Capital Structure - Science topic

look at a couple of last issues of journals that publish that topics, for latest topics

Missing data is a common problem in financial data. There are several methods to handle missing data in financial data. One approach is to replace the missing value with a constant value. This can be a good approach when used in discussion with the domain expert for the data we are dealing with. Another approach is to replace the missing value with the mean or median. This is a decent approach when the data size is small but it does add bias. A third approach is to replace the missing value with values by using information from other columns.

Research have been conducted on developed countries (countries with strong economies) and developing countries (countries with low per capita income). In such research, capital structure (i.e., long-term, short-term, total debts, etc.) can be used as the independent variable in order to test its effect on the dependent variable, financial performance (i.e., ROA, ROE, ROI, profit margin, etc.).

I think the following paper will help you out in this regard:

Effects of Financial Performance, Capital Structure and Firm Size on Firms' Value of Insurance Companies in Nigeria.

H Ayuba, AJ Bambale, MA Ibrahim… - Journal of Finance …, 2019 - search.ebscohost.com

Yes, you can do.

The COVID-19 Impact on Corporate Leverage and Financial Fragility

The optimal capital structure for a given firm exists only in theory. It is an illusion in modern finance theory. The debt to equity ratio is without any logic in practice, because the most important is the differentiation between short and long-term debt. Therefore, long term engaged assets (inventories and short-term receivables) should be long term financed. To achieve this principle, the difference between actual net working capital and net working capital that is needed, is crucial. This is called “capital adequacy” of the company. This difference means a surplus or a deficit of net working capital. Its movement shows the movement of the solvency risk of a company. Maintaining liquidity and solvency of the company is a prerequisite for a sound based corporate strategy. More about capital adequacy, you can find on:

https://www.hrvatski-racunovodja.hr/pub/2018/rim-2018-zbornik-znanstvenih-radova.pdf. (Živko Bergant)

Capital Structure and Sustainability: An Empirical Study of Microfinance Institutions

Capital Structure, Financial Performance, and Sustainability of Micro-Finance Institutions (MFIs) in .................

Effect of Capital Structure on Financial Sustainability of Deposit-Taking Microfinance Institutions in ...................

Dear Alhaji Ahmadu Ibrahim,

Yes, intellectual capital is one of the most important success factors for many business entities and projects implemented in various spheres of human activity.

Thank you very much,

Best wishes,

Dariusz Prokopowicz

It depends on the estimated findings of your final model design. You have to check whether the interaction term LIQUIDITY x PRECRISIS_DUM could capture the potential effect or not.

In my opinion, Market Timing Theory (Baker & Wugler, 2002) is a capital structure theory that is closer to the real conditions of companies in general.

It depends on a number of issues, the most important of which is the Objectives of your study and the Question(s) you want to answer by your study. You may add Liquidity to the other predictors if you deem necessary to fulfil your study purposes. You may also want to add Tobin's Q as a financial performance variable.

Dear Chia-Lin Chang,

Yes of course. The currently used automated trading systems used by institutional investors are based on analytical models that use the latest achievements in the implementation of statistics and econometrics in the field of finance.

Thank you, Best wishes,

Dariusz Prokopowicz

It could be interesting to include the exchange rate with respect to the currency, for an international projection.

You are welcome, Jan Jansen . You may want to give the link to your SCF paper.

I have a number of published research papers dealing with similar issue. You may want to check them out.

