Determinants of Capital Structure: Comparison of Empirical Evidence from the Use of Different Estimators

Transcription

1 Serrasqueiro and Nunes, Inernaional Journal of Applied Economics, 5(1), Deerminans of Capial Srucure: Comparison of Empirical Evidence from he Use of Differen Esimaors Zélia Serrasqueiro * and Paulo Maçãs Nunes ** Universidade da Beira Inerior and CEFAGE, Porugal Absrac In his aricle we exend he comparaive analysis beween he resuls of a pooled OLS regression and he use of fixed effecs panel models concerning he deerminans of deb, comparing he resuls of using saic panel models and dynamic panel esimaors, including he dynamic esimaor of correcion of fixed effecs. The resuls show ha he differences beween he resuls of saic panel model evaluaions and hose of dynamic esimaors are no significan, and so he resuls of his sudy are no dependen on he ype of esimaors used. The mos profiable Poruguese companies resor less o deb, his resul suggesing ha Poruguese companies follow a hierarchical order concerning heir sources of finance, preferring inernal capial o exernal capial. Larger Poruguese companies resor more o deb. Keywords: Deb, dynamic panel esimaors, saic panel models JEL Classificaion: C23, G32 1. Inroducion Bevan and Danbol (2004) es he inconsisency of he deerminans of capial srucure in Briish companies, comparing he resuls of a pooled OLS regression wih he resuls of applicaion of panel models considering fixed non-observable individual effecs. The auhors, using as deerminans of various ypes of deb according o is mauriy and origin, size, level of securiy, profiabiliy and opporuniies for growh, conclude ha he resuls of a pooled OLS regression validae heories abou capial srucure, whereas he resuls of panel models considering fixed non-observable individual effecs do no validae hese same heories. The auhors conclude ha he conclusions of sudies of he deerminans of capial srucure, backed up by pooled OLS regressions, are biased by no conrolling he specific effecs of companies which are no measured by he relaionship beween deb and is deerminans. As Scherr and Hulbur (2001) sae, i is fundamenal o ry o undersand he dynamism of company capial srucure, given he need o carry ou permanen adjusmens as a consequence of he need o renegoiae he level and condiions of deb. The advance of economeric echniques allowed invesigaors o use dynamic panel esimaors in empirical sudies of he deerminans of company capial srucure. The sudies by Kremp e al.

2 Serrasqueiro and Nunes, Inernaional Journal of Applied Economics, 5(1), (1999), Shyam-Sunder and Myers (1999), Miguel and Pindado (2001), Ozkan (2001) and Gaud e al. (2005), use dynamic panel esimaors raher han saic panel models. The aims of his sudy are: 1) o exend he comparison carried ou by Bevan and Danbol (2004) o he applicaion of dynamic panel esimaors, comparing he resuls of saic and dynamic panel models, verifies he possible differences in he resuls obained and; 2) draw conclusions abou he deerminans of capial srucure of lised Poruguese companies. As deb deerminans of lised Poruguese companies, we use he deerminans presened regularly in he lieraure: nondeb ax shields; profiabiliy; size; angibiliy; level of risk; and finally, growh opporuniies. We use he FINBOLSA daabase concerning lised Poruguese companies. We seleced 39 companies in he period 1998 o Given he number of cross-secions, and consequenly observaions, applicaion of he Bruno (2005) esimaor is jusified so as o es he robusness of he resuls obained from applying dynamic esimaors, since as Bruno (2005) concludes, he number of insrumens generaed by dynamic panel esimaors, given he reduced number of observaions, can lead o disorion of he esimaed parameers. We divide he aricle, afer his inroducion, as follows: in secion 2, we presen he daabase and mehodology; in secion 3, we presen he resuls obained; in secion 4, we discuss he resuls obained; and finally, in secion 5, we draw conclusions from his sudy. 2. Daabase and Mehodology 2.1. Daabase We use he Finibolsa daabase covering Poruguese companies quoed on he Sock Exchange. The Poruguese capial marke is no ye very developed, and so he number of companies is raher small. From he oal number of companies we ook ou 3 financial companies and 2 fooball clubs, leaving a oal of 39 companies wih he necessary available daa, in he period from 1998 o Since no all companies were par of he Poruguese share marke in 1998, he panel obained was no uniform. We use he informaion considered relevan aken from published resuls and company balance shees Mehodology Saic Panel Models The mos commonly used ways of assessing he relaionship beween deb and is deerminans, considering saic panel models, are: 1) a pooled OLS regression; 2) panel model of random effecs; 3) panel model of fixed effecs. Considering he previously defined deerminans of deb used in his sudy, he evaluaion of an pooled OLS regression can be presened in he following way:

