Home > Threshold Effects between Capital Structure and Operating Performance of Electronic Listed Firms in Taiwan
Panel Threshold
Effect of Debt Ratio on Firm Value in Taiwanese Listed Companies
Feng-Li Lina
aAssociate
Professor, Department of Accounting, Chaoyang University of Technology,
Taichung, Taiwan. Ph.D Candidate,
College of Business, Feng Chia University,
Taichung, Taiwan, ROC.
Abstract
The goal in this paper is to analyze whether leverage affects firm value for a panel of 272 Taiwanese listed companies during the nine-year period 1997 to 2005. Advanced panel threshold regression model is performed to test if there exists an “optimal” debt ratio , which may result in threshold effects and asymmetrical relationships between debt ratio and firm value. Tobin’s q is adopted as proxy variables for firm value.
The
result in this study shows that there exists two thresholds effect between
debt ratio and firm value. The estimated threshold value (,) are found
to be 48.92% and 49.55%. Among three coefficients (,,), the estimate
of coefficient , are negative but not significant which means when the
debt ratio is smaller than 48.92% or greater than 49.55% , the relationship
between debt ratio and firm value is unclear. In the second regime,
where the debt ratio is between 48.92% and 49.55%, the estimate of coefficient
is 0.009, which implies Tobin’s q will be increased by 0.009% via
1% increase of debt ratio. Tobin’s q will also be increased. Thus,
this suggests that financial managers should utilize the relevant financial
leverage wisely in order to maximize the firm’s value.
Keywords:
Tobin’s q, Panel threshold effect, Debt ratio
1. Introduction
The theory of business finance in a modern sense starts with the Modigliani and Miller (1958) capital structure irrelevance proposition, showing that the firm value and weighted average cost of capital is unaffected by the financial structure of the firm. However, Modigliani and Miller’s (1958) perfect market assumptions: such as no transaction costs, no taxes, symmetric information and identical borrowing rates, and risk free debt, are contradictory to the operations in the real world. The literature on capital structure emphasis has been placed on releasing the assumptions made by Modigliani and Miller, in particular by taking into account corporate taxes (Modigliani and Miller, 1963), personal taxes (Miller, 1977), information asymmetries (Ross, 1977; Myers and Majluf, 1984; Myers, 1984), agency costs (Jensen and Meckling, 1976; Myers,1977; Kim and Sorensen,1986), and bankruptcy costs(Stiglitz, 1972; Castanias,1983; Altman,1984). Two main theories currently dominate the capital structure debate: the trade-off theory and the pecking order theory.
The trade-off theory, developed by Myers (1977), is a static amount of debt leading managers to find the “optimal capital structure” that maximize firm value when the benefits of debt equal the marginal cost of debt. The pecking order theory, developed by Myers (1984) and Myers and Majluf (1984), predicts that information asymmetry between managers and investors creates a preference ranking in firms’ financing policy. Beginning with internal funds, followed by debt, and then equity, companies work their way up the pecking order to finance investment in an effort to minimize adverse selection costs. Aggarwal and Zong (2006) documents that in all four countries (USA, UK, Japan, and Germany), controlling for the investment opportunity set, investment levels are significantly positively influenced by levels of internal cash flows, indicating that firms face limitation in access to external finance and may operate using a pecking order.
Like the pecking order theory, two additional theories of capital structure assert that there is no reversion to a target capital ratio: the market timing theory and the inertia theory. Unlike the pecking order theory, the market timing theory argued by Baker and Wurgler (2002), asserts that the most realistic account of leverage is that it reflects the attempts by managers to time the equity market—issuing equity when the market is receptive.. The firm’s observed capital structure reflects its cumulative ability to sell overpriced equity shares. If market timing affects debt and equity issuance decisions, then measures of the equity market (the market-to-book ratio) and the debt market (the interest rate) ought to have significant impacts on changes in leverage. The inertia theory suggested by Welch (2004), is that managerial inertia permits stock price changes to have a main effect on market-valued debt ratios. In Welch’s view, shocks to the stock market affect capital structure but since firms do not take steps to reestablish a leverage target, the levels of debt and equity do not influence subsequent leverage adjustments. In contrast, the tradeoff theory maintains that market imperfections generate a link between leverage and firm value, and firms take positive steps to offset deviations from their optimal debt ratios. The pecking order, market timing, and inertia theories of capital structure imply that managers perceive no great leverage effect on firm value and therefore make no effort to reverse changes in leverage.