Dear Vladimir Mirkovic,

Yes, I agree, the factor of moral hazard, breaking the rules of business ethics, moral hazard, etc., played a significant role in generating the 2008 global financial crisis, but these were not the only major determinants that caused this crisis. Other significant factors include mistakes made as part of state intervention, creating financial innovations that precede the development of control systems of financial systems supervision institutions, imperfections in the procedures and systems of credit risk management, the generation of a speculative bubble on credit derivatives created specifically to maintain the economic situation in the housing sector, real estate e.t.c. In the context of the effective functioning of banking, a particularly key issue is to maintain high standards of financial risk management, including primarily credit risk, debt, financial liquidity, etc., and to apply high standards of business ethics and corporate social responsibility to avoid financial and economic crises, i.e. it appears from time to time in Western financial systems. The issue of unethical business practices of investment banking, which became one of the key factors in generating the global financial crisis of 2008, is described in my publications available on the Research Gate portal. Such practices that do not comply with the principles of business ethics and corporate social responsibility should not be used in finance and banking, including in the field of granting loans, selling securities, including subprime bonds, and other forms of external financing, as it largely leads it is the execution of financial transactions with too high credit risk etc. and, consequently, also to financial and economic crises. In banking, these standards should be high because banks are institutions of public trust. The only issue that remains is the effective use of modern information technology, ICT, Internet and Industry 4.0 in finance, including the improvement of risk management processes. Therefore, it is also necessary to improve operational and technological risk management processes as well as IT systems, with particular emphasis on the risk of loss of data transferred via the Internet as part of the development of online electronic banking, including mobile banking. I am conducting research in this field. I have published my conclusions from the research in scientific publications that are available on the Research Gate portal. I invite you to research cooperation on this interesting and still topical issue.

Best wishes,

Dariusz Prokopowicz

@Eulogio thank you so much sir

اعتقد ذلك... ولهذا أهمية وأثر كبير في هذا الخصوص

Yes. It is a continuing debate.

LITERATURE REVIEW. If you have a topic, conduct literature review of you research issue of that topic. The literature will tell you what factors or variables had been used in prior research. see which factors or variables are available in your target territory of study, what could be substituted if not available? what new variables could be added and tested if the current condition in your country of study differs from the literature.

Hi Ali,

You're most welcome. Just in case you need a comparison on small credit banks, you may look at this link https://scholar.google.com/citations?user=Bp0BE7kAAAAJ&hl=en&gmla=AJsN-F7HTwoOAYlZJhCoSTL9gVKNAGhLi8Ie1WaU-IVC1rm5Rdp7n4J7ylS6ZZsGn2Y5YpSePXmEEsM0Igvjl_9dNg7O23sBysD275h9CCQsKQaww-q6_0i_MsEtGb6etyji0e9ddw1p

for my articles and PhD thesis in Credit Banks in Indonesia. Thanks.

Kind regards,

Yafet

Sanjeev Bansal

It depends on who your respondents are; their risk appetite, composition of their portfolios, investment strategy, and whether they equate "Low Debt" with "Peace of Mind" or "Low EPS" with "Peace of Mind" and/or "Peace of Mind" with "Low EPS." Context is also critical, sector (GICS), stock (defensive or growth) and business cycle (expansionary vs recessionary).

You cannot include fixed effects and industry level variables; use random effects and drop the fixed effects; see these three papers

This topic is extensively researched and hence it would be difficult to find question(s) that is/are not answered.

So try first to write your research question(s) and go through the literature to see if any of your research question(s) is/are not answered or addressed adequately.

For a master degree you may consider this topic but for a PhD you should be very careful before you go further.

Further, banks are totally different than other sectors as they operate at a very high level of leverage and you need to bear this in mind.

You can find a very good summary of research conducted in this area in a paper written by Frank and Goyal (2009).

"Frank, M. Z., & Goyal, V. K. (2009). Capital Structure Decisions: Which Factors are Reliably Important?. Financial Management • Spring 2009 • pages 1 - 37"

DEBT RATIO = Total debts / Total Assets

DEBT RATIO = Total liabilities / Total Assets

LARGE ASSETS tbat has better return of asset, i.e. effective use of assets to produce higher income. With better ROA or high income from assets tbere is no need to borrow money. This is called self sufficient, no need for borrowing to sustain operations. When there is no need to borrow, there is no or little liabilities or debts, thus, debt ratio is low.

I Second Mr Sheikh

Yes, you can find it in datastream.