3 Serrasqueiro and Nunes, Inernaional Journal of Applied Economics, 5(1), β GROWT = β + β NDTS 1 + d + e, + β PROF 2 + β SIZE 3 + β TANG 4 + β EVOL 5 + (1) where i represens each of he companies, represens he period of ime, is he level of deb (raio beween Toal Liabiliies and Toal Asses), NDTS are nondeb ax shields (raio beween Depreciaions and Toal Asses), PROF is profiabiliy (raio beween Operaing Income and Toal Asses), SIZE is size (Logarihm of Toal Sales), TANG is asse srucure (raio beween Fixed Asses and Toal Asses), EVOL is he level of risk (absolue value of percenage change of Operaing Income), GROWT are growh opporuniies (Growh of Toal Sales), d are emporal dummy variables ha measure he impac of possible macroeconomic aleraions on company deb, and is he error which is assumed o have a normal disribuion. e i, Using an pooled OLS regression, companies non-observable individual effecs are no conrolled, and so, as Bevan and Danbol (2004) conclude, heerogeneiy, a consequence of no considering hose effecs, can influence measuremens of he esimaed parameers. Using panel models of random or fixed effecs, i is possible o conrol he implicaions of companies non-observable individual effecs on he esimaed parameers. Considering he exisence of non-observable individual effecs, we have: 6 0 β GROWT = β + β NDTS 1 + d + u, + β PROF 2 + β SIZE 3 + β TANG 4 + β EVOL 5 + (2) where u = vi + e, wih v i being companies non-observable individual effecs. The difference beween an pooled OLS regression and a model considering non-observable individual effecs lies precisely in. v i To es he relevance of non-observable individual effecs we use he LM es. This ess he null hypohesis of non-relevance of non-observable individual effecs, agains he alernaive hypohesis of relevance of non-observable individual effecs. No rejecing he null hypohesis, we can conclude ha non-observable individual effecs are no relevan, and so a pooled OLS regression is an appropriae way of carrying ou evaluaion of deb deerminans. On he conrary, if we rejec he null hypohesis ha non-observable individual effecs are no relevan, we can conclude ha a pooled OLS regression is no he mos appropriae way of carrying ou analysis of he relaionship beween deb and is deerminans. However, here may be correlaion beween companies non-observable individual effecs and deb deerminans. If here is no correlaion beween companies non-observable individual effecs and deb deerminans, he mos appropriae way of carrying ou evaluaion is by using a panel model of random effecs. If here is correlaion beween companies individual effecs and

4 Serrasqueiro and Nunes, Inernaional Journal of Applied Economics, 5(1), deb deerminans, he mos appropriae way of carrying ou evaluaion is using a panel model admiing he exisence of fixed effecs. To es for he possible exisence of correlaion we use he Hausman es. This ess he null hypohesis of non-exisence of correlaion beween nonobservable individual effecs and he explanaory variables, in his sudy, deb deerminans, agains he null hypohesis of exisence of correlaion. By no rejecing he null hypohesis, we can conclude ha correlaion is no relevan, a panel model of random effecs being he mos correc way of carrying ou evaluaion of he relaionship beween deb and is deerminans. On he oher hand, by rejecing he null hypohesis, we conclude ha correlaion is relevan, and so he mos appropriae way o carry ou evaluaion of he relaionship beween deb and is deerminans is by using a panel model of fixed effecs. In his sudy we also presen he evaluaion of he mos appropriae panel model, according o he resuls of he LM and Hausman ess, consisen wih he exisence of firs order auocorrelaion Dynamic Panel Esimaors As was already menioned, saic panel models do no allow us o analyse he possible dynamism exising in company decisions when choosing heir capial srucure. Nex we presen he dynamic panel esimaors, and heir paricular relevance, compared o saic models, in he sudy of choice of company capial srucure. Besides he advanages menioned earlier, concerning eliminaion of companies non-observable individual effecs, of greaer conrol of endogeny, use of dynamic panel esimaors also has he advanage of allowing us o deermine he level of adjusmen of acual deb owards opimal level of deb. We can describe ha adjusmen process as follows: = α * ), (3) 1 ( 1 where is he acual deb of company i in period, 1 is he acual deb of company i in period -1 and, * is he opimal deb of company i in period. Regrouping he erms and solving o he order of, we have: = * + (1 α) 1 α. (4) If α = 1, we have = *, he acual level of deb being equal o he opimal level of deb. In hese circumsances, companies manage o find an opimal capial srucure. On he conrary, if α = 0, we have = 1, he acual level of deb in he curren period being equal o he level of deb in he previous period, he adjusmen of he level of acual deb owards he opimal level of deb being nil. We can conclude ha high values of α, mean a close proximiy of he level of acual deb o opimal level of deb. On he conrary, low values of α, mean less proximiy beween he acual level of deb and opimal level of deb.

