INDIAN JOURNAL OF FINANCE AND BANKING 10(1) (2022), 54-60 54 FINANCE AND BANKING IJFB VOL 10 NO 1 (2022) P-ISSN 2574-6081 E-ISSN 2574-609X Available online at https://www.cribfb.com Journal homepage: https://www.cribfb.com/journal/index.php/ijfb Published by CRIBFB, USA DETERMINANTS OF DIVIDEND POLICY: AN EMPIRICAL INVESTIGATION OF INDIAN COMPANIES USING PANEL DATA ESTIMATION TECHNIQUE Razique Anwar (a) Sandeep Kumar (b)1 (a) Research Scholar at Indian Institute of Management, Kashipur - 244713, India; E-mail: razique.phd1902@iimkashipur.ac.in (b) Assistant Professor at Jamshedpur Workers’ College, Jamshedpur – 831001, India; E-mail: sandijaiswal@gmail.com A R T I C L E I N F O Article History: Received: 19th May 2022 Accepted: 29th June 2022 Online Publication: 31st July 2022 Keywords: Dividend Yield, Dividend Policy Indian Companies, Leverage Panel Data JEL Classification Codes: G1, G3, M2 A B S T R A C T The decision to pay dividends by companies is among the most researched and contentious topics in corporate finance as, according to some scholars, it has a bearing on companies' valuation. The purpose of this research is to find out important factors that act as determinants of the dividend policy of Indian companies. The study employs secondary time series data gathered from the latest data available from the CMIE (Centre for Monitoring India) Prowess database. The study has been divided into three time periods i.e. 1) 2000-01 to 2009-10, 2) 2010-11 to 2020-21, 3) 2000-01 to 2020-21, by merging the two time periods and the panel data regression technique has been applied. The results of the analysis suggest that Return on assets, Debt to equity ratio, Cash and cash equivalents & Debt to total capital ratio significantly affect dividend yield from 2000-01 to 2009-10 and from 2000-01 to 2020-21, whereas the results of the analysis from period 2010-11 to 2020-21 found Sales/Size, Return on the asset, and Debt to total equity ratio as determinants of dividend policy. These results corroborate the earlier findings that profitability, liquidity, and leverage are important factors in the decision-making of companies regarding the payment of dividends. © 2022 by the authors. Licensee CRIBFB, USA. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). INTRODUCTION The decision to pay dividends or not is one of the most contentious decisions for companies. “The harder we look at the dividend picture the more it seems like a puzzle, with pieces that just do not fit together” (Black, 1998). The puzzle is among the top ten unsolved problems of finance. So it is very hard to dissect the dividend policy of the companies. The decision of firms to declare dividends or to retain earnings for future growth has always been dynamic. A vast amount of literature available on this study suggests theoretical and empirical support for variables such as corporate size, age, ownership structure, market risk, cash holdings, leverage, growth opportunities, net working capital, and profitability of the firms as important factors as determinants of dividend policy for companies. In light of the brief above argument presented, the present study intends to find out the factors that act as determinants of dividend policy for 356 Indian companies under study using the panel data regression analysis technique. The analysis of this study has been divided into three time periods i.e 1) From the financial year 2000-01 to 2009-10, 2) From the financial year 2010-11 to 2020-21, 3) From the financial year 2000-01 to 2020-21 by merging the above two time periods to find out the significant factors that act as determinants of dividend payment by companies. In this study banking and financial services providing