(Luận văn) capital structure and corporate performance, evidence in vietnam

INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS t to UNIVERSITY OF ECONOMICS ng hi ep w n VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN lo ad DEVELOPMENT ECONOMICS ju y th yi pl al n ua CAPITAL STRUCTURE AND CORPORATE PERFORMANCE: n va EVIDENCE IN VIETNAM ll fu oi m at nh A thesis submitted in partial fulfillment of requirements for the degree of z MASTER OF ARTS IN DEVELOPMENT ECONOMICS z ht vb k Academic Supervisor n a Lu Dr CAO HÀO THI om l.c gm PHẠM THỊ THÚY DIỄM jm By n va y te re HO CHI MINH CITY, NOVEMBER 2013 i t to ng CERTIFICATION hi ep “I certify that the substance of this thesis has not already been submitted for any degree and has not been currently submitted for any other degree w n I certify that to the best of my knowledge and help received in preparing this thesis and all sources used have acknowledged in this thesis” lo ad ju y th yi PHAM THI THUY DIEM pl n ua al Date: … November 2013 n va ll fu oi m at nh z z k jm ht vb om l.c gm n a Lu n va y te re th ii t to ng ACKNOWLEGMENTS hi ep Foremost, I would like to thank so much to Vietnam – The Netherlands programme for Master of Art in Development Economics (MDE programme), I have been studying useful w n knowledge Besides, I thank so much for all lecturers because of their valuable lo ad contributions as well as all my friends because of their helps during period of studying y th I would like to thank deeply to my academic supervisor, Doctor Cao Hao Thi for his ju yi enthusiastic supports, advices and great encouragements during my completion of the pl thesis ua al n Last but not least, I am deeply grateful to my family, especially my mother who supports in va n my life as well as lovely thank to my husband Nguyen Phuc Loc and his family fu ll One time again, I am grateful to all of you Thank you so much! oi m at nh z z k jm ht vb om l.c gm n a Lu n va y te re th iii t to ng ABSTRACT hi ep This article aims to examine the influence of capital structure on corporate performance and the reverse causality from corporate performance to capital structure, using data from w n 150 Vietnamese listed manufacturing firms from 2008 to 2012 Comparing the results of lo ad random effects model (REM) and fixed effects model (FEM), the more appropriate model y th will be discussed some empirical results The study found that the capital structure has ju significant and positive relationship with corporate performance in associated with debt to yi pl assets (TDTA) and short-term debt to assets (STDTA) In contrast, corporate performance al ua is insignificantly influenced by debt to assets (TDTA) The results also state that there is no n existence of optimal capital structure decision The reverse causality from corporate va n performance to capital structure, corporate performance has a significant and positive fu ll influence on capital structure in related with debt to assets (TDTA) and short-term debt to m oi assets (STDTA) but corporate performance has no meaning with long-term debt to assets at nh (LTDTA) z Keywords: capital structure, leverage, corporate performance z k jm ht vb om l.c gm n a Lu n va y te re th iv t to ng TABLE OF CONTENTS hi ep CERTIFICATION i