5 Serrasqueiro and Nunes, Inernaional Journal of Applied Economics, 5(1), As saed by Kremp e al. (1999), Shyam-Sunder and Myers (1999), Miguel and Pindado (2001), Ozkan (2001) and Gaud e al. (2005), opimal level of deb depends on companies specific characerisics ha are on he deerminans considered relevan in explaining deb. Therefore, he opimal level of deb is given by: 6 * = λ + λ NDTS 0 + λ GROWT 1 + d i + v + e + λ PROF. 2 + λ SIZE 3 + λ TANG 4 + λ EVOL 5 + (5) Subsiuing (5) in (4), and solving o he order of, we have: 5 β EVOL = β 0 + δ 1 + β GROWT 6 + β NDTS 1 + θ + η + ε i + β PROF 2, + β SIZE 3 + β TANG 4 + (6) where δ = ( 1 α), β 0 = αλ0, β 1 = αλ1, β 2 = αλ2, β 3 = αλ3, β 4 = αλ4, θ = αd, η i = αvi, ε =. αe Evaluaing equaion (6) using saic panel models, admiing or no correlaion beween nonobservable individual effecs and deb deerminans, we obain biased and inconsisen evaluaions of he evaluaed parameers, since as well as here being correlaion beween η i and 1, here is also correlaion beween ε and 1. The correlaion of non-observable individual effecs and he error wih he lagged deb has he consequence of bias and inconsisency of he esimaed parameers. Arellano and Bond (1991) propose evaluaion of he equaion (6) wih he variables in firs differences, and he use of deb lags and is deerminans a level as insrumens. Evaluaion of he equaion (6) in firs differences allows us o eliminae non-observable individual effecs, eliminaing in his way he correlaion beween η i and 1. The use of lags of he deb and is deerminans as insrumens allows for he creaion of orhogonal condiions beween ε i and 1, eliminaing he correlaion. However Blundell and Bond (1998) conclude ha when he dependen variable is persisen, here being a high correlaion beween is values in he curren period and in he previous period, and he number of periods is no very high, he GMM (1991) esimaor is inefficien, he insrumens used generally being weak. In hese circumsances, Blundell and Bond (1998) exend he GMM (1991) esimaor, considering a sysem wih variables a level and firs differences. For he variables a level in equaion (6) he insrumens are he variables lagged in firs differences. In he case of he variables in firs differences in equaion (6) he insrumens are hose lagged variables a level. However he GMM (1991) and GMM sysem (1998) dynamic esimaors can only be considered robus on confirmaion of wo condiions: 1) if he resricions creaed, a consequence of using he insrumens, are valid; and 2) here is no second order auocorrelaion.

