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 American Finance & Banking Review; Vol. 2, No. 1; 2018 

                  ISSN 2576-1226  E-ISSN 2576-1234 

                       Published by Centre for Research on Islamic Banking & Finance and Business 

 

44 
 

  

Effect of Liquidity Risk, Premium Growth on the Performance of 

Quoted Insurance Firms in Nigeria: A Panel Data Analysis 
 

Lasisi Isiaka Olalekan1 

 

1Department of Accounting and Management, Faculty of Arts and Social Sciences, Nigerian Defence 

Academy,Kaduna, Nigeria. 

Correspondence: Department of Accounting and Management, Faculty of Arts and Social Sciences, Nigerian 

Defence Academy, Kaduna, Nigeria. E-mail:lasmanyk30@gmail.com,Tel:+2348037322585 

 

Received: January 20, 2018      Accepted: January 25, 2018      Online Published: January 27, 2018   

 

Abstract 

This study assesses the effect of liquidity risk on firm performance of listed insurance companies in Nigeria for the 
period of 2011-2015. The listed insurance firms are twenty Five (25) in numbers out of which a sample of twelve 

(12) were used for the study. Liquidity risk as the independent variable was proxy with leverage, claim loss ratio 

and premium growth, while the return on asset was used to proxy firm performance. The study adopts a panel 

multiple regression techniques and data were collected from secondary source through the annual reports of the 

firms after controlling for fixed/random effects.The findings of random effect reveal that leverage has significant 

negative effect on return on assets. The claim loss ratio has insignificant negative influence on return on assets 

while premium growth has positive and insignificant effect on firm performance of listed insurance companies in 

Nigeria. It is recommended among others that the managers, shareholders and other stake holders to checkmate 

and control liquidity risk as it have been found empirically to enhance the quality of the firm’s financial 

performance. 

 

Keywords: Liquidity Risk, Claim Loss Ratio, Premium Growth, Performance and Insurance Companies. 

 

1. Introduction  

Insurance industry plays a major role in the society as the operation of the industry can set energy for other 

industries and development of an economy (Abate, 2012). The insurance market plays an important role in the 

financial services industry in almost all developed and developing countries, contributing to economic growth, 

allocating efficient resources, reducing transaction costs, creating liquidity, promoting investments and 

distribution of financial losses (Das, Davies, & Podpiera, 2003). Insurance companies shares the function of 

banks and other financial institutions beside to the role of risk minimizing by pooling similar risk exposures 

(Daare, 2016). The function of insurance companies and other financial institution is to establish effective and 

efficient pecuniary structure by risk transfer, intermediation and savings mobilization in economy.  

Consequently financial bodies canal resources and transport risks from one monetary element to another to assist 

resources pact and trade (Saeed & Khurram, 2015). One of the most severe liquidity stress scenarios faced by an 

insurer is a mass surrender of policies owing to a loss of confidence in its financial strength. This happened to 

Equitable Life following the House of Lords ruling on its guaranteed annuity liabilities in 2000. Risk is a natural 

element of business and community life (kamau, F & Njeru, 2016). It is a condition that raises the chance of 

losses/gains and the uncertain potential events which could manipulate the success of financial institutions 

(Crowe, 2009). 

Financial risk is the unexpected variability or volatility of returns (Holton, 2004). It includes credit risk, liquidity 

risk and market risks which contribute to the volatility of financial performance (Tafri et al., 2009 & 

Dimitropoulos et al., 2010). The global financial crisis that began in July 2007 has highlighted gaps in the field 



 

 

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of liquidity risk management(Otieno & Nyagol, 2016). Financial institution have demonstrated the lack of good 

forecasting models to manage liquidity risk, which has led to a liquidity spiral and given rise to a sudden 

deterioration of financial institution balance sheets with consequent difficulties in finding new sources of 

liquidity on financial markets(Cucinelli, 2013). The inability of insurance firms to raise liquidity can be 

attributed to a funding liquidity risk that is caused either by the maturity mismatch between inflows and outflows 

and/or the sudden and unexpected liquidity needs arising from contingency conditions (Duttweiler, 2009). 