ACKNOWLEGMENTS ii w n lo ABSTRACT iii ad LIST OF FIGURES vii y th ju LIST OF TABLES viii yi pl ABBREVIATIONS ix al n ua Chapter 1: INTROCDUCTION Problem statement 1.2 Research objective 1.3 Research questions 1.4 Research scope and data 1.5 Thesis structure n va 1.1 ll fu oi m at nh z z vb 2.1 jm ht Chapter 2: LITERATURE REVIEW Conceptual issues k gm Capital structure 2.1.2 Corporate performance om 2.2 l.c 2.1.1 Theoretical Literature a Lu Theories of capital structure and corporate performance 2.2.2 Theories of reverse causality from corporate performance to capital structure n va 2.3.1 The impacts of capital structure on corporate performance 13 th Empirical Literature 13 y te re 12 2.3 n 2.2.1 v t to ng 2.3.2 hi ep 2.4 The reverse causality from corporate performance to capital structure 18 Conceptual framework 21 Chapter 3: RESEARCH METHODOLOGY 25 w n Research process 25 lo 3.1 ad 3.2 Measurement of variables 27 y th Capital structure variable 27 ju 3.2.1 yi Corporate performance variable 28 3.1.3 Control variables for firm characteristics 29 pl 3.1.2 n ua al Hypothesis development 34 3.4 Model specification 35 n va 3.3 ll fu m Capital structure and corporate performance 35 3.3.2 Reverse causality from corporate performance to capital structure 37 oi 3.4.1 at nh Estimation strategy 38 3.5 Data collection 39 z 3.4 z jm ht vb Chapter 4: EMPIRICAL ANALYSIS RESULTS 41 k gm Descriptive statistics 41 4.2 Empirical results 44 om l.c 4.1 Corporate performance and capital structure 44 4.2.2 Reverse causality from corporate performance to capital structure 50 n a Lu 4.2.1 va n Chapter 5: CONCLUSIONS 55 te re Conclusions 55 5.2 Limitations and suggestion of further research 56 y 5.1 th vi t to ng REFERENCE 58 hi ep APPENDIX 65 w n lo ad ju y th yi pl n ua al n va ll fu oi m at nh z z k jm ht vb om l.c gm n a Lu n va y te re th vii t to ng LIST OF FIGURES hi ep Figure 2.1: The trade-off of capital structure w Figure 2.2: Conceptual framework for the impacts of capital structure on corporate n lo performance 22 ad ju y th Figure 2.3: Conceptual framework for the reverse causality from corporate performance to capital structure 23 yi pl ua al Figure 3.1: Research process 27 n Figure 3.2: Analytical framework for the reverse causality from corporate performance to va n capital structure 34 ll fu oi m at nh z z k jm ht vb om l.c gm n a Lu n va y te re th viii t to ng LIST OF TABLES hi ep Table 2.1: A summary of the empirical results analyzing the relationship between capital structure and corporate performance 14 w n lo Table 2.2: A summary of the empirical results the reverse causality from corporate ad ju y th performance to capital structure 20 yi Table 3.1: Control variables used in some previous studies 32 pl ua al Table 3.2: Analytical framework for the impact of capital structure on corporate n performance 33 n va ll fu Table 3.3: Variable description and expected sign for Model 36 m oi Table 3.4: Variable description and expected sign for Model 37 nh at Table 3.5: Variable description and expected sign for Model 38 z z ht vb Table 4.1: Summary statistics of the explanatory variables, 2008-2012 42 k jm Table 4.2: Correlation matrix of the explanatory variables, during 2008-2012 43 gm Table 4.3: Results of Hausman test 