE and HXN
exchanges was 10 banks, including: CTG, VCB, BID, ACB, EIB, MBB, NCB, SHB, STB
and VPB. The remaining samples are unlisted banks.
Data for calculating internal variables inside the bank are collected from annual reports,
audited consolidated financial statements, corporate governance reports, annual
shareholder meeting documents of commercial banks.
Data for calculating external factors in the macro environment are collected from official
sources such as the World Economic Outlook (WEO) data set of the International
Monetary Fund (IMF), General Statistics Office of Vietnam.
The data is collected and selected after eliminating banks that does not disclose
information or disclose incomplete information, resulting in a sample of balance sheet data
study consisting of 29 banks with 203 observations to be used for research purposes.
Therefore, the data set will be in a balanced form and is presented in Appendix 2.
3.3. Measuring the impact of corporate governance on the risks of commercial banks
in Vietnam
3.3.1. Research models
+ Based on the research models of Pathan and Faff (2013), Dong et al (2017), this study
applies the following regression model 1 as follows:
𝐑𝐢𝐬𝐤𝑖𝑡 = 𝛼0 + 𝛼 ∗ 𝐑𝐢𝐬𝐤𝑖𝑡−1 + 𝛾 ∗ 𝐶𝐺𝑖𝑡 + 𝛿 ∗ 𝑋𝑖𝑡 + 𝜀𝑖𝑡 (1)
Of which:
13
𝛼0: Coefficient of original coordinates;
i: Cross data of banks;
t: Current year (t = 1,.,k); Risk𝑖𝑡: Risk of bank i (Z-score, NPL) at time t;
CGit: The variables representing the Corporate Governance of bank i at time t, including:
size of the Board of Directors (Bsize), Number of Independent Members of Board of
Directors (Bindep), Number of female members of Board of Directors (Femdir), The
percentage of foreign members of Board of Directors (Fordir), the percentage of members
of Board of Directors taking part in management (Execdir), and Education level of Board
of Board of Directors (Edu).
The variable adjusted to suit the Vietnamese context in the research model is the variable
of the percentage of members of Board of Directors taking part in management (Execdir),
as defined in Paragraph 1, Article 34 of the 2010 Law on Credit Institutions of Vietnam:
“Chairman of the Board of Directors, Chairman of the Board of Members of a credit
institution must not concurrently be the executive of that credit institution and of other
credit institutions”. Therefore, in this thesis, the author uses the variable of the members of
Board of Directors to participate in the management as compared to the previous studies,
the majority used the duality variable (Chairman of the Board of Directors cum CEO).
Xit: The control variables include bank characteristics and macro variables: bank size
(SIZE), size of lending activities (LAR), equity size (CAP), Loan to Deposit ratio (LDR),
Bank Liquidation (LIQ), Management Effectiveness (CTI), Listed Bank (List) and
Economic Growth (Ecogrow).
𝛼 ,γ,δ: Are the estimated coefficient vectors.
ε𝑖𝑡:: Is the standard error.
3.3.2. Measurement of variables in the research model
3.3.2.1. Risk dependent variable
The thesis measures the risks of Vietnamese commercial banks by the Z-score bankruptcy
risk index inherited from the research of Boyd and Graham (1986), Goyeau and Tarazi
(1992); Barry et al (2011) and Lepetit and Strobel (2013) and ratio of bad debts (NPL).
- Z-score is calculated based on the following formula:
𝑍𝑖𝑡 = 𝑅𝑂𝐴𝑖𝑡 + 𝐸𝑇𝐴𝑖𝑡𝜎(𝑅𝑂𝐴)
In which:
+ ROA: Return on assets of bank i at time t.
+ ETA: Equity to asset ratio of bank i at time t.
14
+ σ(ROA): Standard deviation of returns on the assets of the entire sample.
A higher Z-score indicates that the bank is more stable and less risky. Because the Z-score
has a high deviation, according to Laeven and Levine (2009), to reduce the bias, the
natural logarithm of Z-score should be used. Z-score is often used in studies to measure
bank risk (for example: Angkinand and Wihlborg, 2010; Barry et al, 2011; Demirgüç-Kunt
and Huizinga, 2013; Laeven and Levine, 2009) .
- The traditional risk of banks is often related to lending and is measured by the ratio of
bad debt to total outstanding debt (NPL), which reflects the quality of a bank’s assets
(Demirgüç –Kunt et al, 2006; Shehzad et al., 2010; and Delis and Kouretas, 2011).
Because bad debts cause losses for banks, high NPL ratios lead to high credit risks (Delis
and Kouretas, 2011).