I think you need to conduct some pre-estimation tests to determine any possible heteroskedasticity or cross sectional dependence in the dataset. You may also read Tsalavoutas et al. 2012, Moundigbaye, William S. Rea, and W. Robert Reed 2018.

Hello, it is do difficult to find out globa data for capital structure.

I believe it is a better way, that you might find them in IMF(Asian) resources.

We did not try Fitch connect. Institutional ownership is provided by Bloomberg.

Best .

@Adnan read and review some articles from the Gurus in the area. You may search these articles from reputable research platforms like Research Gate, SSRN google scholar etc. Another simple way to get a possible research gap is to create a meaningful interaction among the predictors ( Mediation or moderation)

Can you share about model or idea need to be worked.

Hi Muhammad,

In addition to the earlier suggestion, you might want to consider this publication by Aggarwal & Kyaw (2010) which examines capital structure and dividend policy both from empirical and theoretical points of view:

Best wishes..

You may read literature on Agency Theory, Agency Costs and Corporate Governance. Many scholars argue that Financial Performance and Shares Valuation has an association with quality of governance. Moreover, the Corporate Governance rules under Listing Agreement Clause 49 requires corporate to form various committees including audit committee to look into financial affairs of organization.

Test the following;

i. Normality,

ii. Collinearity,

iii. Homoscedasticity,

iv. Linearity

*

However, you can read the file i have attached here on panel data for proper guidance and steps. And i am very sure that you will get your problem(s) resolved.

*

*

Goodluck

Dear Ikram

Please read my paper on capital structure

Available on:

All the best

Oscar

EBITDA (EBIT + Depreciation and Amortization, EBITDA) is a proxy, it is an incomplete version of FCF or any other CFs (you can arrive to FCF, to CFE and to CCF starting from EBITDA). What is of interest for valuation is the Free Cash Flow, FCF or the Capital Cash Flow CCF or the Cash Flow to Equity CFE or the FCF + Tax Savings (when using the APV approach for valuation).

In summary, if you need to value a firm, don't use EBITDA, use the proper cashflow (ALL methods, if properly done, will yield the same and identical result: the firm value (in the case of CFE you need to add the value of debt).

In addition, multiples are not a "method" for valuation. Use multiples AFTER you have done a proper and detailed valuation. Having value, you can COMPARE with multiples for the industry. Say, you divide your calculated Value through EBITDA, EBIT, Net Income, Sales, whatever..., and compare that with the average for the industry or with a recently transaction for a firm belonging to the same industry.

Best regards

Ignacio

Dear Riman

I hope you would have gotten good answers. Nevertheless, I apply some test such as cross-sectional dependency test using stata. if you looking any help regarding stata, most welcome!

For ykur convenience, I am sharing link of my thesis. Have a look on it. Good luck!

Multi-level(2 or 3) SEM would be a solution

Hi,

You can perhaps try to use an "instrumental variable" . Anyway I advise you to read this paper:

"Endogeneity in empirical corporate finance" Michael R Roberts and Toni Whited, 2012

Best regards.

I think the following paper could help you do the needed estimation:

Forecasting default with the Merton distance to default model

ST Bharath, T Shumway - Review of financial studies, 2008 - Soc Financial Studies

The actual placing of the GDP observations in the spreadsheet of your Panel depends on how it is arranged.

Where we have added Macro-economic avariables to a panel analysis we have just added them as another column in the panel.

If the Panel is by company and several vaiables just add it as the last variable for the correct year; this involves 10 entries for 1 company.  Repeat this for each company.  It is a bit repititous but won't take to long for 200 companies.  If you can write a little visual basic it is even quicker.

As final comment you might want to use some other measures like inflation, unemployment rate, exchange rate.  Best to collect them all and do it at once.

HI,

This recent paper may be of interest for you:

H chou, P Hamill, Y Yeh, "Are regulatory compliant independent director appointements the same? An analysis of Taiwanese board appointments", Journal of Corporate Finance, 2016, forthcoming.