Liquidity Risk is a risk of insufficient liquid assets to meet payouts from policies (surrender, expenses, maturities, 

etc.), forcing the sale of assets at lower prices, leading to losses, despite company being solvent(kamau, F & 

Njeru, 2016). Loss from meeting liquidity comes either from fire sale or by paying interest on borrowing to meet 

payouts. Liquidity risk arises due to two reasons, one on the liability side and other on the asset side (Sonjai, 

2008). Financial performance on the other hand refers to the act of performing financial activity; it is used to 

measure firm's overall financial health over a given period of time. Financial performance is a desirable 

objective for all profit-oriented firms. The absence of it can indeed spells failure. Typical measures of financial 

performance are profitability,( Yahaya & Lamidi, 2015). 

In Nigeria, previous studies such as (Ahmed et al (2011), Daniel and Tilahun (2012), Sumaira and Amjad (2013) 

among others concentrated on the effect of liquidity on profitability of commercial banks and non financial 

institution. From the review, very few study focus on insurance sectors. This study therefore differs by 

concentrating on the effect liquidity risk, premium growth on the performance of insurance firms in Nigeria. In 

order to achieve this, we hypothesized that; Ho1: liquidity risk, premium growth has no significant effect on the 

Performance of insurance firms in Nigeria. 

2. Literature Review 

2.1 Liquidity Risk and Firms Performance  

Performance measures serve as a basis for evaluating the financial performance of a corporate entity. 

Performance of an organization can be measured through return on assets (ROA), return on equity (ROE), 

earnings per share (EPS) among others. Furthermore, many researchers; Pathirawasam and Adriana, (2013); Ali, 

Mohammed & Amer, (2015); Yahaya & Lamidi, 2015; have used ROA to measure the financial performance of 

companies. For this, ROA has been will be used this study as the dependent variable for analysis. This variable is 

most sustainable because it is measure of efficiency, by revealing how effectively and efficiently a firm utilizes 

the resources (assets) at its disposal, in revenue generation. The liquidity risk factor that affect the firm 

performance of insurance companies include, liquidity ratio, premium growth rate, leverage, underwriting risk, 

claim ratio loss among others. This study therefore focuses on leverage risk, claim ratio loss, and premium 

growth rate because they are liquidity risk attributes that relates to the performance of insurance companies  

Leverage Risk 

Insurance companies could prosper by taking reasonable leverage risk or could become insolvent if the risk is 

out of control. Nevertheless more empirical evidence supports the view that leverage risk reduces the 

performance of the companies. It is a financial ratio that indicates the percentage of firm’s asset that is financed 

with debt. Leverage is measured as total liabilities to total assets (Nikhik, Kingshuk, and Mihir., 2015; Mehari 

and Aemiro, 2013). In this study the ratio of total liabilities to total asset is taken as an independent variable. 

Claims Ratio 

The claim ratio is something very specific for the insurance business. It is claims payable as a percentage of 

premium income. This is also known as claims loss ratio. (Pietersz,  2016.). The claims ratio measures the 

number of claims in a period and divides that by the earned premium for the same period. Insurance is the 

business of managing liquidity risks and it is essential to have a thorough understanding of the incurred claims 



 

 

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ratio. If the value is higher than expected or established norms, then further investigation is required to figure out 

why that is. It is important to investigate if there is a threat of an insurance fraud. If the ratio is lower than 

expected, it could indicate irrelevant products or difficulties in claiming, possibly affecting customer satisfaction, 

and obviously requires more investigation. (Klipfolio 2016.).  

Premium Growth Rate 

Premium growth is another important financial variable that influences the financial performance of insurance 

companies. Therefore the growth in premium of the firm has been argued to have influenced on the financial 

performance of insurance companies and this has been studied frequently. Premium growth as measured by 

percentage change in total assets or sometimes as percentage change in premium of insurance companies (Abate, 

2012). Premium growth rate measures the rate of market penetration (Ahmed et al, 2011). 