44 l.c om Table 4.4: Choice between fixed effects model and random effects model 45 a Lu Table 4.5: Corporate performance and capital structure 47 n va Table 4.6: Corporate performance and optimal capital structure 49 n te re Table 4.7: Reverse causality from corporate performance to capital structure 53 y th ix t to ng ABBREVIATIONS hi ep EFF: Effectiveness w FEM: Fixed effects regression model n lo ad Growth: Sales growth y th ju HNX: Hanoi Stock Exchange yi pl HOSE: Ho Chi Minh Stock Exchange n ua al LEV: Leverage va n LTDTA: Long-term debt to total assets ll fu at z ROA: Return on total assets nh PROFIT: Profitability oi m M&M: Modigliani and Miller z k jm STDTA: Short-term debt to total assets n a Lu Tang: Tangibility om l.c gm Size: Firm size ht REM: Random effects regression model vb ROE: Return on total equity n va y te re th 87 t to ng Table 15: Hausman Test regression results for Model using LTDTA hi ep Coefficients w (b) (B) (b-B) | fixed random sqrt(diag(V_b-V_B)) n | lo Difference S.E ad -+ y th -.3502247 -.4726903 1224656 133661 ltdta2 | 1.100664 9288749 1717893 2697936 1.243442 -.5297023 071622 1703836 0506102 0622189 3820959 -.177925 0763129 -.0039485 0058406 001957 -.005319 0049225 ju ltdta | yi pl 7137402 size | 2209938 tang | 2041709 growth | -.0700093 -.0660608 d2 | 2153866 2134296 d3 | 0511312 0564502 d4 | -.1849743 -.1728673 d5 | -.1347445 -.1134737 n ua al profit | n va ll fu oi m nh 0098793 -.0212708 0106139 at -.012107 z z ht vb jm b = consistent under Ho and Ha; obtained from xtreg k B = inconsistent under Ha, efficient under Ho; obtained from xtreg difference in coefficients not systematic om l.c Ho: gm Test: 0.0000 y te re (V_b-V_B is not positive definite) n Prob>chi2 = va 64.45 n = a Lu chi2(10) = (b-B)'[(V_b-V_B)^(-1)](b-B) th 88 t to ng Table 16: Fixed effects regression results for Model using STDTA ep = 750 Group variable: firms Number of groups = 150 = 0.3634 Obs per group: = between = 0.2726 avg = 5.0 overall = 0.3012 max = F(10,149) = 26.33 Prob > F = 0.0000 Number of obs w hi Fixed-effects (within) regression n R-sq: within lo ad ju y th yi corr(u_i, Xb) = 0.0594 pl ua al (Std Err adjusted for 150 clusters in firms) n va | Robust n Coef Std Err t P>|t| [95% Conf Interval] ll fu tobinq | 0.72 0.471 -.3181946 6854157 3951308 1.05 0.297 -.3670585 1.194509 8308981 2340436 3.55 z 0.001 3684249 1.293371 size | 1873405 0612688 3.06 0.003 0662726 3084083 tang | 2212659 1247569 1.77 0.078 growth | -.0665166 0183261 -3.63 0.000 -.1027291 d2 | 2086531 028848 7.23 0.000 151649 d3 | 045795 0308684 1.48 0.140 -.0152014 l.c stdta2 | 4137253 profit | d4 | -.1897574 034382 -5.52 0.000 -.2576966 -.1218182 d5 | -.1407143 0318695 -4.42 0.000 -.2036888 -.0777399 _cons | -.4646552 3249452 -1.43 0.155 -1.106751 1774407 z 2539483 at 1836106 nh stdta | oi m -+ ht vb k jm -.0252553 4677871 -.0303041 gm 2656573 1067915 om rho | 64817213 y 22642959 te re sigma_e | n 30733586 va sigma_u | n a Lu -+ th (fraction of variance due to u_i) 89 t to ng Table 17: Random effects regression results for Model using STDTA hi ep Number of obs = 750 Group variable: firms Number of groups = 150 R-sq: = 0.3487 Obs per group: = between = 0.4589 avg = 5.0 max = Random-effects GLS regression w within n lo ad overall = 