Unlike Welch (2004), Flannery and Rangan (2006) find that stock price changes have only transitory effects on capital structure. Their evidence indicates that firms do target a long run capital structure, and that the typical firm converges toward its long-run target at a rate of more than 30% per year. However, more than half of the observed changes in capital structures can be attributed to targeting behavior while market timing and pecking order considerations explain less than 10% each. Frank and Goyal (2004) found that there is a long-run leverage ratio to which the system reverts. Deviations from that ratio help to predict debt adjustments, but not equity adjustments. A high ratio of market to book is associated with subsequent debt reduction, but there is no effect in the equity market.
The relationship between capital structure and firm value has been the subject of considerable debate throughout the literature. There are two issues to discuss: 1. “Do firms reverse to an optima debt ratio ?”; 2. “Do leverage effect on firm value ?”. The goal of this study is to test whether application of financial leverage affects firm value of Taiwanese listed firms, we apply threshold regression model to the observed “balanced panel data” to test if there exists an optimal debt ratio which may result in threshold effect and asymmetrical responses of the firm value to the debt ratio. If this “threshold” value of is verified, the financial managers should take steps to increase debt levels in the low debt regime of debt ratio lower than the. Conversely, they should take steps to reduce debt levels in the high debt regime of debt ratio higher than.
This paper contributes to previous literature in two aspects. First, we apply advanced panel threshold regression model developed by Hansen (1999) to test if there exists a “threshold” of optimal debt usage. In contrast with traditional linear model, this nonlinear threshold model can describes the “trade-off” between the benefits of tax shields of more debts and the disadvantages of costs from additional debts that may damage the firm value. Second, it’s panel data of Taiwanese listed companies to be considered for fully examining the financial characteristics of the Taiwanese industry and solving the short period sample problem.
The
remainder of this paper is organized into five sections. Section 2 provides
data collection and Methodology, Section 3 discusses the empirical results,
and finally Section 4 concludes the study with implications of the findings
and suggestions for future research.
2.
Methodology
2.1 Sample set
This paper explores if there exists an optimal debt ratio, which may result in threshold effect and asymmetrical responses of the firm value to the debt ratio through employing threshold regression model. The investigation has been performed via “balanced panel data” for a sample of 272 selected Taiwanese companies listed on the Taiwan Stock Exchange during 1997 to 2005. Total 2,448 observations are adopted for each variable considered. Table 1 shows the industry breakdown of the sample. Eighteen industries are listed in the table 1. Electron and Textiles industries accounted for more than one-thirds of the sample, and the remaining industries had fewer than ten percent of the sample.
2.2 Variables
In order to consider the effect of market valuation of a firm, Tobin’s q, which defined as the ratio of the market value of a firm to the replacement cost of its assets, is also selected as the proxy variable for the firm value. The calculation of the approximated q, following the suggestions by Chung and Pruitt (1994), is defined as follows:
Approximated Tobin’s q = (MV + PS + DEBT)/TA,
where MV is the product of a firm's share price and the number of common stock shares outstanding, PS is the liquidating value of the firm's outstanding preferred stock, DEBT is the value of the firm's short-term liabilities net of its short-term assets, plus the book value of the firm's long-term debt, and TA is the book value of the total assets of the firm.
There are two categories of explanatory variables in our panel data. The first is the threshold variable, debt to total assets ratio (debt ratio), which is the key variable used to investigate whether there exists an asymmetric threshold effect of the leverage on firm value. In our examination, four applied control variables include management ownership ratio, natural log of total assets, ratio of R&D expenses to total sales, and ratio of advertising expenses to total assets, which are presumed to have influences upon the firm value. All data sets are obtained from Taiwan Economic Journal (TEJ) Data of Taiwan. Table 2 shows the descriptive statistics of variables for 272 firms
2.3 Research methodology
2.3.1 Panel Unit Root Models
Hansen’s (1999) panel threshold regression model is an extension of the traditional least squared estimation method, in fact. It requires that variables considered in the model need to be stationary in order to avoid the so-called spurious regression1. Thus, the unit root test is first processed in this study. Since the data are all panel in this investigation, both well known LLC (Levin, Lin and Chu, 2001) and IPS (Im, Pesaran and Shin, 1997) techniques are employed for the panel unit root tests2.