2. 2 Liquidity Risk and Financial performance: An Empirical Review 

This section focuses on related studies of which emphasis is placed on the attributes used in this study as they 

relate to performance of insurance companies. For instance kamau, & Njeru, (2016) examine the effect of 

liquidity risk on financial performance of six Insurance Companies Listed at the Nairobi Securities Exchange for 

the period 2012-2015. The risks studied included operational risk, market risk and credit risk. The study was 

descriptive in nature. It was found out that operational, market and credit risks has negative effect on the 

financial performance. The study recommended that measures should be put into place to hedge these risks and 

hence maintain a healthy financial performance. 

Muriithi & Waweru, (2017) conduct a study on liquidity risk and financial performance of 43 registered 

commercial banks in Kenya over a period of (2005 – 2014). Liquidity risk was measured by liquidity coverage 

ratio (LCR) and net stable funding ratio (NSFR) while financial performance by return on equity (ROE). Panel 

data techniques of random effects estimation was used for the study. Findings indicate that NSFR is negatively 

associated with bank profitability both in long run and short run while LCR does not significantly influence the 

financial performance both in long run and short run. It is recommended that bank’s management to pay the 

required attention to the liquidity management. Similarly, Iqbal ,Chaudry, qbal & Zia ud Din  (2015) examine 

the impact of liquidity risk on firm specific factors; A case of four (4) Islamic bank of Pakistan  for the period 

of  2000-2013. Their findings reveal that profitability is negatively co integrated with liquidity risk, exist strong 

positive significance of return on assets on liquidity risk, leverage is found to be positively correlated with 

liquidity risk and bank size has positive impact on liquidity risk. The findings of this study might not be widely 

accepted since the study is centered on Islamic banks.  

Otieno & Nyagol, (2016) examine the relationship between Liquidity risk management and financial 

performance of microfinance banks in Kenya. Longitudinal research design utilizing panel data covering the 

period from 2011 to 2015 was used. The findings were that Liquidity risk management with FGR and CAR 

parameters had a strong Positive correlation (r=0.45), giving a significant negative relationship with both ROAA 

and ROAE performance measures as depicted by regression coefficient of 0.3 estimated by GMM. Also, Mehari 

& Aemiro, (2013) conduct a study on firm specific factors that determine insurance companies’ performance in 

Ethiopia. Return on total assets (ROA) - a key indicator of insurance company's performance- is used as 

dependent variable while age of company, size of the company, growth in writing premium, liquidity, leverage 

and loss ratio are independent variables. The sample includes 9 insurance companies over the period 2005- 2010. 

The results of regression analysis reveal loss ratio (risk) is statistically significant and negatively related with 

ROA of insurance companies in Ethiopia. But, growth in writing premium, insurers’ age and liquidity have 

statistically insignificant relationship with ROA. 

Saeed & Khurram, (2015) examine the factors influencing the financial performance of 24 non-life insurance 



 

 

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companies of Pakistan over the period 2005 - 2013. Fixed effect model of Hausman test was employed for the 

study. The findings indicate that loss ratio proves significant in determining performance. Also, Nikhik, 

Kingshuk, Mihir (2015) conducted a study on firm specific factors affecting the overall financial performance of 

life insurance companies in India over the period of ten (10) years from 2003-04 to 2012-13. The analysis shows 

that there is significant negative relationship between leverage and financial performance (ROE). Also, Kambi & 

Ali, (2016) study the effects of financial risk management practices on the financial performance of 44 listed 

banks at the Nairobi Securities Exchange. Secondary data was used and census approach was used since the 

target population is small. The study recommends that there is need for the management to maintain the liquidity 

level at safe level and training could be organized for staff so that they learn more about the concept of financial 

risk management on financial performance.  