Best regards

We have been working on this subject and used the Dickinson (2011) model to distinguish the life cycle stages in which firms can be classified. It has advantages and drawbacks. If you are interested, you can consult the following references available through Researchgate and/or directly in the web page of the Juornal:

- CASTRO, P., TASCÓN, M.T., AMOR, B. and DE MIGUEL, A. (2016). Target leverage and speed of adjustment along the life cycle of European listed firms, Business Research Quarterly, 19(3), July-September, 188-205.

- CASTRO, P., TASCÓN, M.T., and AMOR-TAPIA, B. (2015). Dynamic analysis of capital structure in technological firms based on their life cycle stages. Spanish Journal of Finance and Accounting, Vol. 44, nº 4, 458-486

- CASTRO, P., TASCÓN, M.T. y AMOR-TAPIA, B. (2014). The role of life cycle on the firm’s capital structure. Pecvnia. Revista de la Facultad de Ciencias Económicas y Empresariales de León, nº 19, julio-diciembre, 131-155.

I hope you find them useful.

Maria T. Tascon

why not trying thomson reuters Data stream

I am currently finishing up my dissertation on external costs generated by leveraged transactions involving corporations.

During the research I have developed a sound theory of capital structure vs. transactions involving the capital structure. Basically in order to link the two, you need to construct the current capital structure of the company (using the basic MM and accounting proposition of Enterprise Value = Debt + Equity, or Assets = Liabilities + Equity) and then construct the new capital structure which will be determined by the amount of debt and equity assumed by the post-transaction entity.

I know this all may sound somewhat incomprehensible, but in order to reach these conclusions you need not only good theory, but also a very good understanding of the financial modelling procedures used by investment bankers involved in such transactions.

If anyone is interested in developing this further, please let me know.

Dear Ali Hussain,

I suggest to read some articles:

de Jong, A., Kabir, R., and Nguyen. T.T. (2008), “Capital structure around the world: The roles of firm- and country-specific determinants”, Journal of banking and Finance, Vol. 32, No. 9, pp. 1954-1969.

Harris, M., and Raviv, A. (1991), “The Theory of Capital Structure”, The Journal of Finance, Vol. 46, No. 1, pp. 297-355.    

Masulis, R. (1988), “The Debt/Equity Choice”, Ballinger Publishing, Co.

Modigliani, F., and Miller, M.H. (1958), “The cost of capital, corporation finance, and the theory of investment, The American Economic Review, Vol. 48, No. 3, pp. 261-267.

Myers, S.C. (1977), “Determinants of corporate borrowing”, Journal of Financial Economics, Vol. 5, No. 2, pp. 147–175.

Myers, S.C. (1984), “The Capital Structure Puzzle”, The Journal of Finance, Vol. 39, No. 3, pp. 574-592.

Myers, S.C., and Majluf, N.S. (1984), “Corporate financing and investment decisions when firms have information that investors do not have”, Journal of Financial Economics, Vol. 13, No. 2, pp. 187-221.

Rajan, R.G., and Zingales, L. (1995), “What Do We Know about Capital Structure? Some Evidence from International Data”, The Journal of Finance, Vol. 50, No. 5, pp. 1421-1460.

Ross, S.A. (1977) “The Determination of Financial Structure: The Incentive-Signalling Approach”, The Bell Journal of Economics, Vol. 8, No. 1, pp. 23-40.  

Strebulaev, I.A., and Yang, B. (2013), “The mistery of zero-leverage firms”, Journal of Financial Economics, Vol. 109, No. 1, pp. 1-23.

Greetings,

Fabrizio

HI,

I agree with Shabana Kashif Talpur. I just want to add that multiple block-holders may collude towards a same goal, or disagree and fight against the others. Suppose some blockholder wants to get a short term risky debt , and that the others prefer. long term debt,, a conflict of interest may happen. The CEO position and traits deserve perhaps also to be taken into account.