Table
2 presents the descriptive statistics of variables for 272 firms. The
result of the stationary test for each panel (i.e. explained variables,
threshold variable, and control variables) shows that all the variables
are most likely to be presumed to carry stationary characteristics since
the null of unit root are mostly rejected, especially in the findings
from LLC test3. These stationary findings are available
for further estimations of the panel threshold regression.
2.3.2 Threshold Autoregressive Model
Modern dynamic capital structure model proposed an idea of finding the “target” optimal debt ratio and firms will adjust outstanding debt levels in response to changes in firm value. This paper applies a newly-developed, alternative method: panel threshold regression model to solve this problem, to strike a “balance” between the tax benefit and the potential costs that comes along with this benefit. Since Tong (1978) proposed Threshold Autoregressive model, thereafter, this non-linear time series model has become very popular for economic and financial research.
When the Threshold Autoregressive Model is estimated, first we should test if there exists threshold effects. If we can not reject the null hypothesis, the threshold effect doesn’t exist. Again, the existence of nuisance will make the testing statistic follow non-standard distribution, which was called “Davies’ Problem4”. Hansen (1999) suggested a “bootstrap” method to compute the asymptotic distribution of testing statistics in order to test the significance of threshold effect. Furthermore, when the null hypothesis doesn’t hold, which means, the threshold effect does exist, Chan (1993) proved that OLS estimation of threshold is super consistent, the asymptotic distribution is derived. However, nuisance influences this distribution and makes it non-standard. Hansen (1999) used simulation likelihood ratio test to derive the asymptotic distribution of testing statistic for a threshold. Hansen (1999) proposed to use two-stage OLS method to estimate the panel threshold model. On the first stage, for any given threshold, compute the sum of square errors (SSR) separately. On the second stage, try to find the estimation of by minimization of the sum of squares. At last, use the estimation of threshold to estimate the coefficient for every “regime” and do analysis.
2.3.3 Threshold Model Construction
According
to the “Tradeoff Theory” of Capital Structure, when debt ratio increases,
the interest tax shield increases; however, on the other side, leverage
related costs increase to offset the positive effect of debt ratio to
the firm value. Thus, this paper aims at examining whether threshold
effect exists between the financial leverage and value. We assume that
there exists an optimal debt ratio, and try to use threshold model to
estimate this ratio, which can capture the relationship between financial
leverage and firm value as well as help financial managers make decisions.
Thus we set up single threshold model as follows:
(1)
,
Where represents proxy variables of the firm value, which are : Tobin’s q; , debt ratio, which is also the threshold variable; , the specific estimated threshold value. There are four “control variables”() that they may have influences upon the firm value, which are : natural log of total assets, : management ownership ratio,: ratio of R&D expenses to total sales, and : ratio of advertising expenses to total assets. Besides,, the fixed effect, represents the heterogeneity of companies under different operating conditions; The errors is assumed to be independent and identically distributed with mean zero and finite variance(); i represents different companies; t represents different periods.
Another threshold regression model of (1) is to set:
(2)
where I(.) represents indicator function,
can be written as:
(3)
where , , .
The
observations are divided into two “regimes” depending on whether
the threshold variable is smaller or larger than the threshold value
(). The regimes are distinguished by differing regression slopes, and.
We will use known and to estimate the parameters (, , , and ).
2.3.4 Estimation
Note that taking averages of (3) over the time index t to derive:
(4)
where ，,
and
Taking the difference between (3) and (4) yields:
(5)
where ，， and
Let
，，
Denote the stacked data and errors for an individual , with one time period deleted. Then let , and denote the data stacked over all individuals.