Cucinelli, (2013) conduct a study on the relationship between liquidity risk and probability of default: Evidence 

from the Euro Area. The sample is composed of 575 listed and non-listed Eurozone banks and the methodology 

applied in the analysis is OLS regression based on panel data. The results show a relationship only between the 

liquidity coverage ratio and credit rating, while there is no relationship between the long- term liquidity measure 

and probability of default. Similarly, Hossein, Dezfouli, Hasanzadeh, & Shahchera, (2014)  inspect the 

effectiveness of liquidity risk on banks profitability in Iran. Using a four-step econometric model and GMM 

linear forecasting model, it was concluded that there is a significant relation between mentioned factors (NPL 

(dependent variables- Non-Performing loans ratios, liquidity ratios, liquidity gap ratio, capital ratio, and bank 

size) and the profitability ones (independent variables- ROE and ROA). Furthermore, Ail, Tabari, Ahmadi, & 

Emami, (2013) examine the effect of liquidity risk on the performance of commercial banks using of panel data 

related to commercial banks of Iran during the years 2003 to 2010. In the estimated research model, two groups 

of bank-specific variables and macroeconomic variables are used. The results of research show that the variables 

of bank's size, bank's asset, gross domestic product and inflation will cause to improve the performance of banks 

while credit risk and liquidity risk will cause to weaken the performance of bank.  

2.3 Theoretical Framework of the Study 

There are two theory that accord these study; risk return theory and extreme value theory. 

Markowitz (1952) is the pioneer scholar that propounded the risk return theory .The theory that underpins this 

study is Risk-Return Theory; this is because insurance companies is both a risk-taking and profit making 

business, and insurance firms activities should return profits commensurate with their risk. The higher the risk, 

the higher will be the financial performance and vice versa. This postulation is true when the insurance firm risk 

appetite is lower than the risk tolerance. 

Extreme value theory which was pioneered by Leonard Tippet in the 1950’s. is a practical and useful tool for 

modeling and quantifying risk. It is the theory of modeling and measuring events which occur with very small 

probability. This implies its usefulness in risk modeling as risky events per definition happen with low 

probability. This theory shows that the probability on very large losses is eventually governed by a simple 

function, regardless the specific distribution that underlies the return process. . 

3. Research Methodology  

This study adopts the Correlation research design. The data were obtained from secondary sources through the 

firm’s audited annual reports. The population of the study consists of the twenty five (25) listed insurance 

companies in Nigeria as at 31st December 2016 (NSE, 2016). The sample size is twelve (12) over the period 

2011 to 2015. This study relied on the use of judgemental sampling techniques which is based on the availability 

and accessibility of data. Multiple regression techniques were used for the analysis through the use of STATA 

13.0. The model encapsulates the contribution of Leverage, claim loss ratio and premium growth on the firm 



 

 

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performance given as; ROAit= β0it + β1LVit + β2CLit + β3PGit + εit 

Where, Return on Asset (ROA) are measure as firm performance, β0 = constant, β1… β3 = the slope which 

represents the degree in which financial performance changes as the independent variable change by one unit 

variable., LV = Leverage, CL = claim loss ratio, PG= premium growth, ε = error term, t = measure of time, i = 

number of insurance firm observation. Where:  

Return on Asset (ROA) = Profit before tax/Total Asset 

Leverage (LV)   = Total Liabilities/Total Asset 

Loss Ratio (CLR)  = Net claims incurred/ Net earned premiums 

Premium Growth (PG) =  Current Premium–Previous Premium/Previous Premium  

4. Result and Discussions 

This section presents the result of data analysis and tests of hypotheses formulated earlier in the paper. First, 

descriptive statistics, followed by the correlation matrix table and then the summary of Regression Result are 

presented and analyzed. 

Table 1: Descriptive Statistics 

Variables No of OBS Minimum Maximum Mean Standard Dev. 

ROA 60 - 0.2142 0.2228 0.03353 0.0643 

Leverage 60   0.1351 1.2628 0.56816 0.2254 

Claim Loss Ratio 60   0.1918 1.5808 0.3942 0.1882 

Premium Growth 60 -0.5025 1.4187 0.1467 0.3012 

Sources: Output of Stata13 Result 

Table 1 above presents the descriptive statistics for all the variables of the study. From the description, it is 

observed that for the sampled firms and for the period covered by the study, the average value for financial 

performance (ROA) is 0.3353 with standard deviation of 0.6143 which is very far to the mean. The mean value 

leverage (LV) of 0.568 or 57% is an indication that debt financing in the insurance sector is to the tune of 57% of 

the total finance sources. The remaining 43% are sourced from asset financing. The claim loss ratio has 

minimum and maximum value of 0.19 and 1.58 respectively with the mean value of 0.3935 and standard 

deviation of 0.1882. Therefore, there exists a moderate variation among the value of loss ratio across the sample 

insurance companies included in this study. Premium growth has an average mean value of 0.1467 and standard 

deviation of 0.3012 with a minimum and maximum are -0.5025 and 1, 4186.  