companies have been excluded as we assume that they differ in business practices and have different financial parameters than industries in other sectors. Trends in Dividend Yield Figure 1 below shows the average dividend yield of all 356 companies understudy from the financial year 2000-01 to 2009- 10. It shows that the average dividend yield showed an increasing trend though it started falling from 2005-06 till 2008-09 1Corresponding author: ORCID ID: 0000-0002-8292-0972 © 2022 by the authors. Hosting by CRIBFB. Peer review under responsibility of CRIBFB, USA. https://doi.org/10.46281/ijfb.v10i1.1777 To cite this article: Anwar, R., & Kumar, S. (2022). DETERMINANTS OF DIVIDEND POLICY: AN EMPIRICAL INVESTIGATION OF INDIAN COMPANIES USING PANEL DATA ESTIMATION TECHNIQUE. Indian Journal of Finance and Banking, 10(1), 54-60. https://doi.org/10.46281/ijfb.v10i1.1777 https://orcid.org/0000-0002-8292-0972 http://creativecommons.org/licenses/by/4.0/) http://creativecommons.org/licenses/by/4.0/) https://orcid.org/0000-0002-8292-0972 https://doi.org/10.46281/ijfb.v10i1.1777 https://orcid.org/0000-0002-1573-0415 Anwar & Kumar., Indian Journal of Finance and Banking 10(1) (2022), 54-60 55 which could be attributed to the global financial crisis and India too was not fully immune to it and thus companies refrained from paying dividends. But again it picked up its increasing trajectory from there onwards. Figure 1. Above shows the average dividend yield of companies from 2000-01 to 2009-10. Figure 2 below shows the average dividend yield of all the companies understudy from the financial years 2010- 11 to 2020-21. It also shows an increasing trend though it was on the decline from the financial year 2015-16 till 2017-18 which could be attributed to major structural changes within India’s economy like the implementation of Goods and Services Tax ( GST ) move toward the digital economy, tax amendments laws, etc. So the companies once again refrained from paying dividends as they had to adjust to these changes. Figure 2. Above shows the average dividend yield of companies from 2010-11 to 2020-21. Figure 3 below shows the average dividend yield of all 356 companies from the financial year 2000-01 to 2020- 2021 by merging the above two time periods. It shows a slight increasing trend in average dividend yield with a few ups and downs. Figure 3. Above the shows average dividend yield of companies from 2000-01 to 2020-21. 0 2 4 6 8 10 12 1999-00 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 D iv id en d Yi el d( % ) Data Source : CMIE Prowess IQ Average Dividend yield(%) 0 2 4 6 8 10 12 14 16 18 2011-12 2012-13 2014-15 2015-16 2016-17 2017-18 2018-19 2019-20 2020-21 2021-22 D iv id en d Yi el d( % ) Data Source : CMIE Prowess IQ Average Dividend yield(%) 0 2 4 6 8 10 12 14 16 18 199 9- 00 200 0- 01 200 1- 02 200 2- 03 200 3- 04 200 4- 05 200 5- 06 200 6- 07 200 7- 08 200 8- 09 200 9- 10 201 0- 11 201 1- 12 201 2- 13 201 4- 15 201 5- 16 201 6- 17 201 7- 18 201 8- 19 201 9- 20 202 0- 21 202 1- 22 D iv id en d Y ie ld (% ) Data Source : CMIE Prowess IQ Average Dividend yield(%) Anwar & Kumar., Indian Journal of Finance and Banking 10(1) (2022), 54-60 56 REVIEW OF LITERATURE According to some scholars, the decision to pay dividend has an important bearing on the valuation of the firms while others believe that dividend decisions are irrelevant and does not have any effect on variables affecting firms. A dividend decision is irrelevant in the absence of taxes, transaction costs, or other market imperfections, and the investment decisions of a firm are not affected by its dividend policy (Miller & Modigliani, 1961). Also, it has been found in many studies that dividend has no significant