3.2.2.2. Independent variables for corporate governance in the model
a) Size of Board of Directors
Hypothesis 1a (H1a): Large size of Board of Directors is positively correlated with the
bank’s Z-Score.
Hypothesis 1b (H1b): Large size of Board of Directors is negatively correlated with the
bank’s NPL.
b) Independent members of Board of Directors
Hypothesis 2a (H2a): The percentage of independent members of the Board of Directors is
positively correlated with the bank’s Z-Score.
Hypothesis 2b (H2b): The percentage of independent members in the Board of Directors is
negatively correlated with the bank’s NPL.
c) Female members of Board of Directors
Hypothesis 3a (H3a): The high percentage of female board members is positively
correlated with the bank’s Z-Score.
Hypothesis 3b (H3b): The high percentage of female members in the Board of Directors is
negatively correlated with the bank’s NPL.
d) Foreign members of Board of Directors
Hypothesis 4a (H4a): The high percentage of foreign members of Board of Directors is
positively correlated with the bank’s Z-Score.
Hypothesis 4b (H4b): The high percentage of foreigner members of Board of Directors is
positively correlated with the bank’s NPL.
e) Percentage of members of Board of Directors taking part in management
Hypothesis 5a (H5a): The high percentage of board members taking part in management
is negatively correlated with the bank’s Z-Score.
15
Hypothesis 5b (H5b): The high percentage of members of Board of Directors taking part
in management is negatively correlated with the bank’s NPL.
f) Education level of Board of Directors
Hypothesis 6a (H6a): The high percentage of members of Board of Directors with
postgraduate degrees is significantly correlated with the bank’s Z-Score.
Hypothesis 6b (H6b): The high percentage of board members with postgraduate degrees is
significantly correlated with the bank’s Z-Score.
3.3.2.3. Control variables in the model
Table 3.1. Description of the variables used in the regression model 1
Variable Measurement method Scientific basis
Expected mark
Z-Score NPL
Dependent variable (Risk)
NPL NPL to total outstanding loan
PL NPL to total outstanding
loan Dong et al (2014),
Berger et al (2014), Berger
et al (2016), Calomiris and
Carlson (2016), Dong et al
(2017), Skała and Weill
(2018)
Z-Score
= ln (𝑅𝑂𝐴𝑖𝑡 + 𝐸𝑇𝐴𝑖𝑡
𝜎(𝑅𝑂𝐴) )
Pathan (2009); Anginer et al
(2014); Dong et al (2014);
Chan et al (2016); Berger et
al (2016); Mollah et al
(2017); Ben Zeineb and
Mensi (2018); Skała and
Weill (2018); Setiyono and
Tarazi (2018)
Independent variable (Corporate Governance - representative variable of Corporate
governance)
Bsize
Natural logarithm of BOD Berger et al (2014), Dong et
al (2017), Ben Zeineb and
Mensi (2018)
+ -
Bindep
Percentage of independent
members of Board of
Directors/Total members
Chan et al (2016), Dong et
al (2017) + -
16
Variable Measurement method Scientific basis
Expected mark
Z-Score NPL
of Board of Directors
Femdir
Percentage of female
members of Board of
Directors/Total members
of Board of Directors
Dong et al (2014), Dong et
al (2017)
+ -
Fordir
Percentage of foreign
members of Board of
Directors/Total members
of Board of Directors
Dong et al (2017)
+ -
Execdir
Percentage of members of
the Board of Directors
taking part in
management/Total
number of members of
Board of Directors
The author proposes to
comply with Paragraph 1,
Article 34 of the Law on
Credit Institutions in 2010
of Vietnam.
