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M-+ 8-.@2H9 %F)E +G ME 8."@" ?I. #%V. 789 , 2ll " 5{2n9 P 7_0)E 5?X)A " (0-_n)A .""%-i %-N_9 -AU@P %F-I0+ 72-T. 7)Fs@ P %Fi]+ #AU@P 5%F)E #<Fd@ #8_+ Mr" #-AM.@ 6«"2C XTCP ME ?R O@ %]+» 61@ gZ@ %+ A,.+ %]E%)F9 HT.+ UV. , 7 .C@" 8_A@24 ,X $%&' #@%+ #%FI0+ e]0f.@ (@%+_+ P e"2). !" ]+ M-r2 "2-9 e@2-)A "%-TK)d %-+ %}-9 6-9@2d O@ %-f" HT ?@2_dM+ ]0. 0T=9 F4Z t2. 8-C+ %z-1 "-3FG@ " M-="<9 M_]A ei%A ME Z@ ?R %f.0+ 8OP2E 2*I9 #2v .e"2+ (0SSW9 M-="<9 M-_]A ?P8-+ #-0." H-;G@P #-0." -9@ 9.8-_EH-). %F9 @ #"3FG@ H@E 0T=9 F4Z " 5(-@ "2-rP -+ 10."2-CH-9 "%TK)d /Pz jd+ 0T=9 F4Z %00x 5/21 (@ " .X0. 1. Hellmann (2000), Besanko and Thakor (1993), Boot and Greenbaum (1993) and Matutes and Vives (2000) 2. Concentration Stability 3. Allen and Gale (2000) 4. Boyd and Gianni (2005) 5. Caminal and Matutes (2002) 6. Too Big to Fail (1999) (0TI09 .7 8. Coase 10. Cooter and Ulen (2000) (1960) OP2E .9 49 56"71 2,34"1 /"0 '()*( +,"%-.( +" !"#$%& 124 HT ?@2_d M+ %FI0+ HF=P" 0T=9 ."@8. "2rP #8s@P M%V. 5"%TK)d %+ 0T=9 t2. \S. "29 "]-. " P-+ (-@ 6-0=" (%-[-*9 1."2-CH-9 H-SK -Aef_+ J0;c "%TK)d P H@E. 69@2d O@ 5(-@ "2-rP -+ 2.-Z@ H-F=P" #-Ae-f_+ " "%TK)d "2<*+ #@%+ H!E e]0f.@ ?@8S! 5?@%fIAPk' ?R 8-_.9 P H=9 m+_9 M+ HZ%FZ" P /@O0F9@ 5=P" )s 8_.9 H)*9 #@]9 O@ HF=P" #Af_+ M-E H;9@2-r " %-f" #2-Z O@ ."2-C ^r29 @ ef_+ "%TK)d "2<*+ 8.@2H9 (@ ME 8.@"24%+ P (0-;9 #-Ae"@2.-4 ,K) " H1234 H=9 /XZ}9 58C+ #8r HKTI9 H2r.@ P "X! " UO #A"@8.F-Z@ P -Z@ -A,-.+ (-@ " m-C #@MKvX9 H2C72' 5e"2+ HZ0Z #AeP%i (09- #@%-+ HT-X D 9-E #-AU@P P e8-I. -d H-1234 #A,.+ BZ2 AU@P 4@"%' 8-1"81 ,-.+ ,- 5P (-@ O@ 3."2-CH-9 4@"%' B<%9 #AeP%i P bLC@ H=9 #AO0. 8-_.9 H-;=>9 M-E -Z@ 6-0=" (0)A M+ ."@8. #%F)E U@P ?O 9P]= e8CH1234 H1234 -0T=9 ?"2-+ H-1234 58-IL+H-9 "2-<*+ @ "%TK)d Mn.R ME 8.@e"%E jW+ 4%E' P "@P609 5 .Z@ HF+G 0;G29 " (FC@" @%G MTK+ X0. ef_+ .-Z@HT.