Table 2: Correlation Matrix Table 

Variables ROA LV CLR PG 

ROA 1.0000    

LV -0.4197* 

 0.0008 

1.000   

CLR -0.1191 

 0.3649 

0.1706 

0.1925 

1.0000  

PG  0.2142 

 0.1004 

-0.1171 

 0.3730 

-0.3138* 

 0.0146 

1.0000 

Sources: Output of Stata13 Result 

 



 

 

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Table 2 above captures the correlation values between the independent variables and dependent variable as well 

as among independent variables themselves. From the table it is revealed that leverage is significant negative 

related with ROA, also there exist insignicant and negative association between Claim loss and return on asset. 

This implies that the higher the Claim loss the lower the return on asset and vice versa.  Furthermore, ROA is 

however positively correlated with premium growth and insignificant, this implies that the higher the premium 

growth the higher the return on asset. The correlation matrix also revealed that no two explanatory variables 

were perfectly correlated. This means that there is the absence of multicolinearity problem in the model. 

4.1 Regression Diagnostics Tests  

The value of F- statistics of 9.73 (p-0.0210) is significant at 5% level of significance. This indicates that the 

model is fit and the explanatory variable are properly selected, combined and used. The results of the VIF further 

prove the absence of perfect multicollinearity among the independent variables, because the mean Variance 

Inflation Factor (VIF) is 1.09. The rule of thumb is that a value of VIF of 10 and above is a suggestion of 

multicolinearity among the explanatory variables (Gujarati, 2004). Breusch-Pagan / Cook-Weisberg test for 

heteroskedasticity revealed homoskedastic results (Prob > chi2 = 0.0602). Besides Ramsey RESET (regression 

specification error test) test was performed for model specification and the results show the model has no 

omitted variable (Prob > chi2 = 0.1434).  

4.2 The Regression Result 

Table 3 below is the regression result of the random effect model. The model was selected for interpretation 

because the hausman specification test favors the random effect model with, probability of 0.3062 which is not 

significant at 5%. The cumulative R2 of 0.1970 is the multiple coefficient of determination which shows the 

percentage of the total variation in the dependent variable explained by the independent variable together. 

Therefore, it indicates that 20% of total change in operating performance of quoted insurance companies in 

Nigerian is explained by their level of leverage, claim loss ratio and premium growth. 

Table 3: Summary of Regression Result – Random Effects Model 

Variables Coefficient t-value P-value VIF 1/VIF 

Constant  0.967694  3.34 0.001   

Leverage -0.0997178 -2.43 0.015 1.03 0.966417 

Claim Loss ratio -0.0253039  -0.59 0.553 1.13 0.883331 

Premium growth  0.0231079   0.95 0.344 1.11 0.897344 

Mean VIF     1.09 

Hausman Ch2=3.61  pro>chi2=0.3062   

R2 (overall)   0.1970   

F-Statistics FStat=9.73   P>F=0.0210   

Ramsey test F=1.88  P>F=0.021   

Source: STATA Output Result** Significant at 5% 

Test of Hypothesis and Policy Implication 

There is no significant effect for leverage on return on asset. It was found that the beta coefficient (β) of 

-0.0997178 and ρ=0.015 at 5% level of significant. This implies that an increase in the liabilities by 1% will lead 

to decrease in the performance of the study insurance firm by 9%. The result suggested that the insurance firms 

should properly manage their liabilities in order to avoid future insolvency or liquidation. Thus, it can be 



 

 

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concluded that insurers with high leverage (using leverage beyond a level) will have adverse impact on the 

profitability. This finding is against the study results of (Charumati, 2012). The study therefore provides 

evidence to reject the null hypothesis. Hence, there is a significant negative relationship between insurance 

leverage and return on assets. 