，,
Use this notation, (5) is equivalent to
(6)
The equation (6) represents the major estimation model of threshold effect. For any given , the slope coefficient can be estimated by ordinary least squares (OLS). That is,
(7)
The vector of regression residuals is
(8)
and the sum of squared errors,
SSE is
(9)
Chan (1993) and Hansen (1999) recommend estimation of by lease squares. This is easier to achieve by minimization of the concentrated sum of squared errors (9). Hence the least squares estimators of is
(10)
Onceis obtained, the slope coefficient estimate is. The residual vector is ， and the estimator of residual variance is
(11)
where n indexes the number
of sample, T indexed the periods of sample.
2.3.5 Testing for a threshold
This
paper hypothesizes that there exists threshold effect between the debt
ratio and firm value. It is important to determine whether the threshold
effect is statistically significant. The null hypothesis and alternative
hypothesis can be represented as follows:
When the null hypothesis holds, the coefficient=the threshold effect doesn’t exist. When the alternative hypothesis holds, the coefficient≠ the threshold effect exists between the debt ratio and firm value.
Under the null hypothesis of no threshold, the model is
(12)
After the fixed-effect transformation is made, we have
(13)
The regression parameter is estimated by OLS, yielding estimate, residuals and sum of squared errors.
Hansen (1999) suggests that the relevant F Test Approach and the sup-Wald statistic are used to test the existence of threshold effect and to test the null hypothesis, respectively.
(14)
(15)
Under the null hypothesis, some coefficients (e.g. the pre-specified threshold, ) do not exist, therefore, the nuisance exists. According to “Davies’ problem” (1977, 1987), the F statistic becomes non-standard distribution. Hansen (1996) showed that a bootstrap procedure attains the first-order asymptotic distribution, so p-values constructed from the bootstrap are asymptotically valid. Treat the regressors and threshold variable as given, holding their values fixed in repeated bootstrap samples. Take the regression residuals, and group them by individual:. Treat the sample as the empirical distribution to be used for bootstrapping. Draw a sample of size n from the empirical distribution and use these errors to create a bootstrap sample under. Using the bootstrap sample, estimate the model under the null (13) and alternative (5) and calculate the bootstrap value of the likelihood ratio statistic (15). Repeat this procedure a large number of times and calculate the percentage of draws for which the simulated statistic exceeds the actual. This is the bootstrap estimate of the asymptotic p-value for under. The null of no threshold effect is rejected if the p-value is smaller than the desired critical value.
(16)
where is the conditional mean
of .
2.3.6 Asymptotic distribution of threshold estimate
Chan
(1993) and Hansen (1999) showed that when there is a threshold effect
, is consistent for, and that the asymptotic distribution is highly
non-standard. Hansen (1999) argued that the best way to form confidence
intervals for is to form the ‘no-rejection region’ using the likelihood
ratio statistic for tests on. To test the hypothesis
We construct the testing model:
(17)
Hansen (1999) pointed out that when is too large and the p-value exceeds the confidence interval, the null hypothesis is rejected5. Besides, Hansen (1999) indicated that under some specific assumptions6 and ,
(18)
as , where is a random variable with distribution function
(19)
The asymptotic p-value can be estimated under the likelihood ratio. According to the proof of Hansen (1999), the distribution function (18) has the inverse
(20)
from which it is easy to calculate
critical values. For a given asymptotic level, the null hypothesis
rejects if exceeds .
2.3.7 Multiple thresholds Model
If there exist double thresholds, the model is modified as:
(21)
where threshold value . This
can be extended to multiple thresholds model （）.
3. Empirical Results
It’s applied in this paper that the threshold theory is proposed by Hansen (1999) and the assumption that debt ratio and corporate performance have asymmetric nonlinear relationship is made. First, if there exists threshold effect, then test double threshold and single threshold effect are tested, respectively, and the relevant formulas for both models are as follows:
for double threshold effect
for single threshold effect
The dependent variable represents firm value, which uses Tobin’s q as proxies. The independent variable represents debt ratio, which is indeed the threshold variable. is a control variable vector that contains four variables of natural log of total assets, management ownership ratio, R&D expenses to total sales ratio, and advertising expenses to total assets ratio. Besides,, the fixed effect, represents the heterogeneity of companies under different operating conditions. The errors is assumed to be independent and identically distributed with mean zero and finite variance(). i and t are symbols for firms and time periods.