Best regards

Hi,

Perhaps you may benefit from reading this paper:

J L Coles, M Lemmon, JF Meschke, "Structural models and endogeneity in corporate finance: The link between managerial ownership and corporate performance", Journal of Financial Economics, 2012, 103, p 149 -168.

Best regards.

Based on selected empirical evidence across countries, the pecking order theory seems applicable to listed firms in some countries.                                                                  

Ni and Yu (2008) use the entire cross-section sample of China's listed firms. The authors find no evidence that listed firms in China follow a pecking order when they need funds to finance investment projects. Further subgroup analyses indicate that big firms follow a pecking order and small and medium firms do not. Likewise, Aggarwal and Zong (2006) use comparable data of corporations in the US, the UK, Japan, and Germany, they find that in all four countries, investment levels are significantly positively influenced by levels of internal cash flows, indicating that corporations may follow the pecking order theory.                                                

Conversely, Delcoure (2007) findings reveal that neither the pecking order, tradeoff, nor agency costs theories explain the capital structure policies in Central and Eastern European (CEE) countries. They conclude that the differences of banking systems, disparity in legal systems governing corporate operations, shareholders’ and bondholders’ rights protections, sophistication of stock and bond markets, and corporate governance, are the factors that influence corporate leverage decisions for CEE countries.

It appears that the pecking order theory explains capital structure decisions of some listed firms, but the validity of pecking order theory is country specific.

It is interactive. Structure determines governance and id is determined by it, especially as between majority and minority shareholders and especially in Asia. Performance in conventional even studies terms is dealt with in agency theory modified by positive accounting theory with an underlying thread that the more power majority shareholders have over the Board and the debt holders, the higher earnings will be for them. However, nothing in governance overrides the eternal business verities of what drives a profitable business to succeed, increase earnings quality and maintain solvency. Governance is icing on the cake, not the cake itself.

Hello Victor, This seems like a good question and my initial reaction is to agree with you prior that there are links.  In my research on companies and small businesses in several countries the finding indicate statistical significant relationships between financial structure and performance.  This is of course consistent with the financial leverage impacting beta.  The method used in these articles is regression models with Tobin's Q, ROA, ROE etc used as dependent variable.  The properties of the data influence the particular regression used OLS, GLS and GMM, etc. and while most have used panel data when poolability test indicated inadequate changes over time cross-sectional regression is used.

Strategy as a series of independent variables is an interesting idea.  As strategy will be reflected in operating leverage you would expect it to be important.  As strategy will also be impacted in financial leverage there is a jointness in the testing which may mean to equations with a high degree of commonality. 

If you can make financial structure eg D/A the dependent variable, and Strategy a continuous variable between very conservative to very aggressive, and use performance metrics as the independent variables then you might have two nice equations.  As Strategy and financial structured are likely to be correlated could then use "multivariable" to analyse which could be fun.  Enjoy! (you can find my papers on research gate or Google if you want to check any of them out)

All of the above are rational answers to your question but will not really address the underlying issue I see which is your question itself.

The issues are 1/ "wealth", 2/ whether you can continually maximise it, 3/ what is rational and 4/ "optimal capital structure".

1/ There are good books on wealth by Beinhocker, Santa Fe etc but what is wealth? It also demands a time consideration so therefore are you discussing price/value over rational financial decisions compared to eventual outcomes?

2/ Is it wise to always maximise or should the strategy be to target an optimal outcome and achieve it? Maximisation involves increased risk that outcomes do not materialise.

3/ In today's multi-investor world the size of investments need must necessarily involve investors with competing objectives so what would be rational for one may not be for another, besides reflect on what you mean by rational, is it "logical process" or a reference to accepted frameworks?

4/ This infers you are looking at regulated industries which opens a completely different problem that is well articulated in the financial press. If you infer gambling via non-regulated instruments with all its risks then that is equally available from the G-20 and FT etc, otherwise you are always putting your capital at risk with equity/debt it's just the price at the time that needs to be assessed.

Remember price and value are not the same and governance, variety and time are variables that need attention.