relation with size and industry classification, while it has an inverse relation with the growth of the companies. In the United States, Canada, United Kingdom, Germany, France, and Japan, the propensity to pay dividends is higher among larger and more profitable firms (Denis & Osobov, 2008). Also, profitable firms with more stable net earnings, ownership concentration, and market liquidity have a positive impact on dividend payout policy. The study done in Pakistan to find out determinants of dividend policy using the dynamic panel data analysis technique found stable net earnings, liquidity, and ownership concentration have positive effects whereas investment opportunity and leverage have negative effects on dividend payment decisions by companies (Yusof & Ismail, 2009). Also, the study done in Srilanka for 191 firms and 1337 firms year observation using binary logistic regression and fixed effect panel data technique found that past dividend decisions, earnings, investment opportunities, profitability, free cash flow (FCF), corporate governance, state ownership, firm size and industry influence as the key determinants of propensity to pay dividends. In addition to this past dividends, investment opportunities, profitability, and dividend premium are identified as the determinants of dividend payout. As per a study done for the U.A.E. (United Arab Emirates) firms, profitability and size are the most important considerations of dividend payout decisions. In another study done for 799 companies in 15 countries over 14 years, the key significant determinants of dividend payout decisions were found to be free cash flow, growth, liquidity, profitability, and size (Madra-Sawicka & Ulrichs, 2020). Also, a few other pieces of the literature suggest liquidity, size, and profitability have a positive relationship with dividend payment decisions by the companies. The study done in the Indian context using the panel data regression technique concludes that in the automobiles sector liquidity/cash flow, dividend distribution tax, investment opportunities, and retained earnings are the significant factors that influence the dividend payout ratio whereas in the pharmaceuticals sector profitability, debt to equity ratio, sales growth and retained earnings are the significant factor for deciding dividend payout ratio (Nathani & Gangil, 2019). Profitability, liquidity, leverage, risk, size of the firm, and inflation were also found to be the major determinants of dividend policy of selected NSE( National Stock Exchange) listed firms in India (Brahmaiah et al., 2018). Investment opportunity, financial leverage, size of the company, business risk, firm life cycle, profitability, tax, and liquidity were found to be the major determinants of the dividend policy for Indian companies (Labhane & Mahakud, 2016). The study done using pooled ordinary least square and fixed effect panel data technique found that size, profitability, and interest coverage ratios have a significant positive relation to dividend policy, and business risk and debt level have a significantly negative relationship with payment of dividends (Pinto & Rastogi, 2019). DATA & METHODOLOGY List of Variables The following is the list of variables with their definitions used in this study –  Dividend percent / Dividend Yield- It is a financial ratio that shows the percentage of dividends a company pays out relative to its share price in the financial year ( Dividend / Share price). It is used as a dependent variable.  Sales- It is defined as the activity of selling products or services. It is also taken as a proxy for the size of the company in this study.  