This paper follows the bootstrap method to get the approximation of F statistic and then calculate the p-value. After repeating bootstrap procedure 1,000 times for each of the three panel threshold tests, Table 3 presents the empirical results of test for single threshold, double threshold, and triple threshold effects. we find that the test for a single threshold and a triple threshold are insignificant with a bootstrap p-value of 0.896 and 0.78 respectively, the significant finding at the 1% level with a bootstrap p-value of 0.004 occurs only in the test for a double threshold. We thus conclude that there exists a double threshold effect of the debt ratio on firm value. For the remainder of the analysis we work with this double threshold model.
When there exists a double threshold effect of the debt ratios on firm value, all observations are split into three regimes. Table 4 represents the regression slope estimates together with the conventional OLS standard errors and White-corrected standard errors for two regimes. Figure 1 shows the single threshold. Figure 2 and Figure 3 presents the double threshold.
The
estimated model from our empirical result is represented as follows:
and plit the observations into three regimes depending on whether the threshold variable is smaller or larger than the threshold value ().The regimes are distinguished by differing regression slopes, , and . In the first regime, where the debt ratio is below 48.92%, the estimate of coefficient is -0.001, but insignificant. In the second regime, where the debt ratio is above 48.92%% but below 49.55%, the estimate of coefficient is 0.009 , significant at the 1% level, which implies, Tobin’s q will be increased by 0.009% via 1% increase of debt ratio. In the third regime, where the debt ratio is above 49.55%, the estimate of coefficient is -0.002, but insignificant. The estimate of coefficient , are negative but not significant, which means, when the debt ratio is smaller than 48.92% or greater than 49.55% , We thus conclude that there exists an optimal debt ratio above 48.92%% but below 49.55% that increase firm value. These results are consistent with the trade-off theory, for which we may search a “balance” that the interest tax shield is equal to the incremental costs through debt financing.
This
paper further investigates the influences of four control variables
upon the firm value. The estimation of coefficients of control
variables shown in Table 5, which shows that natural log of total assets and R&D
expenses to total sales ratio have significant negative impact on firm
value. It means that the negative relationship between firm size and
firm value and the negative relationship between R&D expenses and
firm value.此段再加Hansen P361Tale4圖
4. Conclusion
Two main theories currently dominate the capital structure debate: the trade-off theory and the pecking order theory. The trade-off theory (Myers, 1977), is a static amount of debt leading managers to find the “optimal capital structure” that maximize firm value when the benefits of debt equal the marginal cost of debt. The pecking order theory ( Myers, 1984; Myers and Majluf, 1984), predicts that information asymmetry between managers and investors creates a preference ranking in companies’ financing policy. Beginning with internal funds, followed by debt, and then equity, firms work their way up the pecking order to finance investment in an effort to minimize adverse selection costs.
The goal of this paper is to investigate whether leverage affects firm value for a panel of 272 Taiwanese listed companies during the nine-year period 1997to 2005. Advanced panel threshold regression model is performed to test if there exists an “optimal” debt ratio, which may result in threshold effects and asymmetrical relationships between debt ratio and firm value. Tobin s’ q are adopted as proxy variables for firm value.
The
result shows that there exists double thresholds effect between debt
ratio and firm value. The estimated threshold value (,) are found to
be 48.92% and 49.55%. Among three coefficients (,,), the estimate of
coefficient , are negative but not significant, which means when the
debt ratio is smaller than 48.92% or greater than 49.55% , the relationship
between debt ratio and firm value is unclear. In the second regime,
where the debt ratio is between 48.92% and 49.55%, the estimate of coefficient
is 0.009, which implies Tobin’s q will be increased by 0.009% via
1% increase of debt ratio. Thus, it’s concluded that there exists
an optimal debt ratio between 48.92% and 49.55% that increases firm
value. These results are more consistent with the trade-off theory (Modigliani
and Miller, 1963; Myers, 1977, and Ross, 1977) for which firm may search
a “balance” that the interest tax shield is equal to the incremental
costs through debt financing. This suggests that financial managers
should use financial leverage wisely in order to maximize the firm’s
value and investors should refer to the debt ratio to make investment
decisions.