6 Serrasqueiro and Nunes, Inernaional Journal of Applied Economics, 5(1), To es he validiy of he resricions we use he Sargan es in he case of he GMM (1991) esimaor and he Hansen es in he case of he GMM sysem (1998) esimaor. In boh cases, he null hypohesis indicaes he resricions imposed by use of he insrumens are valid, indicaing he alernaive hypohesis ha he resricions are no valid. By rejecing he null hypohesis, we conclude ha he esimaors are no robus. We es for he exisence of firs and second order auocorrelaion. The null hypohesis is ha here is no auocorrelaion, he alernaive hypohesis being he exisence of auocorrelaion. By rejecing he null hypohesis of non-exisence of second order auocorrelaion, we conclude ha he esimaors are no robus. Bruno (2005) concludes ha in siuaions where he number of cross-secions is no very high, and consequenly nor is he number of observaions, he use of dynamic esimaors, given he number of insrumens generaed, can lead o bias of he esimaed parameers. Given he number of observaions, we presen he Bruno (2005) dynamic esimaor, LSDVC (Leas Squares Dummy Variable Correced). wih regression of convergence of fixed effecs correced, so as o es he robusness of he resuls obained from applying he dynamic esimaors. 3. Resuls The resuls of he descripive saisics of he variables and corresponding correlaion marix are presened in appendix, ables A1 and A2 respecively. We find ha only he risk and growh opporuniies variables show a somewha volaile behaviour. According o Aivazian e al. (2005), when correlaion coefficiens beween explanaory variables are no more han 30%, he problem of collineariy is no paricularly relevan. Observing he correlaion coefficiens beween he explanaory variables, Table A2 in appendix, we see ha only he correlaion coefficiens beween risk and profiabiliy and beween risk and size are more han 30%. However, heir values are no considerably above 30%, and so he problem of collineariy may no be paricularly relevan beween explanaory variables. We calculaed he correlaion coefficien beween deb in he curren period and deb in he previous period, obaining a correlaion coefficien of The value of he correlaion coefficien is quie significan, and so we can conclude ha he deb series is persisen, he GMM sysem (1998) dynamic esimaor being possibly more efficien han he GMM (1991) dynamic esimaor. Nex we presen he resuls of he saic panel model evaluaions. From analysis of he resuls of he Wald and F ess, we can conclude ha we canno rejec he null hypohesis ha he explanaory variables do no explain, aken as a whole, he explained variable, and so he deerminans seleced in his sudy can be considered explanaory of he deb. The resuls of he LM es indicae we can rejec he null hypohesis, a 1% significance, ha companies non-observable individual effecs are no significan. Therefore, a pooled OLS

7 Serrasqueiro and Nunes, Inernaional Journal of Applied Economics, 5(1), regression will no be he mos appropriae way of carrying ou evaluaion of he relaionship beween deb and is deerminans, since we do no consider he heerogeneiy of companies, a heerogeneiy which canno be measured by he relaionship beween deb and is deerminans. The fac of he deerminaion coefficien increasing subsanially when we evaluae panel models wih random or fixed effecs shows he relevance of non-observable individual effecs in explaining deb. The resuls of he Hausman es show ha we canno rejec he null hypohesis of absence of correlaion beween companies non-observable individual effecs and explanaory variables, ha is o say, deb deerminans. Therefore, we can conclude ha he mos appropriae way o carry ou evaluaion of he relaionship beween deb and is deerminans is evaluaion of a random effecs panel model. The similariy of he resuls obained, using random or fixed effecs, shows ha he correlaion beween non-observable individual effecs and deb deerminans is no relevan. Therefore, and given he possible exisence of auocorrelaion, we assess he random effecs panel model, consisen wih he exisence of firs order auocorrelaion. Nex we presen he resuls of he GMM (1991) and GMM sysem (1998) dynamic esimaors. The resuls appear in he following ables. The resuls of he Wald and F ess, as in he case of he saic panel models, le us conclude ha he deerminans used in his sudy can be considered, as a whole, explanaory of he deb. From he resuls of he Sargan and Hansen ess, we can conclude ha we canno rejec he null hypohesis of insrumen validiy, and consequen resricions generaed, from use of he GMM (1991) and GMM sysem (1998) dynamic esimaors respecively. The resuls of he second order auocorrelaion ess concerning respecively he GMM (1991) and GMM sysem (1998) dynamic esimaors, allow us o conclude ha we canno rejec he null hypohesis of absence of second order auocorrelaion. Given he validiy of he insrumens and absence of second order auocorrelaion, we can conclude ha he GMM (1991) and GMM sysem (1998) dynamic esimaors are efficien and robus. Given ha in his sudy he number of cross-secions is no very high, and consequenly, nor is he number of observaions, we presen he resuls of he Bruno (2005) dynamic esimaor, a correced dynamic esimaor of fixed effecs. The resuls appear in he following able. We presen he Bruno (2005) dynamic esimaor for cases of correcing he GMM (1991) and GMM sysem (1998) dynamic esimaors. The resuls are similar, and so use of he Bruno (2005) esimaor allows greaer convergence of he resuls of he GMM (1991) and GMM sysem (1998) dynamic esimaors. 4. Discussion of he Resuls Nex we presen he comparison of he resuls of he models previously presened. Firs we compare he resuls of he saic panel models, hen we compare he resuls of he dynamic panel esimaors, and finally we go on o compare he resuls of he saic and dynamic panel