-+ U-V. " :2;9 /<=>9 [Ns ?"2+ + 6" O@ %f" HT ]0. J0;c %89 5?i8-.%0iU@P #%f=-+%Q #@%-+ [-E /-*9 6T-C " 8-.@2 H9 HT.+ /XZ}9 " 7O. H@E .8-C+ /-XZ}9 (-@ " ?i8-.%0iU@P /-0K)d %-+ J0;-c /-V. P -A M-S0P ^-Z_9. H+O@ -+ M-*r@29 P U@P ?-O #@%-+ #%F-I0+ "@8;F-Z@ O@ (0-' H-0K)d H@-E + H=9 #A "*. 5(@%+_+ .8.@"24%+ #<Fd@ ,X -0=;! g0S9 \@]!@ u%` O@ ef_+ , BZ2F9 M_]A \AE M+ ME g0S9 #AM!%1 "2rP {-' .8-_E H-!%;9 @ A,.+ " U@P ?O e8__E(00; 69@2d O@ %f" HT 8.@2H9 58_EH9 eC@ .8-A" Y0-c2 @ :2-;9 /-<=>9 ?-Z2. O@ H-IL+ 8-.@2H-9 M-E -Z@ #%f" %0xF9 ,.+ eO@8.@ 6 P%-)KG P g-0S9 M-+ p2-+%9 #-AM!%-1O@ #@"2-4%+ ^<-Z M-+ %Fi]+ 0=;! g0S9 + A ,.+ .8_C+ MFC@" "@%!@ ,X O@ #%u0G" H+O@ 8_.@2H9 )Fs@ 1. Alchian (1965) (1995 P 1974) ]F0KfFZ@ .2 (2004) 20A .3 4. Millward and Parker (1983) 5. Louzis, et al 6. Economies of Scope 125 '()*( +6(MN4"E OA#> 6, ?@A! ;"BC"D! )E )FG! H!(@I JKKBL (0-+ e-C@ "2-9 p-<@ Y0-c2 M-+ #0-X+ H+%N /;=>9 e8C MFzi js<9 2ll " -+ HT.-+ #-AU-V. F4-Z P -A,-.+ /0-1234 (0-_l [A P ?DE "3FG@ #A%0xF9 P B@%C .[0_EH9 P%9 @ /;=>9 (@ (%[*9 H4%+ M9@"@ " .8.@MF4@"%' HT.+ :2;9 /<=>9 M-w@@ -T%9R HT.+ UV. " AU@P ?O 69@2d HZ%+ e+" #@M0=P@ M;=>9 1{29 P ?2F0E ?I. :2;9 /<=>9 =4 M+ p2+%9 ?O O@ e"zFZ@ + AU@P ?O M0=P@ #%0ieO@8.@ + ?I@ .8."@" #AU@P ?O " /Pz 5b4 #A\L+ J0;c "%TK)d + e@%)A HK4@" "3FG@ B@%C ME 8."@" O2-_A M-E 8-C e8AI9 69@2d (@ %@ 7%F_E O@ {' HFs .8_A"H9 Y0c2 @ A,.+ (0+ e8C < /-0K)d ,-X /P-z M-+ 5e8.)0G+ /Pz (@ .Z@ /PzF9 JKFL9 #A,.+ (0+ AU@P ?O H-Z%+ M-+ #%-f" /-;=>9 (1987) {2-9 P ?2F0E M=S9 7<." M+ ."2CH9 e"@" <X. A,.+ P HT_0-Z 5M-.2). #@%+ .8.@MF4@"%' e8WF9 /@ " :2;9 #AU@P P AU@P ?O e8__E(00; 69@2d @%-G H-Z%+ "2-9 H-+%N M-.2iM-+ @ e8-WF9 /@ " #N #A,.+ U@P ?O 2=@P (%i #-A,.+ O@ HAe"@" #@%+ H)Ff= H>4 ?20Z%i 789 , (0)L M+ M;=>9 (@ " .8.@e"@" HT.-+ b4 69@2d ME 8."@" ?I. A?R .8.@MF4@"%' 1987 1984 7Z O@ T%9R #N ]+ [0-XS 6-1s /2-1 M-+ M-E) -A,-.+ U@P ?-O %-. 8-_.@2H9 #"3FG@ ?DE #A4C P J-%; =-4 M-_]A P U@P 6-E m-)r6-1s %-+ :2-;9 /-<=>9 ePDd M+ U@P =4 #AM_]A %"S9 5e%*+ #A %. 