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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.�˶��ټ�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. 
       
       

       

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      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�� 87�� 88�� 89�� 90�� 91�� 92�� 93�� 94�� 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.������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.

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