References
Aggarwal, R. and S. Zong (2006), “The cash flow–investment relationship: International evidence of limited access to external finance,”Journal of Multinational Financial Management, 16, 89–104.
Altman, E. I. (1984), “A Further Empirical Investigation of the Bankruptcy Cost Question,” The Journal of Finance, 39(4), 1067-1090.
Baker, M., Wurgler, J., 2002. Market timing and capital structure. The Journal of Finance 57, 1–32.
Castanias, R. (1983), “Bankruptcy Risk and Optimal Capital Structure,” The Journal of Finance, 38(5), 1617-1636.
Chan, K.S. (1993), “Consistency and Limiting Distribution of the Least Squares Estimator of a Continuous Threshold Autoregressive Model,” The Annals of Statistics, 21, 520-533.
Chung, K. H. and S. W. Pruitt (1994), “A Simple Approximation of Tobin’s q,” Financial Management, 23(3), 70-74.
Davies, R. B. (1977), “Hypothesis testing when a nuisance parameter is present only under the alternative,” Biometrika, 64, 247-254.
______ (1987), “Hypothesis testing when a nuisance parameter is present only under the alternative,” Biometrika, 74, 33-43.
Flannery, Mark Jand Kasturi P. Rangan (2006), “ Partial adjustment toward target capital structures,” Journal of Financial Economics 79 (2006) 469–506.
Frank,M.Z. and V.K. Goyal(2004), “The effect of market conditions on capital structure adjustment,” Finance Research Letters, 1, 47–55.
Hansen, B. E. (1996), “Inference when a nuisance parameter is not identified under the null hypothesis,” Econometrica, 64, 413-430.
______ (1999), “Threshold effects in non-dynamic panels: Estimation, testing and inference,” Journal of Econometrics, 93, 345-368.
______ (2000), “Sample splitting and threshold estimation,” Econometrica, 68, 575-603.
Jensen, M.C. and W. H. Meckling(1976), “Theory of the firm: Managerial Behavior, agency cost and ownership structure,” Journal of Financial Economics,3, 305-360.
Kim, W. S. and E. H. Sorensen (1986), “Evidence on the Impact of the Agency Costs of Debt on Corporate Debt Policy,” Journal of Financial and Quantitative Analysis, 21( 2), 131-145.
Miller, M. H. (1977), “Debt and Taxes,” Journal of Finance, 32, 261-275.
Modigliani, F. and M. H. Miller (1958), “The cost of capital, corporate finance, and the theory of investment,” American Economic Review, 48, 261-297.
______ (1963), “Corporate income taxes and the cost of capital: A correction,” American Economics Review, 53, 433-443.
Myers, S. C. (1977), “The Determinants of Corporate Borrowing,” Journal of Financial Economics, 5, 147-175.
______ (1984), “The Capital Structure Puzzle,” Journal of Finance, 39, 575-592.
Myers, S. C. and N. S. Majluf (1984), “Corporate Financing and Investment Decisions When Firms Have Information That Investors Do Not Have,” Journal of Financial Economics, 13(2), 187-222.
Ross, S. A. (1977), “The Determination of Financial Structure: the Incentive Signaling Approach,” Bell Journal of Economics and Management Science, 8(1), 23-40.
Stiglitz, J. E., ‘Some aspects of the pure theory of corporate finance: bankruptcies and takeovers’,Bell Journal of Economics and Management Science, Vol. 3, 1972, pp. 458–482.
Tong, H. (1978), “On a Threshold Model, in C.H. Chen (ed.), Pattern Recognition and Signal Processing, Amsterdam: Sijthoff & Noordhoff, 101-141.
Welch, I., 2004. Capital structure and stock returns. Journal of Political Economy 112, 106–131.