0.4030 y th Wald chi2(10) = 321.05 corr(u_i, X) Prob > chi2 = 0.0000 ju Random effects u_i ~ Gaussian yi = (assumed) pl ua al (Std Err adjusted for 150 clusters in firms) n Std Err z P>|z| [95% Conf Interval] ll fu Coef n tobinq | Robust va | stdta | -.2687951 2817783 oi m -+ stdta2 | 936997 4459997 2.10 profit | 1.330209 2566551 size | 1543503 tang | -0.95 -.8210704 2834801 0.036 0628536 1.81114 5.18 0.000 8271741 1.833243 0491529 3.14 0.002 0580124 2506882 3988796 1318494 3.03 0.002 growth | -.06589 019947 -3.30 d2 | 2100618 0287303 d3 | 0555706 d4 | at nh 0.340 z z 0.001 -.1049854 -.0267947 7.31 0.000 1537515 266372 0295486 1.88 0.060 -.0023436 1134849 -.1722134 0314531 -5.48 0.000 -.2338603 -.1105665 d5 | -.111461 0294521 -3.78 0.000 -.1691861 -.0537359 _cons | -.35088 2719341 -1.29 0.197 -.8838611 1821012 k jm 6572996 l.c ht vb 1404596 gm om 50501038 th rho | y 22642959 te re sigma_e | n 22871007 va sigma_u | n a Lu -+ (fraction of variance due to u_i) 90 t to ng Table 18: Hausman Test regression results for Model using STDTA hi ep Coefficients w n lo | (b) (B) (b-B) | fixed random sqrt(diag(V_b-V_B)) Difference S.E ad -+ -.1836106 ju 4137253 yi stdta2 | pl -.2687951 4524057 1486637 936997 -.5232717 2246719 1.330209 -.4993107 0758935 1543503 0329902 0625731 3988796 -.1776137 0778611 n y th stdta | -.0006265 0059765 -.0014086 -.0097756 0048953 8308981 size | 1873405 tang | 2212659 growth | -.0665166 d2 | 2086531 2100618 d3 | 045795 0555706 d4 | -.1897574 -.1722134 d5 | -.1407143 -.111461 n ua al profit | va -.06589 ll fu oi m nh -.017544 0099919 at 0109329 z -.0292534 z ht vb jm b = consistent under Ho and Ha; obtained from xtreg k B = inconsistent under Ha, efficient under Ho; obtained from xtreg difference in coefficients not systematic om l.c Ho: gm Test: 0.0000 y te re (V_b-V_B is not positive definite) n Prob>chi2 = va 65.43 n = a Lu chi2(10) = (b-B)'[(V_b-V_B)^(-1)](b-B) th 91 t to ng Table 19: Fixed effects regression results for Model using TDTA hi ep Number of obs = 750 Group variable: firms Number of groups = 150 = 0.1121 Obs per group: = between = 0.2447 avg = 5.0 max = F(9,149) = 4.40 Prob > F = 0.0000 Fixed-effects (within) regression w R-sq: within n lo ad overall = 0.2159 ju y th = 0.2351 yi corr(u_i, Xb) pl (Std Err adjusted for 150 clusters in firms) ua al n Coef n tdta | Robust va | Std Err t P>|t| [95% Conf Interval] fu ll -+ m 0581658 0250628 2.32 profit | -.384572 0769716 -5.00 size | 0581419 0330903 1.76 0.081 tang | 0821295 0621208 1.32 0.188 growth | -.0072377 0059546 -1.22 0.226 d2 | 0084173 0108699 0.77 d3 | 012401 0118892 d4 | 0213479 d5 | _cons | oi tobinq | 0086413 1076903 0.000 -.5366689 -.2324752 -.007245 1235287 -.0406221 2048812 at nh 0.022 z z ht vb 0045287 0.440 -.0130618 0298963 1.04 0.299 -.0110922 0358942 013992 1.53 0.129 -.0063004 0489962 0105544 0147515 0.72 0.475 -.0185948 0397036 -.1062856 1786161 -0.60 0.553 -.4592334 2466621 k jm -.0190041 om l.c gm rho | 81980078 (fraction of variance due to u_i) y 08315189 te re sigma_e | n 1773576 va sigma_u | n a Lu -+ th 92 t to hi ep Number of obs = 750 Group variable: firms Number of groups = 150 R-sq: within = 0.1097 Obs per group: = between = 0.2487 avg = 5.0 overall = 0.2247 max = Random-effects GLS regression w ng Table 