8_.9 H.P" 69@2d P U@P ?O %. (0+ <9 M>+@ .8_A" Y0c2 @ ("2CH9 -=@P (%-i P HT_0-Z (0_n)A .Z@ M;=>9 (@ #AMF! MK)r O@ M9%Z zE P HF4@"%' U@P %-. %-+ %}9 69@2d MK)r O@ ]0. HK4@" "3FG@ #"2E B@%C ME 8.80Z MN0F. (@ M+ ]0. (1991) .Z@ #N #A,.+ ?O P M-_]A H@-E P :2-;9 /-<=>9 (0-+ -0Kd M>+@ "24 [*9 M=S9 " (1997) . P %i%+ P e"2-). H-Z%+ 1985-1994 eP" " T%9R #N #A,.+ O@ #@M.2). #@%+ @ ,.+ M9%Z "2-rP :2-;9 /<=>9 P M_]A H@E (0+ Hz_9 M>+@ 6TC M+ #@M!%`P" 0Kd ME ME 8."@" ?I. O@ e8-WF9 /-@ " U@P #-A72-T. %-+ /-<Fd@ 8-C %-@ HZ%+ 2V_9 M+ (1999) ?2F0E ."@" /2-1 M-+ U@P 72-T. M-;=>9 (-@ " ."%-+H9 E M+ @ VAR 789 , 1996 1982 #A7Z ."2-CH-9 J-%; 5MF!%f. uK; ?R M+ #@e%*+ 8.@MFC&i 80Z%Z O@ OP 90 O@ \0+ ME HAU@P 1. Keeton and Morris (1987) 2. Sinkey and Greenwalt (1991) 49 56"71 2,34"1 /"0 '()*( +,"%-.( +" !"#$%& 126 U@P %F-I0+ ?O M+ #<Fd@ (0' #A"@8.FZ@ + u+>9 /@<Fd@ m%Z 8C ME 8A"H9 ?I. ?2F0E .Z@ e8C %N_9 e8WF9 /@ O@ H14 #A\L+ " U-N.@ /-;=>9 -+ H*+I9 yF. 8.@e"@" @%G HZ%+ "29 @ %f" H=9 #AUV. ME H;=>9 #@8-T.+ U-V. HfFXTCP 1?@T)A P ~E%+ M.2). #@%+ .8_A"H9 Mw@@ e8WF9 /@ " e8C M-E 8-."@" ?-I. -A?R .8-.@e"@" @%-G H-Z%+ "2-9 1993-1996 H.9O eP" 72` " @ (0F.@oR M-+ ?i8_-X2. .8_F-XA "3FG@ ?DE Y>Z 69@2d P HT.+ b4 69@2d %0 W :2;9 /<=>9 2-V_9 M-+ 3_-Z P g-Z .8-.%+H-9 E M+ @ 2S+ 60KW $P 569@2d (@ %0 ,0Tz 2V_9 #-2' 789 , O@ 0.|Z@ #N P O@8.@{' #A,.+ " @"6TI9 #AU@P HK1@ 69@2d HZ%+ 8-C 5GDP 8-C M-E 8_A"H9 ?I. A?R .8."%E e"zFZ@ 1985-1997 H.9O eP" 72` " 6.' M-+ ,-.+ "%-TK)d P -0T=9 F4-Z 5@O-+ /8-G 5M9%Z <X. 5,.+ eO@8.@ 5/@<Fd@ m%Z P ?-r@ .8-_A" Y0c2 @ :2;9 /<=>9 " /@%00x 8_.@2H9 %89 0z0E O@ H34C ?@2_d "-3FG@ 2-K>9 B@%C ME 8_A"H9 ?I. 6.' ?20Z%i 60KW P M]N , #%0iEM+ + 47A" B@%-C P M-_]A 580-Z%Z 8-_.9 H=-9 69@2d P ("2CH9 MF!%i eO@8.@ GDP 8C u%` O@) ?DE " #-N #-A,-.+ :2;9 /<=>9 %+ #@"_;9 %0 #<Fd@ #%0i *r P ,.+ eO@8.@ 5<Fd@ 50000 69-C 2-IE 119 O@ ]-+ M-.2). , O@ e"zFZ@ + (2004) ?