8 Serrasqueiro and Nunes, Inernaional Journal of Applied Economics, 5(1), esimaors. The summary of he resuls of he signs and saisical significance concerning he relaionship beween deb and is deerminans are presened in he following able Comparison of he Resuls of Saic Panel Models Bevan and Danbol (2004) concluded ha he differences beween he resuls of an pooled OLS regression and he evaluaion of a fixed effecs panel model were especially significan in he relaionship beween angibiliy and deb, and beween size and deb. Concerning he relaionships beween profiabiliy and deb, and beween growh opporuniies and deb, he auhors conclude ha he differences beween he resuls of an pooled OLS regression and he resuls of a fixed effecs panel model are no significan. Observing he resuls presened in Table 4, we can conclude he resuls of a pooled OLS regression are no subsanially differen from hose obained using panel models of random and fixed effecs. The only significan difference concerns he relaionship beween nondeb ax shields and deb. Whereas using a pooled OLS regression he esimaed parameer is negaive, bu no significan saisically, considering a panel model wih random or fixed effecs, he relaionship becomes posiive and saisically significan. As for he impac of asse composiion on deb, using an pooled OLS regression he esimaed parameer is posiive, whereas using panel models wih random or fixed effecs, he esimaed parameer is negaive. However, his difference is no significan, since in neiher case can we consider he relaionship beween angibiliy and deb as saisically significan. Considering he relevance of non-observable individual effecs, given by he resuls of he LM es, we can draw he following conclusions from he resuls of he saic panel models: 1) here is a posiive and saisically significan relaionship beween nondeb ax shields and deb; 2) here is a negaive and saisically significan relaionship beween profiabiliy and deb; 3) here is a posiive and saisically significan relaionship beween size and deb; and 4) saisically, he relaionships beween angibiliy and deb, risk and deb, and growh opporuniies and deb are no significan Comparison of he Resuls of he Dynamic Panel Esimaors Comparing he resuls of he dynamic esimaors GMM (1991) and GMM sysem (1998), we find ha he esimaed resuls do no vary grealy, alhough here are some differences in he resuls obained. Using he GMM (1991) dynamic esimaor, we obain an impac of deb in he previous period on deb in he curren period which is saisically significan a 5% significance, of δ = , and so wih δ = ( 1 α), we have an adjusmen of acual deb owards opimal level of deb of α = Applying he GMM sysem (1998) dynamic esimaor he impac of deb in he previous period on deb in he curren period is saisically significan a 1% significance, of δ = 0.414, and so adjusmen of acual deb owards opimal level of deb is α = When we apply he Bruno (2005) dynamic esimaor, he impac of deb in he previous period on deb in he curren period is δ = when we consider correcion of he GMM (1991)

9 Serrasqueiro and Nunes, Inernaional Journal of Applied Economics, 5(1), dynamic esimaor and δ = when we consider correcion of he GMM sysem (1998) dynamic esimaor, and so adjusmens of acual deb owards opimal level of deb are respecively α = and α = The parameer measuring he impac of nondeb ax shields on deb is posiive using boh GMM (1991) and GMM sysem (1998) dynamic esimaors. However, using he GMM (1998) dynamic esimaor, he esimaed parameer is no saisically significan. When we use he Bruno (2005) dynamic esimaor, we find ha in boh cases, correcion of he GMM (1991) and GMM sysem (1998) dynamic esimaors, here is a posiive and saisically significan relaionship. As for he relaionships beween profiabiliy and deb, and beween size and deb, we find, whichever esimaor we use, he exisence of relaionships ha are saisically significan, and negaive and posiive respecively, resuls which are mainained when we apply he Bruno (2005) correcion dynamic esimaor. The relaionships beween angibiliy and deb, level of risk and deb, and growh opporuniies and deb, are no saisically significan. From applicaion of he Bruno (2005) dynamic esimaor we can corroborae hese conclusions. Based on he resuls of he dynamic panel esimaors, we can draw he following conclusions: 1) companies adjus he level of acual deb owards he opimal level of deb, he adjusmen being around 33%, 34 %, considering he LSDVC (2005) dynamic esimaor. However, he adjusmen is no subsanial; 2) here is a posiive and saisically significan relaionship beween nondeb ax shields and deb; 3) here is a negaive and saisically significan relaionship beween profiabiliy and deb; 4) here is a posiive and saisically significan relaionship beween size and deb; and 5) he relaionships beween angibiliy and deb, risk and deb, and growh opporuniies and deb are no saisically significan. 5. Conclusions Given he relevance of companies non-observable individual effecs, an pooled OLS regression is no he mos appropriae way o carry ou evaluaion beween deb and is deerminans. This being so, and given he absence of correlaion beween non-observable individual effecs and deb deerminans, we consider he panel models of random or fixed effecs equally suiable. Given he persisence of he deb series, we advise in hese circumsances applicaion of he GMM sysem (1998) dynamic esimaor raher han he GMM (1991) dynamic esimaor. However, given he low number of cross-secions, and he consequenly low number of observaions, we will consider he Bruno (2005) dynamic esimaor for correcion of he esimaed parameers, as a reference for applicaion of he dynamic panel esimaors. The resuls obained by he wo esimaors are idenical concerning he signs and level of significance of he saisically significan parameers. Comparing he resuls of he random effecs panel model and he Bruno (2005) correcion dynamic esimaor we find ha in boh cases: 1) here is a posiive relaionship which is saisically significan a 1% significance,