I suggest this article:

Strebulaev, I.A., and Yang, B. (2013), “The mistery of zero-leverage firms”, Journal of Financial Economics, Vol. 109, No. 1, pp. 1-23.

It really depends on the group of firms you are analyzing. However, what is really important it to watch for the causality issue in your econometric analysis. In other words, does capital structure affect profitability or profitability affects capital structure???

Thank you so much dear. I am screening the whole universe (S&P) for Shariah compliance. my screening is based on 30 years data... Once I am done I can share it with you... All the calculations are based on my analysis so may be you will observe lot of differences with the list of S&P Shariah indices

You may want to consider capital structure based on ownership. Intuitively ownership capital structure for Asian countries. 

PURPOSE O UNIT ROOT TEST: The purpose of unit root test is to verify whether the data series (time series) is non-stationary when running autoregressive modeling. The rationale for testing data for non-stationarity is to be sure that there is no effect from shock that would throw the series out of its long-term equilibrium, i.e. integrated data. Is 9 years considered long-term for purposes of verifying mean reverting characteristic of autoregressive model? To answer this question, you need to revisit your research objective: what does the research want to achieve?

Assume that the 9 years period in the study includes a period of economic crisis. Assume further that the effect of the crisis exert significant effect upon the series equilibrium. Is the unit root est necessary? Here, you need to plot the data and see how the data are distributed.

FIRM CAPITAL STRUCTURE STUDY: Capital structure of a firm is not a univariate array. Firms raise their capital from (i) debt, (equity), or (iii) hybrid. It is not enough by just using ratio analysis and use the ratio from each year to construct a model through autoregressive model. Consider fixed effect and random effect on (i) - (iii) and then test for changes, i.e. first 3 years, second test 4 years, ..., nth test 9 years. (a) are their change in the intercept? (b) are there changes in the slope? and (c) are there changes in the error term? See atatched article.

[1] Dickey, D. A.; Fuller, W. A. (1979). "Distribution of the Estimators for Autoregressive Time Series with a Unit Root". Journal of the American Statistical Association 74 (366a): 427–431. doi:10.1080/01621459.1979.10482531

[2] Sargan, J. D.; Bhargava, Alok (1983). "Testing Residuals from Least Squares Regression for Being Generated by the Gaussian Random Walk". Econometrica 51 (1): 153–174.JSTOR 1912252

A panel of data consists of a group of cross-sectional units that are observed over time. The number of cross-sectional units may be denoted by N and the number of time periods by T. The SUR technique is one of panel data estimation metdods. The acronym SUR stands for seemingly unrelated regression equations which was described by Zellner (1962). SUR is a way of estimating panel data models that are long (large T) but not wide (small N). When estimating an SUR model, the data need to be arranged as a time series (not a panel) with different variables listed separately. In the basic SUR model, the errors are assumed to be homoscedastic and linearly independent within each equation. The error of each equation may have its own variance. Each equation is correlated with the others in the same time period. The latter assumption is called contemporaneous correlation, and it is this property that sets SUR apart from other models. If contemporaneous correlation does not exist, the LSR method applied separately to each equation (fund’s portfolio) is quite efficient.

Best regards,

Joanna

One of my paper published in International Journal of Commerce and Management entitled "Impact of capital structure on performance" indicate that capital structure have material effects on firm value in Pakistan. Pakistan is bank-based economy. Firms prefer to borrow when internal funds are insufficient to meet their needs. However banks prefer to extend short-term loans on favorable terms than long-term risky loans. Moreover interest payments are tax-deductible. These factors encourage the managers to use more debt in their capital structure which in turn generate the agency problems between shareholders and debt holders and thereby creates a negative impact on firm performance.