Return on assets- It is obtained as “net profit after tax divided by its total assets” of a company which shows the profitability of a company.  Current ratio- It is the liquidity ratio and obtained as all “current assets divided by its current liabilities of a company”.  Quick ratio- Also known as acid test ratio, it short-term or term liquidity position of a company. It is calculated as “current assets minus inventory divided by current liabilities”.  Debt to equity ratio- It is a ratio used to evaluate a company's financial leverage and is calculated as the “company’s total liabilities divided by its shareholder’s equity”.  Net cash flow from operating activities to total asset ratio- It is obtained as “net cash flow generated from operating activities of a business divided by its total assets”.  Cash & Cash equivalents- Cash and cash equivalents are a line item on the balance sheet stating the amount of all cash or other assets that are readily convertible into cash.  Retained earnings to total equity ratio- It measures how much retained earnings the company is keeping within when compared to its total equity.  Debt to total capital ratio- It is calculated as “interest-bearing total term liabilities ( short and long term) divided by total capital ( total debt + shareholder’s equity)”. It shows the financial leverage of the company. In this study, analysis is done for 356 companies across three time periods – 1) From the financial year 2000-01 to 2009-2010, 2) From the financial year 2010-2011 to 2020-2021, 3) From the financial year 2000-01 to 2020-2021 by combining the above two time periods. Banking and financial services providing companies have been excluded from the study as we assume that they differ in business practices (lending and borrowing) and have different financial parameters than industries in other sectors. Anwar & Kumar., Indian Journal of Finance and Banking 10(1) (2022), 54-60 57 The panel data regression technique has been employed to find out the variables affecting dividend payment decisions by the firms. The results are reported based on the most robust model of panel data regression technique i.e model used is robust to standard errors disturbances being heteroscedastic or autocorrelated or both if the test indicates the presence of such disturbances. The equation used the for Panel data regression technique in the study is as follows- 𝐷𝑖𝑣 𝑦𝑖𝑒𝑙𝑑𝑖,𝑡 = 𝛼 + 𝛽1𝑠𝑎𝑙𝑒𝑠𝑖,𝑡 + 𝛽2𝑅𝑒𝑡𝑢𝑟𝑛 𝑜𝑛 𝑎𝑠𝑠𝑒𝑡𝑖,𝑡 + 𝛽3𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝑟𝑎𝑡𝑖𝑜𝑖,𝑡 + 𝛽4𝑄𝑢𝑖𝑐𝑘 𝑟𝑎𝑡𝑖𝑜𝑖,𝑡 + 𝛽5𝐷𝑒𝑏𝑡 𝑡𝑜 𝑒𝑞𝑢𝑖𝑡𝑦 𝑟𝑎𝑡𝑖𝑜𝑖,𝑡 + 𝛽6𝑁𝑒𝑡 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝑐𝑎𝑠ℎ 𝑓𝑙𝑜𝑤 𝑡𝑜 𝑡𝑜𝑡𝑎𝑙 𝑎𝑠𝑠𝑒𝑡 𝑟𝑎𝑡𝑖𝑜𝑖,𝑡 + 𝛽7𝐶𝑎𝑠ℎ & 𝑐𝑎𝑠ℎ 𝑒𝑞𝑢𝑖𝑣𝑎𝑙𝑒𝑛𝑡𝑠𝑖,𝑡 + 𝛽8 𝑅𝑒𝑡𝑎𝑖𝑛𝑒𝑑 𝑒𝑎𝑟𝑛𝑖𝑛𝑔𝑠 𝑡𝑜 𝑡𝑜𝑡𝑎𝑙 𝑒𝑞𝑢𝑖𝑡𝑦 𝑟𝑎𝑡𝑖𝑜𝑖,𝑡 + 𝛽9𝐷𝑒𝑏𝑡 𝑡𝑜 𝑡𝑜𝑡𝑎𝑙 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑟𝑎𝑡𝑖𝑜𝑖,𝑡 + 𝜂𝑖 + 𝜀𝑖,𝑡 Where, 𝜂𝑖 shows unobservable firm effects, 𝜀𝑖,𝑡 shows the effect of unobservable cross-section variables changing over time. 𝛼 is a constant term, and 𝛽1 𝑡𝑜 9 are coefficients that show the magnitude of change in the dependent variable due to change in each of the individual independent variables respectively. The data source used in the study is CMIE (Centre for Monitoring India) Prowess. EMPIRICAL RESULTS & FINDINGS Return on assets, Debt to equity ratio, Cash and cash equivalents & Debt to total capital ratio were found to be significantly affecting dividend yield from the financial year 2000-01 to 2009-10 (result shown below in Table 1). Table 1. Below shows the results of Panel data regression results for the period 2000-01 to 2009-10. Robust Divpercent Coef. Std. Err. t P>|t| [95% Conf. Interval] Sales -1.58e-06 1.24e-06 -1.27 0.205 -4.02e-06 8.68e-07 ROA 15.11666 4.000729 3.78 0.000 7.243503 22.98981 Currentratio .2063839 .4004855 0.52 0.607 -.5817434 .9945112 Quickratio -.4230762 .4866113 -0.87 0.385 -1.380693 .5345406 Debttoequit~o -.0014024 .0004352 -3.22 0.001 -.0022589 -.0005459 Nocftota 1.640437 2.588111 0.63 0.527 -3.452783 6.733656 Cashandcash~s .0000466 .0000134 3.48 0.001 .0000202 .0000729 Retototaleq~y -.0000535 .0000495 -1.08 0.280 -.0001508 .0000439 Debttototal~o -.0557873 .0155427 -3.59 0.000 -.0863743 -.0252003 _cons 9.28502 .5192132 17.88 0.000 8.263245 10.30679 Source: Results obtained from Stata 16. The results above in Table 1 have been reported using the “Fixed effect Cluster” variant of the panel data regression technique in Stata 16 as the Hausman test suggests the use of the fixed-effect model (Appendix A.1) and is the most appropriate variant to be used when the estimates of the standard errors are robust to disturbances being heteroscedastic and autocorrelated (Appendix A.2 and A.3 respectively). (https://www.princeton.edu/~otorres/Panel101.pdf) Sales/Size, Return on the asset, and Debt to total equity ratio was found to be significantly affecting dividend yield from the financial year 2010-2011 to 2020-2021 (result shown below in Table 2 ). Table 2. Below shows the results of Panel data regression result for the period 2010-11 to 2020-21. Robust Divpercent Coef. Std. Err. t P>|t| [95% Conf. Interval] Sales 8.47e-06 2.75e-06 3.08 0.002 3.06e-06 .0000139 ROA 43.52311 8.825933 4.93 0.000 26.15453 60.89169 Currentratio 1.246671 1.246429 1.00 0.318 -1.206181 3.699522 Quickratio -1.688633 1.592382 -1.06 0.290 -4.822287 1.445021 Debttoequit~o .0963707 .0469315 2.05 0.041 .0040142 .1887273 Nocftota 4.653729 3.83322 1.21 0.226 -2.889675 12.19713 Cashandcash~s -3.33e-08 .00001 -0.00 0.997 -.0000197 .0000197 Retototaleq~o -.0043646 .0022172 -1.97 0.050 -.0087279 -1.23e-06 Debttototal~o -.0115922 .0214151 -0.54 0.589 -.0537352 .0305507 _cons 7.251368 .8931895 8.12 0.000 5.493658 9.009078 Source: Results obtained from Stata 16. The results above in Table 2 have been reported using the same variant of panel data regression technique as the Hausman test suggests the use fixed-effect model (Appendix B.1 ) and standard error estimates are robust to disturbances being heteroscedastic (Appendix B.2). https://www.princeton.edu/~otorres/Panel101.pdf Anwar & Kumar., Indian Journal of Finance and Banking 10(1) (2022), 54-60 58 Return on assets, Debt to equity ratio, Cash and cash equivalents & Debt to total capital ratio were again found to be significantly affecting dividend yield of two time periods merged from the financial year 2000-01 to 2020-2021 (result shown below in Table 3) as consistent with the findings of the first study. Table 3. Below shows the results of Panel data regression results for the period 2000-01 to 2020-21. Robust Divpercent Coef. Std. Err. t P>|t| [95% Conf. Interval] Sales -1.58e-06 1.24e-06 -1.27 0.205 -4.02e-06 8.68e-07 ROA 15.11666 4.000729 3.78 0.000 7.243503 22.98981 Currentratio .2063839 .4004855 0.52 0.607 -.5817434 .9945112 Quickratio -.4230762 .4866113 -0.87 0.385 -1.380693 .5345406 Debttoequit~o -.0014024 .0004352 -3.22 0.001 -.0022589 -.0005459 Nocftota 1.640437 2.588111 0.63 0.527 -3.452783 6.733656 Cashandcash~s .0000466 .0000134 3.48 0.001 .0000202 .0000729 Retototaleq~y -.0000535 .0000495 -1.08 0.280 -.0001508 .0000439 Debttototal~o -.0557873 .0155427 -3.59 0.000 -.0863743 -.0252003 _cons 9.28502 .5192132 17.88 0.000 8.263245 10.30679 Source: Results obtained from Stata 16. The results above in Table 3 have been reported once again using the “Fixed effect Cluster” variant of the panel data regression technique same as in the first study as the Hausman test suggests the use of the fixed-effect model (Appendix C.1) and it is a robust method to be used where the standard errors disturbances are heteroscedastic and autocorrelated (Appendix C.2 and C.3 respectively). CONCLUDING REMARKS The following three conclusions can be made based on the above results of this study-  The dividend yield is directly related to the Profitability of the firms as per findings in earlier studies. As more profitable the firm, the more its ability to pay dividends to shareholders.  