20: Random effects regression results for Model using TDTA n lo Wald chi2(9) = 70.06 corr(u_i, X) Prob > chi2 = 0.0000 ad Random effects u_i ~ Gaussian ju y th = (assumed) (Std Err adjusted for 150 clusters in firms) yi al Coef Robust Std Err z ua tdta | pl | P>|z| [95% Conf Interval] n -+ -.049598 0238599 2.08 0.038 0028334 0963626 profit | -.4458289 0708319 -6.29 0.000 -.5846568 -.3070009 size | 090193 0199949 4.51 0.000 0510037 1293823 tang | 09531 0536155 1.78 0.075 -.0097744 2003944 growth | -.0105384 0055033 -1.91 oi 0.056 -.0213247 0002478 d2 | 0094228 0107235 0.88 0.380 -.0115949 0304404 d3 | 0096141 0111259 0.86 0.388 -.0121923 0314205 d4 | 0142123 0127494 1.11 0.265 -.0107761 0392007 d5 | 0023363 0137604 0.17 0.865 -.0246336 0293062 _cons | -.272847 111003 -2.46 0.014 -.4904089 -.055285 n va tobinq | ll fu m at nh z z ht vb sigma_e | 08315189 rho | 79585111 (fraction of variance due to u_i) gm 16417783 k sigma_u | jm -+ om l.c n a Lu n va y te re th 93 t to ng Table 21: Hausman Test regression results for Model using TDTA hi ep Coefficients w | (b) (B) (b-B) | fixed random sqrt(diag(V_b-V_B)) Difference S.E n lo -+ -.0581658 049598 0085677 0042379 profit | -.384572 -.4458289 0612568 0161329 size | 0581419 090193 -.0320511 0192693 09531 -.0131805 0197944 ad tobinq | ju y th tang | -.0105384 0033007 0019897 0084173 0094228 -.0010055 0008812 012401 al 0096141 0027869 0021668 d4 | 0213479 0142123 0071356 0036298 d5 | 0105544 0023363 0082181 0038133 d2 | d3 | n ua -.0072377 pl yi growth | 0821295 va n fu b = consistent under Ho and Ha; obtained from xtreg ll oi m B = inconsistent under Ha, efficient under Ho; obtained from xtreg Ho: difference in coefficients not systematic at nh Test: z chi2(9) = (b-B)'[(V_b-V_B)^(-1)](b-B) k jm ht (V_b-V_B is not positive definite) vb 19.57 0.0207 z = Prob>chi2 = om l.c gm n a Lu n va y te re th 94 t to ng Table 22: Fixed effects regression results for Model using LTDTA hi ep Number of obs = 750 Group variable: firms Number of groups = 150 = 0.0683 Obs per group: = between = 0.2475 avg = 5.0 overall = 0.2132 max = F(9,149) = 2.46 Prob > F = 0.0122 Fixed-effects (within) regression w n within lo R-sq: ad ju y th yi corr(u_i, Xb) = 0.2730 pl al ua (Std Err adjusted for 150 clusters in firms) n -Coef Std Err t n ltdta | Robust va | P>|t| [95% Conf Interval] fu -+ -0.585 -.0180738 0319224 profit | -.0804948 048179 -1.67 0.097 -.175697 0147075 size | -.010522 0202205 -0.52 0.604 -.0504781 029434 tang | 135421 0459832 2.95 0.004 0445576 2262844 growth | 0015488 0037341 0.41 0.679 -.0058299 0089275 d2 | 0050557 0073519 0.69 0.493 -.0094718 0195832 d3 | 0037309 0080975 0.46 0.646 -.0122699 0197317 d4 | -.0001265 0092894 -0.01 0.989 -.0184824 0182294 d5 | -.0091453 0096607 -0.95 0.345 -.0282351 0099444 _cons | 1021879 1137942 0.90 0.371 -.1226708 3270467 at 0126508 nh 0069243 m 0.55 oi ll tobinq | z z k jm ht vb gm -+ -sigma_e | 05405292 rho | 82377422 (fraction of variance due to u_i) om 1168661 l.c sigma_u | a Lu n n va y te re th 95 t to ng Table 23: Random effects regression results for Model using LTDTA hi ep Fixed-effects (within) regression Group variable: firms R-sq: Number of obs Number of groups w n lo within = 0.0683 between = 0.2475 overall = 0.2132 ad 750 150 Obs per