- -
Edu
Percentage of members of
Board of Directors with
postgraduate level/Total
number of members of
Board of Directors
Berger et al (2014), Chan et
al (2016), Setiyono and
Tarazi (2018) + -
Control Variables
Size
Natural logarithm of total
assets
Pathan (2009), Berger et al
(2014), Dong et al (2014),
Chan et al (2016), Ben
Zeineb and Mensi (2018),
Setiyono and Tarazi (2018)
+ -
LAR Outstanding loans divided by total assets
Berger et al (2014), Dong et
al (2017) + -
CAP Equity divided by total assets
Chan et al (2016), Mollah et
al (2017) + -
LDR Outstanding loans divided by customer deposits
Dong et al (2017), Ben
Zeineb and Mensi (2018) - -
LIQ Liquidated assets divided by total assets
Dong et al (2017) + -
17
Variable Measurement method Scientific basis
Expected mark
Z-Score NPL
CTI Total operating cost divided by total income
Dong et al (2014) - +
List By 1, the listed bank, by 0 otherwise
Dong et al (2014) + -
Ecogrow Annual growth ratio of GDP
Dong et al (2017), Ben
Zeineb and Mensi (2018) + -
3.4. Measuring the impact of corporate governance on the financial performance of
commercial banks in Vietnam
3.4.1. Research models
+ Based on the research models of Pathan and Faff (2013), Dong et al (2017), this study
proposes the Regression Equation 2 as follows:
FPit = α0 + γ * CGit + δ * Xit + εit (2)
In which:
α0: Coefficient of original coordinate;
i: Cross data of banks;
t: Current year (t = 1, ...., k);
FPit: Financial performance of bank i (ROA, ROE, NIM) at time t;
CGit: Are variables representing the Corporate Governance of bank i at time t, including:
size of the Board of Directors (Bsize), the percentage of Independent Members of Board of
Directors (Bindep), Number of female members of Board of Directors (Femdir), the
percentage of foreign members of Board of Directors (Fordir), the percentage of members
of Board of Directors taking part in management (Execdir), and Education level of Board
of Directors (Edu).
The variable adjusted to suit the Vietnamese context in the research model is the variable
of the percentage of members of Board of Directors taking part in management (Execdir),
as defined in Paragraph 1, Article 34 of the 2010 Law on Credit Institutions of Vietnam:
“Chairman of the Board of Directors, Chairman of the Board of Members of a credit
institution must not concurrently be the executive of that credit institution and of other
credit institutions”. Therefore, in this thesis, the author uses the variable of members of
Board of Directors taking part in management as compared to the previous studies, the
majority used the part-time variable (Chairman of Board of Directors cum CEO).
18
Xit: The control variables include bank characteristics and macro variables: bank size
(SIZE), size of lending activities (LAR), equity size (CAP), Loan to Deposit ratio (LDR),
Bank Liquidation (LIQ), Management Effectiveness (CTI), Listed Bank (List) and
Economic Growth (Ecogrow).
𝛼 ,γ,δ: Are the estimated coefficient vectors.
ε𝑖𝑡:: Is the standard error.
3.4.2. Measure of variables in the research model
3.4.2.1. The variable depends on financial performance
- The thesis measures the financial performance of Vietnamese commercial banks by the
ratios: Return on Assets (ROA) is a key parameter of management performance. It shows
the ability of the Board of Directors in the process of converting a bank’s assets into net
income and inherited from research by De Andres and Vallelado (2008); Lin and Zhang
(2009); Grove et al (2011); Adams and Mehran (2012); Liang et al (2013); García-Meca et
al (2015). ROA is calculated based on the following formula:
𝑅𝑂𝐴 = 𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠 𝑎𝑓𝑡𝑒𝑟 𝑡𝑎𝑥
𝑇𝑜𝑡𝑎𝑙 𝑎𝑠𝑠𝑒𝑡𝑠
- Return on equity (ROE) is an indicator measuring the percentage of income for the
bank’s shareholders. It represents the income that shareholders receive from investing in a
bank and inherited from research by Staikouras et al (2007); Lin and Zhang (2009); Rowe
et al (2011); Westman (2011); Fahlenbrach and Stulz (2011); Aebi et al (2012); Liang et al
(2013); Elyasiani and Zhang (2015). ROE is calculated based on the following formula:
𝑅𝑂𝐸 = 𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠 𝑏𝑒𝑓𝑜𝑟𝑒 𝑡𝑎𝑥
𝐸𝑞𝑢𝑖𝑡𝑦
- Net Interest Margin (NIM) is one of the most important measures for measuring financial
performance in a deposit taking institution (Golin, 2001). Because it usually accounts for
70-85% of a bank’s total income, the higher the percentage, the higher its profit. In
particular, in Vietnam, credit activities make up the main profit in banking activities. Net
Interest Margin is calculated by the following formula:
NIM = Interest returns – Interest paid
Total assets
3.4.2.2. Independent variables for corporate governance in the model
a) Size of Board of Directors
Hypothesis 1c (H1c): The size of the Board of Directors has positive impacts on the
financial performance of the bank.
19
b) Independent members of Board of Directors
Hypothesis 2c (H2c): The percentage of independent members in the Board of Directors
has positive impacts on the financial performance of the bank.
c) Female members of Board of Directors
Hypothesis 3c (H3c): The high percentage of female members in the Board of Directors
has positive impacts on the financial performance of the bank.
d) Foreign members of Board of Directors
Hypothesis 4c (H4c): The high percentage of foreign members in the Board of Directors
has positive impacts on the financial performance of the bank.
e) Ratio of members of Board of Directors taking part in management
Hypothesis 5c (H5c): The high percentage of members of the Board of Directors taking
part in management has negative impacts on the financial performance of the bank.
f) Education level of Board of Directors
Hypothesis 6c (H6c): The percentage of members of the Board of Directors with
postgraduate degrees has positive impacts on the financial performance of the bank.