Claim loss ratio has no significant effect on the return on assets. The claim loss ratio has a beta coefficient of - 

0.0253039 with a p-value of 0.553 which is insignificant at 5% level. This implies that for every 1% increase of 

claim loss ratio, return on assets of insurance companies in Nigeria will decrease by 3%. The study is supported 

by (Saeed & Khurram, 2015). This provides evidence for us to accept the null hypothesis and conclude that 

claim loss ratio has negative and insignificant effect on return on assets of listed insurance firms in Nigeria.  

Finally, premium growth has no significant effect on the return on assets. The computed value of beta coefficient 

is 0.0231079 with p-value of 0.344; therefore, premium growth has a positive relationship with return on assets 

and is found to be statistically insignificant. This implies that an increase in premium growth by 1% will result to 

2% increase in return on asset. This indicate that the higher premium growth over the study period, the better the 

returns on asset and this increase in the insurance firms profitability is at a marginal level. This result is in line 

with the findings of ((Mehari & Aemiro, 2013). This provides evidence for us to accept the null hypothesis and 

concluded that the premium growth has no significant effect on return on assets. 

5. Conclusion and Recommendation 

In this study, the empirical analysis of examining the effect of liquidity risk on the performance of insurance 

firms in Nigeria was conducted using a panel data set consisting of financial data of twelve insurance companies 

over the period of 2011 to 2015. The results of panel multiple regression analysis revealed that leverage are 

negative and statistically significant to explain performance of insurance companies in Nigeria. Claim loss and 

premium growth have no a statistical significant relationship with performance of insurance firms in Nigeria. 

The result of the study also shows that insurance firms claim loss was negatively related to performance (ROA) 

while premium growth were positively related to insurance performance. 

The researcher therefore recommends that there is need to invest on measures to curb liquidity risk in these 

companies in order to have a sound financial performance and manage properly the leverage of the insurance 

companies. These risks can be avoided by ensuring correct and effective measures are in place. Potential 

investors, shareholders and managers should monitor the leverage, claim loss levels and the growth of firm’s 

premium they intend to invest in, so as to check whether they are questionable or favorable. This will help these 

investors in making wise investment decisions. This will benefit investors to take advantage on the investment 

opportunities available when these variables vary. While this study provides some insights of the study variables, 

the implications of the new liquidity frameworks proposed by the study warrants further research. 

References 

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Appendix  

Sample Nigeria Insurance Firms 

1. Consolidated Hallmark Insurance Plc  

2. AIICO 

3. Continental Reinsurance Plc 

4. Cornerstone insurance plc 

5. Great Nigeria insurance plc 

6. Guinea insurance plc 

7. International energy insurance plc 

8. Niger insurance plc 

9. Sovereign trust insurance plc 



 

 

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10. Equity assurance plc 

11. Mutual benefits assurance plc 

12. Lasaco assurance plc 

                  Prob > F =      0.1434

                  F(3, 53) =      1.88

       Ho:  model has no omitted variables

Ramsey RESET test using powers of the fitted values of roa

. ovtest

         Prob > chi2  =   0.0602

         chi2(1)      =     3.53

         Variables: fitted values of roa

         Ho: Constant variance

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 

. estat hettest

    Mean VIF        1.09

                                    

          lv        1.03    0.966417

          pg        1.11    0.897344

         clr        1.13    0.883331

                                    

    Variable         VIF       1/VIF  

. estat vif

                                                                              

       _cons     .0902449   .0254104     3.55   0.001     .0393417     .141148

          pg     .0342411   .0256712     1.33   0.188    -.0171845    .0856668

         clr     .0006341    .041422     0.02   0.988    -.0823442    .0836124

          lv    -.1091059    .033051    -3.30   0.002    -.1753149   -.0428968

                                                                              

         roa        Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

                                                                              

       Total    .222649409    59  .003773719           Root MSE      =  .05626

                                                       Adj R-squared =  0.1611

    Residual      .1772759    56  .003165641           R-squared     =  0.2038

       Model    .045373509     3  .015124503           Prob > F      =  0.0049

                                                       F(  3,    56) =    4.78

      Source         SS       df       MS              Number of obs =      60

. regress roa lv clr pg

              