Table 1 Distribution of Sample
| Industry | Number of the sample | Proportion of the samples |
| Cement | 7 | 2.57% |
| Food | 13 | 4.78% |
| Plastics | 16 | 5.88% |
| Textiles | 34 | 12.50% |
| Electric, Machinery | 14 | 5.15% |
| Appliance, Cable | 10 | 3.68% |
| Chemical | 18 | 6.62% |
| Class, Ceramics | 6 | 2.21% |
| Paper, Pulp | 7 | 2.57% |
| Steel, Iron | 18 | 6.62% |
| Rubber | 7 | 2.57% |
| Automobile | 2 | 0.74% |
| Electron | 55 | 20.22% |
| Construction | 22 | 8.09% |
| Transportation | 10 | 3.68% |
| Tourism | 5 | 1.84% |
| Department Stores | 10 | 3.68% |
| Other | 18 | 6.62% |
| Total | 272 | 100.00% |
Table 2 Descriptive Statistics of Variables for 272 firms
| Variables | Statistic | 86年 | 87年 | 88年 | 89年 | 90年 | 91年 | 92年 | 93年 | 94年 | Total |
| Tobin's Q | Average | 1.54298 | 1.17255 | 1.11566 | 0.55607 | 0.63109 | 0.61516 | 0.70696 | 0.67504 | 0.64153 | 0.85078 |
| Maximum | 7.6233 | 6.11479 | 7.90927 | 3.32177 | 5.50582 | 3.5158 | 3.00755 | 2.26771 | 4.97011 | 7.90927 | |
| Minimum | 0.18843 | -0.0525 | -0.2744 | -0.4317 | -0.5449 | -0.5525 | -0.0101 | -0.0381 | -0.1715 | -0.5525 | |
| Std. Dev. | 1.00619 | 0.83048 | 1.18608 | 0.53085 | 0.64544 | 0.44246 | 0.45399 | 0.40252 | 0.51866 | 0.78629 | |
| Debt Ratio | Average | 39.0678 | 39.3167 | 40.9019 | 42.7617 | 43.2783 | 43.9908 | 43.1507 | 43.3181 | 42.0372 | 41.9804 |
| Maximum | 104.04 | 108.04 | 94.2 | 97.26 | 97.5 | 98.54 | 96.35 | 98.88 | 112.61 | 112.61 | |
| Minimum | 3.2 | 4.95 | 7.22 | 6.55 | 7.88 | 3.54 | 2.5 | 2.08 | 1.55 | 1.55 | |
| Std. Dev. | 15.2508 | 16.0174 | 15.6339 | 15.6559 | 16.0538 | 17.1044 | 17.255 | 17.9874 | 18.9035 | 16.7519 | |
| Management Ownership | Average | 0.50015 | 0.63585 | 0.46926 | 0.35371 | 0.38261 | 0.35665 | 0.65 | 0.63324 | 0.63048 | 0.51244 |
| Maximum | 12.44 | 12.33 | 10.03 | 8.03 | 11.63 | 10.54 | 10.07 | 8.32 | 9.98 | 12.44 | |
| Minimum | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
| Std. Dev. | 1.40523 | 1.70767 | 1.31003 | 1.10363 | 1.30874 | 1.24282 | 1.42398 | 1.2976 | 1.38261 | 1.36553 | |
| Size | Average | 15.7921 | 15.9226 | 15.9988 | 16.0634 | 16.0542 | 16.0533 | 16.0696 | 16.0887 | 16.0827 | 16.0139 |
| Maximum | 19.1188 | 19.1307 | 19.1679 | 19.6473 | 19.6318 | 19.7291 | 19.798 | 20.0049 | 20.0451 | 20.0451 | |
| Minimum | 13.4427 | 13.3823 | 13.4416 | 13.4858 | 13.45 | 13.4177 | 13.4347 | 13.4582 | 12.6558 | 12.6558 | |
| Std. Dev. | 1.01901 | 1.01821 | 1.04626 | 1.10105 | 1.11246 | 1.14413 | 1.17143 | 1.19506 | 1.2493 | 1.12199 | |
| R&D Expenses | Average | 1.29923 | 1.30871 | 1.2286 | 1.22956 | 1.64923 | 1.56868 | 1.36846 | 1.19493 | 1.27246 | 1.34665 |
| Maximum | 14.67 | 16.05 | 14.89 | 15.42 | 54.59 | 44.67 | 15.6 | 14.61 | 18.87 | 54.59 | |
| Minimum | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
| Std. Dev. | 2.42469 | 2.43708 | 2.1355 | 2.1964 | 4.46087 | 3.7292 | 2.52359 | 2.07807 | 2.37747 | 2.81497 | |
| Advertising
Expenses |
Average | 0.74241 | 0.8227 | 0.86541 | 0.71578 | 0.64923 | 0.63743 | 0.57062 | 0.57062 | 0.57062 | 0.68276 |