10 Serrasqueiro and Nunes, Inernaional Journal of Applied Economics, 5(1), beween nondeb ax shields and deb; 2) here is a negaive relaionship, saisically significan a 1% significance, beween profiabiliy and deb; 3) here is a posiive relaionship, saisically significan a 1% significance, beween size and deb; 4) he relaionships beween angibiliy and deb, beween level of risk and deb, and beween growh opporuniies and deb, are no saisically significan. The Poruguese companies adjus he level of acual deb owards he opimal level of deb. The adjusmen is no very pronounced, beween 0.33 and 0.34, when compared wih adjusmens of oher counry companies. Kremp e al. (1999) obain values for adjusmen coefficiens of 0.53 and 0.28 for Germany and France respecively, Shyam-Sunder and Myers (1999) 0.59 for he Unied Saes, Miguel and Pindado (2001) 0.79 for Spain, Ozkan (2001) 0.57 for he Unied Kingdom and Gaud e. al.(2005) values beween 0.14 and 0.387, according o he ype of deb used, for Swizerland. We find a posiive and saisically significan relaionship beween nondeb ax shields and deb, conrary o he expeced negaive relaionship, and so we can conclude ha Poruguese companies do no reduce deb, given he greaer possibiliy of nondeb ax shields. We confirm he exisence of a negaive relaionship beween profiabiliy and deb. This resul suggess he mos profiable Poruguese companies resor less o deb, oping firs for inernal financing. Given he posiive and saisically significan relaionship beween size and deb, we conclude ha larger companies urn more o deb han smaller companies, since he former have access o beer condiions and credi faciliies, given less informaion asymmery and less likelihood of bankrupcy. We do no find a saisically significan relaionship beween asse srucure and deb, level of risk and deb, and beween growh opporuniies and deb. Based on he resuls obained, we canno conclude ha a greaer level of collaerals conribues o companies increasing deb, ha a higher level of risk conribues o decreasing deb or ha companies use deb o finance heir growh. To sum up, we can conclude ha Poruguese companies adjus he acual level of deb owards he opimal level of deb, alhough he level of adjusmen is no subsanial. Size and profiabiliy can be considered deerminan facors in explaining he capial srucure of Poruguese companies, and consequenly in explaining he adjusmen owards opimal level of deb. Larger companies urn more o deb while he mos profiable companies urn less o deb. Endnoes * Zélia Serrasqueiro, Universidade da Beira Inerior, Esrada do Sineiro, Erneso Cruz, Covilhã, Porugal; Telephone: ; Fax: ; Invesigadora do CEFAGE (Cenro de Esudos e Formação Avançada em Gesão e Economia), Universidade de Évora; zelia@ubi.p