Leverage has a negative impact on the payment of dividends by firms once again consistent with the findings of the previous studies.  The liquidity of firms has a positive relationship with dividend yield as the more liquid the firms, they have more ability to pay cash dividends. Figure 4. Shows J.C. Van Horne's classification of factors affecting Dividend Policy. The above findings can be put into an above-shown figure of J.C. Van Horne's classification of factors affecting dividend policy (shown with blue arrows). Author Contributions: Conceptualization, R.A. and S.K.; Data Curation, R.A. and S.K.; Methodology, R.A.; Validation, R.A. and S.K.; Visualization, R.A. and S.K.; Formal Analysis, R.A.; Investigation, R.A.; Resources, R.A., Software, R.A.; Writing – Original Draft, R.A.; Writing – Review & Editing, S.K.; Supervision, S.K.; Project Administration, S.K.; Funding Acquisition, R.A. Authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement: Ethical review and approval were waived for this study, due to that the research does not deal with vulnerable groups or sensitive issues. Funding: The authors received no direct funding for this research. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to restrictions. Conflicts of Interest: The authors declare no conflict of interest. REFERENCES Black, F. (1998). The Dividend Puzzle'. Streetwise: The Best of the J Portfolio Manag, 10. Brahmaiah, B., Srinivasan, P., & Sangeetha, R. (2018). Determinants of corporate dividend policy in India: a dynamic panel data analysis. Academy of Accounting and Financial Studies Journal, 22(2), 1-13. Retrieved from Anwar & Kumar., Indian Journal of Finance and Banking 10(1) (2022), 54-60 59 https://www.abacademies.org/articles/determinants-of-corporate-dividend-policy-in-india-a-dynamic-panel- data-analysis-7138.html Denis, D. J., & Osobov, I. (2008). Why do firms pay dividends? International evidence on the determinants of dividend policy. Journal of Financial economics, 89(1), 62-82. https://doi.org/10.1016/j.jfineco.2007.06.006 Labhane, N. B., & Mahakud, J. (2016). Determinants of dividend policy of Indian companies: A panel data analysis. Paradigm, 20(1), 36-55. https://doi.org/10.1177%2F0971890716637698 Madra-Sawicka, M., & Ulrichs, M. (2020). Dividend payment policy across food industry companies–the case of European economies. Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, 64(4), 146-159. https://doi.org/10.15611/pn.2020.4.11 Miller, M. H., & Modigliani, F. (1961). Dividend policy, growth, and the valuation of shares. the Journal of Business, 34(4), 411-433. Retrieved from https://www.jstor.org/stable/2351143 Nathani, N., & Gangil, R. (2019). Determinants of Dividend Policy in Indian Companies: A Panel Data Analysis. In Proceedings of 10th International Conference on Digital Strategies for Organizational Success. http://dx.doi.org/10.2139/ssrn.3308721 Pinto, G., & Rastogi, S. (2019). Sectoral analysis of factors influencing dividend policy: Case of an emerging financial market. Journal of risk and financial management, 12(3), 110. https://doi.org/10.3390/jrfm12030110 Yusof, Y., & Ismail, S. (2016). Determinants of dividend policy of public listed companies in Malaysia. Review of International Business and Strategy, 26(1), 88-99. https://doi.org/10.1108/RIBS-02-2014-0030 APPENDICES