@T)A P 2T09 .8.@" 8_A [Ns P HF=P" 0T=9 (0+ H)0SFX9 M>+@ 8."@" ?I. 50T=9 /PzF9 #AF4Z + H=9 MXZ}9 7-Z O@ 0.|Z@ #@8T.+ \L+ HZ%+ M+ 5_Z P ]_)0r 5(@ %+ ?P]!@ ."@" "2rP :2;9 /<=>9 %F-I0+ 8-C ^<-Z M-+ :2-;9 /-<=>9 88-I %-+ H-_<9 #8A@2C A?R .8.O@"%'H9 2003 1984 ?I. (0_n)A M;=>9 (@ .8_A"H9 Mw@@ ?ZR #<Fd@ B@%C P HS0Ss e%*+ #+ #A %. 5GDP ^-%! @ ,-.+ ?@%8-9 -Z@ (T)9 8Hi8_). 6TI9 P 7#@MKi F! 56H_0+,"]. ME 8A"H9 O@ e"zF-Z@ -+ 9,-!2! .8-__E -4@"%' eO@8-.@ O@ \0+ #AU@P 5u.P ?@P" " ME #@M.2iM+ 5e"@" 1. Bercoff, et al (2002) 2. Survival Analysis 3. Salas and Saurina (2002) 4. Meanwhile, Rajan and Dhal (2003) 5. Jimenez and Saurina (2005) 6. Myopia 7. Herd Behaviour 8. Agency Problems 9. Fofack (2005) 127 '()*( +6(MN4"E OA#> 6, ?@A! ;"BC"D! )E )FG! H!(@I JKKBL e%-*+ %. 51H;G@P O@ %. 5GDP 8C ME 8A"H9 ?I. HS%!R 2IE 8_l #@%+ 6.' 789 , (-@ " :2-;9 /-<=>9 HK-1@ 6-9@2d M-K)r O@ 2HT.-+ (0+ #AU@P P =4 "2Z M0Cs 5HS0Ss #-A,-.+ 0T=9 F4Z P :2;9 /<=>9 (0+ B+@P HZ%+ M+ 3?@T)A P 20A .8_FXA A2IE .8-.O@"%'H-9 1996-1999 H.-9O eP" " 6-.' e"@" M-d2)N9 , O@ e"zFZ@ + ?@2 " #N -< :2-;9 /-<=>9 5H-F=P" -0T=9 -+ #A,.+ ME Z@ ?R %f.0+ M;=>9 (@ O@ 61s yF. t2-_ M-E H=-s " "@" :2-;9 /-<=>9 -+ Hz_9 p<@ ,.+ eO@8.@ (0_n)A .8.@" #%F)E e8C " -0=F@ #A,.+ O@ ]+ 6.' , #@%+ 4P0Ki@2E .8C<. e8__E(00; 69d , Z@ (T)9 6P P @%0'"2' .8_EH9 %F9 @ :2;9 /<=>9 5#N 6T0Z ME "2). e8AI9 2002 1985 eP" -2005 eP" #@%-+ ,-l HT.-+ ;_-1 " @ M-_]A H@-E P :2;9 /<=>9 (0+ M>+@ (2008) :2-;9 /<=>9 [Ns %+ [0SFX9 %@ 5%89 J;c ME "@" ?I. M;=>9 (@ .8."2). ?29OR 1996 @ HF;_-1 80=2 MzGP + %0 , H<3d MT<C O@ e"zFZ@ + 5?@T)A P %Fz0i 5?R %+ ?P]!@ ."@" e8A-I9 2007 %<9@2-. - 2001 M-2.@o eP" #@%-+ @ M-0E% H=-9 UV. " :2;9 /<=>9 "@8; %+ %-+ @ ?.2 HT.+ UV. " :2;9 /<=>9 t@2.@ #Ae8__E(00; 6?@T)A P ]O2= UN.@%Z .8."2). 58-__EH-9 %-F9 @ /-<=>9 (@ A,.+ /01234 (0_n)A P ?DE 69@2d ME M0c%! (@ #_<9 5#-T0+ 5GDP 69C ?DE "3FG@ B@%C P 69@2d ME 8A"H9 ?I. M;=>9 (@ .8.