group: = avg = max = 5.0 F(9,149) Prob > F = 0.2730 = = 2.46 0.0122 ju y th corr(u_i, Xb) = = yi (Std Err adjusted for 150 clusters in firms) -| Robust ltdta | Coef Std Err t P>|t| [95% Conf Interval] -+ -tobinq | 0069243 0126508 0.55 0.585 -.0180738 0319224 profit | -.0804948 048179 -1.67 0.097 -.175697 0147075 size | -.010522 0202205 -0.52 0.604 -.0504781 029434 tang | 135421 0459832 2.95 0.004 0445576 2262844 growth | 0015488 0037341 0.41 0.679 -.0058299 0089275 d2 | 0050557 0073519 0.69 0.493 -.0094718 0195832 d3 | 0037309 0080975 0.46 0.646 -.0122699 0197317 d4 | -.0001265 0092894 -0.01 0.989 -.0184824 0182294 d5 | -.0091453 0096607 -0.95 0.345 -.0282351 0099444 _cons | 1021879 1137942 0.90 0.371 -.1226708 3270467 -+ -sigma_u | 1168661 sigma_e | 05405292 rho | 82377422 (fraction of variance due to u_i) pl n ua al n va ll fu oi m at nh z z k jm ht vb om l.c gm n a Lu n va y te re th 96 t to ng Table 24: Hausman Test regression results for Model using LTDTA hi ep Coefficients -| (b) (B) (b-B) sqrt(diag(V_b-V_B)) | fixed random Difference S.E -+ -tobinq | 0069243 0024942 0044301 0025483 profit | -.0804948 -.1150664 0345716 0094763 size | -.010522 0215776 -.0320996 0128004 tang | 135421 1889785 -.0535576 0131103 growth | 0015488 -.0018252 003374 0011376 d2 | 0050557 0051198 -.0000641 d3 | 0037309 0010462 0026847 0010062 d4 | -.0001265 -.0060439 0059174 0021727 d5 | -.0091453 -.0160333 006888 0023029 -b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg w n lo ad ju y th yi pl n ua al difference in coefficients not systematic n Ho: va Test: fu ll chi2(9) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 56.66 Prob>chi2 = 0.0000 (V_b-V_B is not positive definite) oi m at nh z z k jm ht vb om l.c gm n a Lu n va y te re th 97 t to ng Table 25: Fixed effects regression results for Model using STDTA hi ep Number of obs = 750 Group variable: firms Number of groups = 150 = 0.1292 Obs per group: = between = 0.1579 avg = 5.0 max = F(9,149) = 4.83 Prob > F = 0.0000 Fixed-effects (within) regression w n R-sq: within lo ad overall = 0.1534 ju y th = 0.0385 yi corr(u_i, Xb) pl al (Std Err adjusted for 150 clusters in firms) Coef va stdta | Robust n | ua -Std Err t P>|t| [95% Conf Interval] n fu -+ -.0364125 0147661 profit | -.3151726 0708984 ll tobinq | 2.47 0072344 0655906 -4.45 0.000 -.4552688 -.1750764 0.015 015128 1405921 0.408 -.1390383 0567449 -.0215325 0005282 size | 07786 0317468 oi m 0.015 tang | -.0411467 0495399 -0.83 growth | -.0105021 0055821 -1.88 d2 | 0069936 0072376 0.97 0.335 -.007308 0212952 d3 | 0102892 008842 1.16 0.246 -.0071827 0277611 d4 | 021626 0108061 2.00 0.047 d5 | 0222641 0115825 1.92 0.056 -.0006232 0451514 _cons | -.2487591 1726867 -1.44 0.152 -.5899903 092472 2.45 at nh 0.062 z z k jm ht vb 000273 042979 gm -+ -sigma_e | 06851223 rho | 80988665 (fraction of variance due to u_i) om 14140798 l.c sigma_u | n a Lu n va y te re th 98 t to ng Table 26: Random effects regression results for Model using STDTA hi ep Random-effects GLS regression Group variable: firms R-sq: Number of obs Number of groups w n lo within = 0.1270 between = 0.1798 overall = 0.1711 ad 750 150 Obs per group: = avg = max = 5.0 Wald chi2(9) Prob > chi2 = = 64.22 0.0000 ju y th Random effects u_i ~ Gaussian