Appendix A 1) Hausman fixed random Coefficients (b) (B) (b-B) sqrt(diag(V_b-V_B)) fixed random Difference S.E. Sales -1.58e-06 -5.68e-07 -1.01e-06 1.38e-06 ROA 15.11666 19.97453 -4.857872 .6234698 Currentratio .2063839 .2309103 -.0245264 .2066785 Quickratio -.4230762 -.4337633 .0106871 .2268247 Debttoequi~o -.0014024 -.0019628 .0005604 . Nocftota 1.640437 4.789302 -3.148865 .4447369 Cashandcas~s .0000466 .0000365 .0000101 7.02e-06 Retototale~y -.0000535 -.0000806 .0000271 .0000205 Debttotota~o -.0557873 -.0647497 .0089623 .0058542 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)'[(V_b-V_B)^(-1)](b-B) = 81.36 Prob>chi2 = 0.0000 (V_b-V_B is not positive definite 2) xttest3 Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (300) = 1.2e+06 Prob>chi2 = 0.0000 3) xtserial Divpercent Sales ROA Currentratio Quickratio Debttoequityratio Nocft > ota Cashandcashequivalents Retototalequity Debttototalcapitalratio Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation F( 1, 296) = 11.166 Prob > F = 0.0009 Appendix B 1) Hausman random fixed similar scale. Coefficients (b) (B) (b-B) sqrt(diag(V_b-V_B)) random fixed Difference S.E. Sales 1.55e-06 8.47e-06 -6.93e-06 . ROA 59.12369 43.52311 15.60058 . Currentratio -1.104143 1.246671 -2.350813 . Quickratio 1.071532 -1.688633 2.760166 . Debttoequi~o -.0063944 .0963707 -.1027652 . Nocftota 7.88746 4.653729 3.233731 . Cashandcas~s 4.48e-06 -3.33e-08 4.51e-06 . Retototale~o -.0051832 -.0043646 -.0008186 . Debttotota~o -.0351521 -.0115922 -.0235599 . https://doi.org/10.1108/RIBS-02-2014-0030 Anwar & Kumar., Indian Journal of Finance and Banking 10(1) (2022), 54-60 60 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(7) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 482.64 Prob>chi2 = 0.0000 (V_b-V_B is not positive definite) 2) xttest3 Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (301) = 5.5e+05 Prob>chi2 = 0.0000 3) xtserial Divpercent Sales ROA Currentratio Quickratio Debttoequityratio Nocft > ota Cashandcashequivalents Retototalequity Debttototalcapitalratio Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation F( 1, 298) = 0.791 Prob > F = 0.3746 Appendix C 1) hausman fixed random Note: the rank of the differenced variance matrix (6) does not equal the number of coefficients being tested (9); be sure this is what you expect, or there may be problems computing the test. Examine the output of your estimators for anything unexpected and possibly consider scaling your variables so that the coefficients are on a similar scale. Coefficients (b) (B) (b-B) sqrt(diag(V_b-V_B)) fixed random Difference S.E. Sales -1.58e-06 -5.68e-07 -1.01e-06 1.38e-06 ROA 15.11666 19.97453 -4.857872 .6234698 Currentratio .2063839 .2309103 -.0245264 .2066785 Quickratio -.4230762 -.4337633 .0106871 .2268247 Debttoequi~o -.0014024 -.0019628 .0005604 . Nocftota 1.640437 4.789302 -3.148865 .4447369 Cashandcas~s .0000466 .0000365 .0000101 7.02e-06 Retototale~y -.0000535 -.0000806 .0000271 .0000205 Debttotota~o -.0557873 -.0647497 .0089623 .0058542 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)'[(V_b-V_B)^(-1)](b-B) = 81.36 Prob>chi2 = 0.0000 (V_b-V_B is not positive definite) 2) xttest3 Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (300) = 1.2e+06 Prob>chi2 = 0.0000 xtserial Divpercent Sales ROA Currentratio Quickratio Debttoequityratio Nocft Cashandcashequivalents Retototalequity Debttototalcapitalratio > ota Cashandcashequivalents Retototalequity Debttototalcapitalratio 3) Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation F( 1, 296) = 11.166 Prob > F = 0.000 Publisher’s Note: CRIBFB stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. © 2022 by the authors. 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