3.4.2.3. Control variables in the model
3.5. Measuring the relationship between risks and financial performance of
commercial banks in Vietnam
+ Regression equation 3 is as follows:
𝐑𝐢𝐬𝐤𝑖𝑡 = 𝛼0 + 𝛼 𝐅𝐏𝑖𝑡 + 𝛾 ∗ 𝐶𝐺𝑖𝑡 + 𝛿 ∗ 𝑋𝑖𝑡 + 𝜀𝑖𝑡+ (3)
+ Regression equation 4 is as follows:
𝐅𝐏𝑖𝑡 = 𝛼0 + 𝛼 ∗ 𝐑𝐢𝐬𝐤𝑖𝑡 + 𝛾 ∗ 𝐶𝐺𝑖𝑡 + 𝛿 ∗ 𝑋𝑖𝑡 + 𝜀𝑖𝑡 (4)
In which:
α0: Coefficient of original coordinates;
i: Cross data of banks;
t: Current year (t = 1, ...., k);
Riskit: Risks of bank i (Z-score, NPL) at time t;
FPit: Financial performance of bank i (ROA, ROE, NIM) at time t;
CGit: Are variables representing the Corporate Governance of bank i at time t, including:
size of the Board of Directors (Bsize), the percentage of independent members of Board of
Directors (Bindep), Number of female members of Board of Directors (Femdir), the
percentage of foreign members of Board of Directors (Fordir), the percentage of members
20
of Board of Directors taking part in management (Execdir), and Education level of Board
of Directors (Edu).
Xit: The control variables include bank characteristics and macro variables: bank size
(SIZE), size of lending activities (LAR), equity size (CAP), Loan to Deposit ratio (LDR),
Bank Liquidation (LIQ), Management Effectiveness (CTI), Listed Bank (List) and
Economic Growth (Ecogrow).
𝛼 ,γ,δ: Are the estimated coefficient vectors.
ε𝑖𝑡:: Is the standard error.
3.6. Methods of data analysis and processing
3.6.1. Methods of estimation
3.6.3. Testing the errors of the model
3.6.4. Handling endogenous phenomena of the model
However, the weakness of the above regression models is unable to handle the potential
endogenous phenomena in the model. To solve this problem, previous studies have used
instrumental variables (IV) estimation. However, the problem arising when using the
instrumental variable estimation is that it is often difficult to find the appropriate
instrumental variables because if you choose weak instrumental variables, the IV
estimation may be skewed (Mileva, 2007). In other words, using the IV estimation without
choosing the appropriate instrumental variables, the problems of OLS estimation will not
be improved. Since then, the GMM dynamic panel data model is proposed to be used
according to the research of Arellano and Bond (1991).
One of the advantages of the GMM model over the instrumental variable estimation model
is that it is easier for the GMM model to select instrumental variables because using
exogenous variables at other time or taking the latency of the variables can be used as
instrumental variables for endogenous variables at the present time. Therefore, GMM has
introduced many instrumental variables to easily achieve the condition of a standard
instrumental variable (Overidentification of Estimators). Moreover, the estimation of
Arellano and Bond are consistent with short panel data with small T time series (7 years)
and large N (29 banks). Therefore, the GMM method introduced by Arellano and Bond
(1991) will be used in this study.
Specifically, the data of the project is as follows: The first, after checking the data, it shows
that the change of variable phenomenon has occurred for the research model. To eliminate
this phenomenon, the regression model is run with robust command in Stata software if it
detects that this phenomenon occurs in the model. The second, the author examines the
multi-collinearity and finds that this was not a problem for the analysis of the topic through
the results of the correlation coefficients between the variables and presented in the data
content description. The last, the relationship between Corporate Governance and bank risk
21
can occur as endogenous phenomena because of the possible causal relationship between
Corporate Governance and bank risk (Lehn et al. 2009; Wintoki et al, 2012). Therefore, it
commonly selects FEM regression models to reduce endogenous problems in case the
research does not find appropriate instrumental variables to handle (Cheung et al., 2010).
Studies of Pathan (2009), Dong et al (2014), Chan et al (2016), Dong et al (2017, Moilah et
al (2017) use the 2-step GMM method to measure the impacts of Corporate Governance on
bank risk. Therefore, in this study, the author uses the 2-step GMM analysis method to
handle potential endogenous problems in the model of measuring the impacts of Corporate
Governance on bank risk.