11 Serrasqueiro and Nunes, Inernaional Journal of Applied Economics, 5(1), ** Paulo Maçãs Nunes (Corresponding auhor), Universidade da Beira Inerior, Esrada do Sineiro, Erneso Cruz, Covilhã, Porugal; Telephone: ;Fax: ; Invesigador do CEFAGE (Cenro de Esudos e Formação Avançada em Gesão e Economia), Universidade de Évora; macas@ubi.p References Aivazian V., Y. Ge, and J. Qiu The Impac of Leverage on Firm Invesmen: Canadian Evidence, Journeal of Corporae Finance, 11, Arellano, M. and S. Bond Some Tess of Specificaion For Panel Daa: Mone Carlo Evidence and an Applicaion o Employmen Equaions, Review of Economic Sudies, 58, Bevan, A. and J. Danbol Tesing for Inconsisencies in he Esimaion of UK Capial Srucure Deerminans, Applied Financial Economics, 14, Blundell, M. and S. Bond Iniial Condiions and Momen Resricions in Dynamic Panel Daa Models, Journal of Economerics, 87, Bruno, G Approximaing he Bias of LSDV Esimaion he Bias of LSDV Esimaor for Dynamic Unbalanced Panel Daa Models, Economic Leers, 87, Gaud, P., E. Jan M. Hoesl and A. Bender The Capial Srucure of Swiss Companies: An Empirical Analysis Using Dynamic Panel Daa, European Financial Managemen, 11, Kremp, E., E. Söss and D. Gerdesmeier Esimaion of a Deb Funcion: Evidence form French and German Firm Panel Daa, in A. Sauvé and M. Scheuer(eds), Corporae Finance in Germany and France (Frankfur-am-Main and Paris:Deusche Bundesbank and Banque de France. Miguel, A. and J. Pindado Deerminans of Capial Srucure: New Evidence from Spanish Panel Daa, Journal of Corporae Finance, 7, Ozkan, A Deerminans of Capial Srucure and Adjusmen o Long Run Targe: Evidence from UK Company Panel Daa, Journal of Business Finance & Accouning, 28, Scherr, F. and H. Hulbur The Deb Mauriy Srucure of Small Firms, Financial Managemen, 30, Shyam-Sunder, L. and S. Myers Tesing Saic Trade-Off agains Pecking Order Models of Capial Srucure, Journal of Financial Economics, 51,

12 Serrasqueiro and Nunes, Inernaional Journal of Applied Economics, 5(1), Appendix Table A1. Descripive Saisics Variable Observaions Mean Sandard Mínimum Maximum Deviaion, i NDTS PROF SIZE TANG EVOL GROWT Table A2. Correlaion Marix NDTS PROF SIZE TANG EVOL GROWT 1 NDTS PROF ** 0.206*** 1 SIZE 0.293*** *** 1 TANG *** *** 1 1 EVOL ** *** *** 0.124* 1 GROWT *** 1 Noes: 1. *** indicaes significance a he 1% level, ** indicaes significance a he 5% level, and * indicaes significance a he 10% level.

13 Serrasqueiro and Nunes, Inernaional Journal of Applied Economics, 5(1), Table 1. Saic Panel Models Dependen Variable:, i Independen Variables OLS Random Effecs Fixed Effecs Random Effecs AR(1) NDTS * ** *** (03638) (0.3493) (0.3817) (0.4088) PROF i, *** *** *** *** (0.1701) (0.1382) (0.1485) (0.1668) SIZE *** *** *** *** (0.0065) (0.0099) (0.0148) (0.0117) TANG (0.0499) (0.0736) (0.1008) (0.1040) EVOL (0.0166) (0.0107) (0.0110) (0.0099) GROWT (0.0233) (0.0144) (0.0147) (0.0212) CONS (0.1259) (0.1984) (0.3009) (0.1253) Observaions LM (χ 2 ) *** Hausman (χ 2 ) 4.42 R Wald (χ 2 ) 46.11*** F(0,1) 7.11*** 7.18*** 56.12*** Noes: 1. The LM es has χ 2 disribuion and ess he null hypohesis ha non-observable individual effecs are no relevan in explaining he dependen variable, agains he alernaive hypohesis of relevance of non-observable individual effecs in explaining he dependen variable. 2. The Hausman es has χ 2 disribuion and ess he null hypohesis ha non-observable individual effecs are no correlaed wih he explanaory variables, agains he null hypohesis of correlaion beween non-observable individual effecs and he explanaory variables. 3. The Wald es has χ 2 disribuion and ess he null hypohesis of non-significance as a whole of he parameers of he explanaory variables, agains he alernaive hypohesis of significance as a whole of he parameers of he explanaory variables. 4. The F es has normal disribuion N(0,1) and ess he null hypohesis of non-significance as a whole of he esimaed parameers, agains he alernaive hypohesis of significance as a whole of he esimaed parameers. 5. Sandard deviaions in brackes. 6. *** significan a 1% significance; ** significan a 5% significance; * significan a 10% significance. 7. The esimaes include consan.