                 0.1004   0.3730   0.0146

          pg     0.2142  -0.1171  -0.3138*  1.0000 

              

                 0.3649   0.1925

         clr    -0.1191   0.1706   1.0000 

              

                 0.0008

          lv    -0.4197*  1.0000 

              

              

         roa     1.0000 

                                                  

                    roa       lv      clr       pg

. pwcorr roa lv clr pg, sig star(5)

          pg          60    .1467467    .3012165     -.5025     1.4187

         clr          60       .3942    .1881533      .1918     1.5808

          lv          60      .56816    .2254438      .1351     1.2628

         roa          60      .03353    .0614306     -.2142      .2228

                                                                      

    Variable         Obs        Mean    Std. Dev.       Min        Max

. summarize roa lv clr pg

. *(6 variables, 60 observations pasted into data editor)

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      1.  (/v# option or -set maxvar-) 5000 maximum variables

Notes:

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www.cribfb.com/journal/index.php/amfbr        American Finance & Banking Review   Vol. 2, No. 1; 2018 

53 
 

. 

                Prob>chi2 =      0.3062

                          =        3.61

                  chi2(3) = (b-B)'[(V_b-V_B)^(-1)](b-B)

    Test:  Ho:  difference in coefficients not systematic

            B = inconsistent under Ha, efficient under Ho; obtained from xtreg

                           b = consistent under Ho and Ha; obtained from xtreg

                                                                              

          pg       .014728     .0231079         -.00838        .0083378

         clr     -.0650551    -.0253039       -.0397512        .0262091

          lv     -.0549078    -.0997178          .04481        .0602361

                                                                              

                   fixed        random       Difference          S.E.

                    (b)          (B)            (b-B)     sqrt(diag(V_b-V_B))

                      Coefficients     

. hausman fixed random

. estimates store random

                                                                              

         rho    .21166216   (fraction of variance due to u_i)

     sigma_e    .05031314

     sigma_u    .02607035

                                                                              

       _cons     .0967694    .029001     3.34   0.001     .0399286    .1536103

          pg     .0231079   .0244115     0.95   0.344    -.0247378    .0709537

         clr    -.0253039   .0426725    -0.59   0.553    -.1089404    .0583327

          lv    -.0997178   .0410809    -2.43   0.015    -.1802349   -.0192006

                                                                              

         roa        Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]

                                                                              

corr(u_i, X)   = 0 (assumed)                    Prob > chi2        =    0.0210

                                                Wald chi2(3)       =      9.73

       overall = 0.1970                                        max =         5

       between = 0.3500                                        avg =       5.0

R-sq:  within  = 0.0859                         Obs per group: min =         5

Group variable: firm                            Number of groups   =        12

Random-effects GLS regression                   Number of obs      =        60

. xtreg roa lv clr pg, re

                delta:  1 unit

        time variable:  year, 2011 to 2015

       panel variable:  firm (strongly balanced)

. xtset firm year

. estimates store fixed

F test that all u_i=0:     F(11, 45) =     2.28              Prob > F = 0.0262

                                                                              

         rho    .36049328   (fraction of variance due to u_i)

     sigma_e    .05031314

     sigma_u    .03777526

                                                                              

       _cons     .0882098   .0413802     2.13   0.039     .0048658    .1715539

          pg      .014728   .0257962     0.57   0.571    -.0372282    .0666841

         clr    -.0650551   .0500785    -1.30   0.201    -.1659185    .0358083

          lv    -.0549078   .0729111    -0.75   0.455    -.2017583    .0919427

                                                                              

         roa        Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]

                                                                              

corr(u_i, Xb)  = 0.0610                         Prob > F           =    0.1828

                                                F(3,45)            =      1.69

       overall = 0.1372                                        max =         5

       between = 0.1880                                        avg =       5.0

R-sq:  within  = 0.1012                         Obs per group: min =         5

Group variable: firm                            Number of groups   =        12

Fixed-effects (within) regression               Number of obs      =        60

. xtreg roa lv clr pg, fe

                delta:  1 unit

        time variable:  year, 2011 to 2015

       panel variable:  firm (strongly balanced)

. xtset firm year

 

 

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