I am afraid that little if anything has changed in risk modeling and the understanding of risk. One should first identify the nature of the system involved. There is no consesus on it, so naturally different approaches are proposed in which some proposals are evident flaws. I can supply loads of examples, how risk was understimated or completely ignored whereas the system revealed symptoms

of collosal changes ex ante, before the events took place. My contribution to this issue is based on the studies of chaotic systems. A lot of discourse takes place on the rigors fulfilled to declare a system is chaotic. Those familiar with the subject know that those rigors are not easily met or computed. Traditional approach through the Lyapunof exponent is not entirely wrong but inappropriate for large perturbations and there is a simpler and shorter way for declaring that the system is chaotic (and deterministic). This directly leads to a revision of the theory of catastrophy. It is a giant step forward from SOC (Self-Organized Criticalities) and the theory of fragility and infragility (Taleb), both based on some degree of randomness in the system. Now we can say that we cannot attribute all cause-and-effect to randomness alone. I can make an even stronger statement, that very little, if not none, of large perturbations is the work of unknowledgable random causes. The discovery of  intrinsic order hidden in the system raises some fundamental questions about the extent of the free market confronted with the work of the so called fat fingers and big hands and also about the borders of social acceptance of control and manipulation. One of the gravest examples is of course the recent devaluation of the euro against the swiss franc (EURCHF). Nothing was random there. It was a well prepared action supported by finely tuned chaotic parameters and a very clever scientific setup. 

Ian Dalling proposed a programme. You select, you click and press OK? I think that we misunderstand the issue of the question.

The problem is that most risk management sucks and creates fragilities while clearly there are antifragile entities who benefit from ill-conceived risks by others. How they do it, is a story for a good thriller book.  That they do it, there is no doubt.         

As I read the answers above, I tended to agree with parts of J. Shi, B.T Matemilola, R. Sharma, and A. Andriansyah. We all recognize the limitations of OLS; however, I doubt any of us would be comfortable ruling it out before seeing the model and the data available, especially in the vast and diverse area of economics and finance research. In addition, I am not sure where I would start in teaching econometrics this semester if OLS was stripped from the course.

I think B.T Matemilola's question begs us to think deeper in a different way. Consider the rate of change in refining theory, technique, and data. It seems to me refinement in the data (i.e., both what we collect and how we collect it), may be the slowest to change in economics. Although I am certain we could improve if we could go back and start over, part of our challenge (especially with time series data) is that we often cannot retroactively refine the data. I suspect few of us would care to sacrifice consistency and/or continuity if we could start over now. We are fortunate that what we research is relatively more easy to quantify than other disciplines and those who preceded us devised more or less effective ways to measure variables. I have been fascinated with the ability to adapt in economics, and especially econometrics, and without consciously doing it, it seems like we have followed the least-costly path of improvement in expanding the frontiers of knowledge.

Prof. Stuart Locke, thank you for your answer. I have come across a study that report negative coefficient on the lagged debt ratios but the authors are silent on the interpretation. I suspect that they find it hard to interpret but it is possible for the coefficient to be negative as you also noted.  Although, such results are uncommon in the literature.

Many SMEs are first time entrepreneurs, and do not have sufficient bandwidth in all the functional areas. They are not able to estimate their own short term and long term financial needs accurately given all the uncertainties they face. Financial agencies and banks however require clarity in financial projections, documentation, current and fixed assets to be able to decide quantum of working capital requirements vis-a-vis long term loan requirements of the SME.

This is such a difficult topic and there is unfortunately not one answer. Like Susanne mentioned, it is time related. However, there is definitely a limit to the extend of debt that should be used as an investor. You might want to read "Leverage, please use responsibly" for some ideas on optimal level of debt. Good luck.

Have you considered the Blundell and Bond method for a dynamic panel. There is a stata ado file that does this.

Yes, sure. Weak corporate governance would allow managers to do what they prefer to, which could be at the expense of equity holders, eg: excessive debts, etc.

This is a topic on which credible research is not available as most of them are hypothetical due to non availability of data.

Dear Ms. Norrima, thank you very much for your attention. I have conducted a simple testing already for China and wanna to develop a more complex model. Do you familiar to some econometric model? thanks.