Studies of Mollah et al (2017), Kusi et al (2018) use the GLS method to measure the
impacts of Corporate Governance on financial performance of banks. Therefore, in the
model to measure the impacts of Corporate Governance on financial performance of banks,
the author uses the GLS analysis method for analysis.
Conclusion of chapter 3
CHAPTER 4: STUDY RESULTS AND DISCUSSION
4.1. Actual situation of commercial banks in Vietnam in the period of 2011-2017
4.2. Descriptive statistics of study variables
Table 4.2. Table of descriptive statistics of study variables
Variable Observation number
Average
value
Standard
deviation
Minimum
value
Maximum
value
Z-Score 203 29.9281 0.7711 0.5081 126.7510
NPL 203 0.0236 0.0142 0.0034 0.088
ROA 203 0.0063 0.0066 -0.0551 0.0253
ROE 203 0.0692 0.0847 -0.8200 0.2682
NIM 203 0.0256 0.0120 -0.0064 0.0742
Bsize 203 6.9891 0.2473 5 15
Bindep 203 0.1439 0.0710 0 0.4
Femdir 203 0.1794 0.1619 0 0.625
Fordir 203 0.0930 0.1251 0 0.4286
Execdir 203 0.1528 0.1271 0 0.4444
Edu 203 0.5391 0.2559 0 1
SIZE 203 89.349 1.0938 13.224 1.202.283
LAR 203 0.5284 0.1271 0.1473 0.7313
22
Variable Observation number
Average
value
Standard
deviation
Minimum
value
Maximum
value
CAP 203 0.0970 0.04192 0.035 0.2384
LDR 203 0.8391 0.2015 0.3719 1.805
LIQ 203 0.1878 0.0959 0.0452 0.611
CTI 203 0.9880 6.0192 0.2875 86.3019
List 203 0.3448 0.4764 0 1
GDP 203 0.0608 0.0054 0.0525 0.0681
4.3. Analyzing the correlation between variables
4.4. Measuring the impacts of corporate governance on the risks of commercial banks
in Vietnam in the period of 2011 - 2017
Table 4.4. Regression analysis results by the 2-step SGMM method
Variable Z-Score NPL
Z-Scoret-1
0.8924 ***
(0.000)
NPLt-1
0.3082 ***
(0.000)
Bsize -0.0613 (0.264)
-0.0014
(0.601)
Bindep -0.4723 ** (0.035)
-0.0197
(0.158)
Femdir 0.3121 *** (0.000)
-0.0043 **
(0.028)
Fordir 0.2316 ** (0.015)
0.0002
(0.954)
Execdir -0.2592 ** (0.031)
-0.0017
(0.646)
Edu 0.0275 (0.746)
-0.0018
(0.302)
SIZE 0.1313 *** (0.000)
-0.0004
(0.693)
LAR 0.3475** (0.034)
-0.0080
(0.360)
CAP 5.9788 *** (0.000)
0.0764***
(0.007)
LDR -0.3608 ** (0.000)
0.0066
(0.142)
LIQ 0.0982 (0.625)
-0.0145
(0.106)
CTI -0.0144 0.0102**
23
Variable Z-Score NPL
(0.856) (0.021)
List 0.0334 (0.271)
0.0034 **
(0.011)
GDP 6.2399 *** (0.001)
-0.5134 ***
(0.000)
Constant -2.8070 (0.000)
0.0501
(0.056)
AR(1) 0.050 0.003
AR(2) 0.661 0.223
Hansen test 0.203 0.245
F-test 0.000 0.000
Note: *, ** and *** are statistical significance at 10%, 5% and 1% respectively
The suitability of regression by the SGMM method was assessed through F test, Hansen
test and Arellano-Bond test (AR). The F test examines the statistical significance of the
estimated coefficients. Hansen test examines excessive constraints, the rationality of
representative variables. The AR test determines whether there is a residual correlation of
the model.
In both models, the Hansen test with p-value of 0.203 and 0.245 respectively is greater
than 0.1, so we accept the Hypothesis H0: the model is correctly defined, the representative
variables are reasonable. The F test in both models has p-value of 0.000 which is less than
0.01, so we reject the Hypothesis H0: all the estimated coefficients in the equation are
equal to 0, or estimated coefficients of explanatory variables with statistical significance.
So both models are appropriate.
The AR test (1) of both models with p-value of 0.050 and 0.003 respectively, is less than
0.1, so we reject the Hypot

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