| Maximum | 15.0779 | 25.8538 | 23.2652 | 15.9404 | 8.16344 | 9.4443 | 9.56762 | 9.56762 | 9.56762 | 25.8538 | |
| Minimum | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
| Std. Dev. | 1.72723 | 2.3386 | 2.22231 | 1.7309 | 1.46739 | 1.50514 | 1.39547 | 1.39547 | 1.39547 | 1.72108 |
| Table 3 Tests for threshold effects between the debt ratio and Tobin’s Q | ||||||||||||
| Single threshold effect test | Double threshold effect test | Triple threshold effect test | ||||||||||
| Threshold -value | 49.55 | Threshold -value | 48.93 49.55 | Threshold -value | 27.97 48.93 49.55 | |||||||
| F | 5.2743447 | F | 27.732463 | F | 6.6421943 | |||||||
| p-value | 0.896 | p-value | 0.004*** | p-value | 0.78 | |||||||
| Critical Value of F | Critical Value of F | Critical Value of F | ||||||||||
| 1% | 5% | 10% | 1% | 5% | 10% | 1% | 5% | 10% | ||||
| 30.37 | 20.182157 | 16.49 | 22.26 | 18.164921 | 15.69 | 50.66 | 37.46094 | 28.97 | ||||
| notes: 1. F Statistic and P-value result from repeating bootstrap procedure 1000 times for each of the three bootstrap tests. | ||||||||||||
| 2. ***, **, and *, represent the significant at 1%, 5%, and 10% levels, respectively. | ||||||||||||
Table 4 Estimated Coefficients for Tobin’s Q
| Coefficient estimate | OLS se | White se | |||
| -0.00109 | 0.001852 | -0.59039 | 0.001682 | -0.65011 | |
| 0.008882 | 0.002475 | 3.5894** | 0.004744 | 1.872264 | |
| -0.00193 | 0.001406 | -1.37215 | 0.001294 | -1.49187 | |
| notes: 1. , and are the coefficient estimates for regimes of , and . | |||||
Table5 Estimation of Coefficients of Control Variables
| Coefficient estimate | OLS se | White se | |||
| 0.010792 | 0.011857 | 0.9102 | 0.009048 | 1.1928 | |
| -0.79509 | 0.040601 | -19.5829*** | 0.070456 | -11.2850 | |
| -0.01457 | 0.008076 | -1.8036* | 0.008625 | -1.6887 | |
| -0.01105 | 0.012376 | -0.8927 | 0.010309 | -1.0717 | |
| notes:
1.、、及represent
the estimated coefficients: management ownership ratio, natural log
of total assets, ratio of R&D expenses to total sales, and ratio
of advertising expenses to total assets.
2. ***, **, and *, represent the significant at 1%, 5%, and 10% levels, respectively. |
|||||
Single Threshold
Double
Threshold
1 Spurious regression is argued in Granger and Newbold (1974) that the estimation of the relationship among non-stationary series is easily getting higher R2 and t statistics.
2 LLC is a modified version of the LL (Levin and Lin, 1992, 1993) panel unit root technique.
3 For saving space, the results are not reported here. However, it will be available upon reader’s request.
4 “Davies’ Problem” is one that testing statistics follow non-standard distribution because of the existence of the nuisance (Davies, 1977,1987). Afterwards, Andrews and Ploberger (1994) and Hansen(1996) tested again to solve the problem.
5 Note that the statistic (17) is testing a different hypothesis from the statistic (15) introduced in the previous section. is testing while is testing .
6 Refer to Hansen (1999) Appendix: Assumptions 1-8.
All Rights Reserved Powered by Free Document Search and Download
Copyright © 2011