14 Serrasqueiro and Nunes, Inernaional Journal of Applied Economics, 5(1), Table 2. Dynamic Models GMM(1991) and GMM sysem (1998) Dependen Variable: Independen Variables GMM(1991) GMM sysem (1998) ** *** 1 (0.1981) (0.1326) NDTS ** (0.5655) (0.5816) PROF i, *** *** (0.2278) (0.3371) SIZE *** ** (0.0132) (0.0226) TANG (0.1030) (0.1285) EVOL (0.0199) (0.0335) GROWT (0.0199) (0.0369) CONS (0.0054) (0.3971) Insrumens GMM GMM sysem Observaions Wald (χ 2 ) F 8.29*** Sargan (χ 2 ) Hansen m1(n(0,1)) -1.97** -2.05** m2 (N(0,1)) Noes: 1. In he GMM (1991) esimaor he insrumens used are (, ), in which are he deb deerminans lagged wo periods. 2. In he GMM sysem (1998) esimaor he insrumens used are (, ) in he firs difference equaions, and ( i, 2 n n 2 Z k, 2 Δ 1 K = 1 K = 1 n Z k, 2 Z k, 2 K = 1 Z k, 1, ) in he levels equaions. 3. The Wald es has χ 2 disribuion and ess he null hypohesis of overall non-significance of he parameers of he explanaory variables, agains he alernaive hypohesis of overall significance of he parameers of he explanaory variables. 4. The Sargan es has χ 2 disribuion and ess he null hypohesis of significance of he validiy of he insrumens used, agains he alernaive hypohesis of non-validiy of he insrumens used. 5. The m1 es has normal disribuion N(0,1) and ess he null hypohesis of absence of firs order auocorrelaion, agains he alernaive hypohesis of exisence of firs order auocorrelaion. 6. The m2 es has normal disribuion N(0,1) and ess he null hypohesis of absence of second order auocorrelaion agains he alernaive hypohesis of exisence of second order auocorrelaion. 7. Sandard deviaions in brackes. 8. *** significan a 1% significance; ** significan a 5% significance; * significan a 10% significance. Δ

15 Serrasqueiro and Nunes, Inernaional Journal of Applied Economics, 5(1), Table 3. LSDVC Esimaor: Regression of Convergence - Correcion FE-GMM (1991) and GMM sysem (1998) Dependen Variable: Independen Variables LSDVC (2005) Iniial GMM(1991) LSDVC (2005) Iniial GMM sysem (1998) *** *** 1 (0.0943) (0.0906) NDTS *** *** (0.4428) (0.4389) PROF i, *** *** (0.1354) (0.1286) SIZE *** *** (0.0163) (0.0158) TANG (0.1003) (0.0101) EVOL (0.0105) (0.0108) GROWT (0.0252) (0.0260) Observaions Noes: 1. Sandard Deviaions in brackes. 2. *** significan a 1% significance; ** significan a 5% significance; * significan a 10% significance.

16 Serrasqueiro and Nunes, Inernaional Journal of Applied Economics, 5(1), Table 4. Summary of he Resuls of Applying he Various Esimaors: Empirical Evidence for Porugal Expeced OLS Random Fixed Random GMM GMM LSDVC LSDVC Sign Effecs Effecs Effecs (1991) sysem (2005) (2005) AR(1) (1998) Iniial GMM (1991) Iniial GMM (1998) ** +*** +*** +*** NDTS - -(n.s.) +* +** +*** +** +(n.s.) +*** +*** PROF - -*** -*** -*** -*** -*** -*** -*** -*** SIZE + +*** +*** +*** +*** +*** +** +*** +*** TANG + +(n.s.) -(n.s.) -(n.s.) -(n.s.) -(n.s.) -(n.s.) -(n.s.) -(n.s.) EVOL - -(n.s.) -(n.s.) -(n.s.) -(n.s.) -(n.s.) -(n.s.) -(n.s.) -(n.s.) + -(n.s.) -(n.s.) -(n.s.) +(n.s.) +(n.s.) -(n.s.) -(n.s.) -(n.s.) GROWT, i Noes: 1. n.s. no significan. 2. *** significan a 1% significance. 3. ** significan a 5% significance. 4. * significan a 10% significance