corr(u_i, X) = (assumed) = = yi (Std Err adjusted for 150 clusters in firms) -| Robust stdta | Coef Std Err z P>|z| [95% Conf Interval] -+ -tobinq | 0319247 0141527 2.26 0.024 0041858 0596635 profit | -.3407963 0653682 -5.21 0.000 -.4689156 -.212677 size | 0779003 0165655 4.70 0.000 0454326 110368 tang | -.0767093 0400126 -1.92 0.055 -.1551326 001714 growth | -.0104628 0055507 -1.88 0.059 -.021342 0004164 d2 | 0080509 0072958 1.10 0.270 -.0062485 0223503 d3 | 0102731 0083161 1.24 0.217 -.0060262 0265724 d4 | 0204109 0099614 2.05 0.040 0008869 039935 d5 | 0209372 0110505 1.89 0.058 -.0007213 0425957 _cons | -.2314657 0897023 -2.58 0.010 -.407279 -.0556524 -+ -sigma_u | 13548674 sigma_e | 06851223 rho | 79636411 (fraction of variance due to u_i) pl n ua al n va ll fu oi m at nh z z k jm ht vb om l.c gm n a Lu n va y te re th 99 t to ng Table 27: Hausman Test regression results for Model using STDTA hi ep Coefficients w | (b) (B) | fixed (b-B) random sqrt(diag(V_b-V_B)) Difference S.E n 0364125 0319247 0044878 0036976 profit | -.3151726 -.3407963 0256237 0141135 size | 07786 0779003 -.0000403 0159312 -.0411467 -.0767093 0355626 0165657 -.0105021 ad tobinq | pl lo -+ ju y th tang | -.0000393 0017378 0069936 0080509 -.0010573 0011436 d3 | 0102892 0102731 0000161 0019785 d4 | 021626 0204109 0012151 0031217 d5 | 0222641 0209372 0013269 0032699 n ua -.0104628 d2 | al yi growth | va n fu b = consistent under Ho and Ha; obtained from xtreg ll oi m B = inconsistent under Ha, efficient under Ho; obtained from xtreg Ho: difference in coefficients not systematic at nh Test: z chi2(9) = (b-B)'[(V_b-V_B)^(-1)](b-B) not positive definite) k jm is ht (V_b-V_B vb 9.15 0.4233 z = Prob>chi2 = om l.c gm n a Lu n va y te re th 100 t to ng Table 28: Testing for multicollinearity in Model using TDTA hi vif ep Variable | VIF 1/VIF w -+ n lo 1.52 0.659167 profit | 1.38 0.724435 size | 1.10 0.906883 tang | 1.07 0.933661 growth | 1.04 0.959992 ad tobinq | ju y th yi -+ pl Mean VIF | 1.22 ua al n Table 29: Testing for multicollinearity in Model using LTDTA va vif n 1/VIF ll VIF fu Variable | 0.724435 1.10 0.906883 tang | 1.07 0.933661 growth | 1.04 0.959992 1.22 jm Mean VIF | ht -+ vb 1.38 size | z profit | z 0.659167 at 1.52 nh tobinq | oi m -+ k Table 30: Testing for multicollinearity in Model using STDTA VIF 1/VIF -+ -0.724435 size | 1.10 0.906883 tang | 1.07 0.933661 growth | 1.04 0.959992 -+ th 1.22 y Mean VIF | te re 1.38 n profit | va 0.659167 n 1.52 a Lu tobinq | om Variable | l.c gm vif 101 t to ng Table 31: Testing for multicollinearity in Model using TDTA hi vif ep Variable | VIF 1/VIF w -+ -1.31 0.760756 size | 1.22 0.822286 profit | 1.15 0.871605 tang | 1.04 0.962063 growth | 1.03 0.968062 n tdta | lo ad ju y th yi -+ pl Mean VIF | 1.15 al ua Table 35: Testing for multicollinearity in Model using LTDTA n vif VIF n va Variable | 1/VIF fu ll -+ -0.692028 tang | 1.35 0.740657 0.929888 growth | 1.03 0.968131 jm ht 1.20 vb Mean VIF | z -+ z 0.917131 1.08 at 1.09 nh size | profit | oi 1.45 m ltdta | Table 36: Testing for multicollinearity in Model using STDTA k vif VIF 1/VIF 1.09 0.918742 tang | 1.05 0.955206 growth | 1.03 0.969294 Mean VIF | 1.11 y te re -+ n profit | va 0.861729 n 0.814546 1.16 a Lu 1.23 size | om stdta | l.c -+ gm Variable | th