@e"2). HZ%+ (-@ e8-_A"Y0-c2 6-" (%-[-*9 5HT.-+ %8-9 -0z0E _E " H92)d HA8+ P e%*+ %. .8_FXA /<=>9 5,"=-0( ,6@! /M! R&)A! .3 e8__E"N@ HK1@ 69@2d (00< #@%+ 52 \L+ " HZ%+ "29 H+%N P #%V. /0+"@ 2ll " #P %-+ MF-X+@P %-0xF9 ?@2-_d M-+ :2-;9 /-<=>9 M.Z 8C 52IE HT.+ UV. " :2;9 /<=>9 #2' 789 , O@ e"zFZ@ + 58_A"H9 Y0c2 @ A,.+ /01234 ME HA%0xF9 O@ #@Md2)N9 4-C 5-A,.+ H`0Fs@ F! 4C 69C Md2)N9 (@ .Z@ e8C $O@%+ HN_Z"3FG@ 6.' 1. Real Effective Exchange Rate 2. Inter-bank Loans 3. Hu, et al (2004) 4. Quagliarello (2004) 5. Cifter, et al (2009) 6. Louzis, et al (2011) 49 56"71 2,34"1 /"0 '()*( +,"%-.( +" !"#$%& 128 (-@ Y0-c2 M+ M9@"@ " .Z@ A,.+ 0T=9 0;cP P eO@8.@ 4C 5A,.+ H0K)d H@E .[O@"%'H9 :2;9 /<=>9 8C P A?R (0+ VF.@ "29 M>+@ t2. P A%0xF9 6-9@2d O@ HT 5"2C J%; H@@" M+ U@P <X. 4C + 8.@2H9 ME A,.+ H`0Fs@ F! 5%-+ #%&-',-X #@@" #-A,-.+ m-G@P " .-Z@ :2-;9 /;=>9 <X. e8__E(00; HK1@ O@ .8.2CH9 #"3FG@ "2E ?@P" 72` " :2;9 /<=>9 <X. O@ #%+ 2>Z 6)WF9 )Fs@ 1 .8C+ MFC@" "2rP :2;9 /<=>9 8C P H@@" M+ U@P <X. (0+ HF<9 M>+@ [@" VF.@ P (@ ]O2-= J0;c %89 M0c%! ?29OR) :2;9 /<=>9 F! %+ %89 \S. e8AI9 2V_9 M+ .-Z@ e8-C -W= 789 " HW0c2 %0xF9 ?@2_d M+ ]0. A,.+ H_! H@E 5((2011) 5?@T)A P O@ e"zF-Z@ + ME Z@ e8C e"zFZ@ (1389) Mn+"%E BZ2 e8C 80=2 #Ae"@" O@ H_! H@E UG@ M-.2). -+ ?-XT #@M-.2). #@%+ H<X. H@E M<ZW9 M+ (MEA) HF*r8_l H@E (0)L $P ?@2-_d M-+ -A,.+ %FI0+ H@E ME "@" "2rP VF.@ (@ .Z@ MF4@"%' M=S9 (@ " e"zFZ@ "29 #<Fd@ ,X \AE P HT.+ /0K)d ^Z_9 7%F_E " ,.+ %89 %FI0+ H.@2 O@ H34C 2 ."2C :2;9 /<=>9 \AE jd+ g-Z@%+ -9@ .8-). %-F9 @ :2;9 /<=>9 @8S9 8.@2H9 ME Z@ #%f" %0xF9 ,.+ eO@8.@ 2-Z ,- O@ .-Z@ [*<-9 :2-;9 /-<=>9 <-X. %+ ,.+ eO@8.@ %0 H+%N 8A@2C P #%V. H.<9 M-+ .8-.@" #%F*+ ,X %89 #A#k@%FZ@ %,l2E #A,.+ M+ <X. % ]+ #A,.+ ^<-Z M-+ P 8-_A"H-9 b-3F4@ "24 M+ @ @O+ O@ #%FI0+ [*Z % ]+ #A,.+ 5%f" /<d ,-X O@ #%-u-0G" H+-O@ 8_.@2H9 )Fs@ P%)KG P g0S9 M+ p2+%9 #AM!%1O@ #@"24%+ #%-F)E /D0*X M+ :2;9 /<=>9 <X. 8_.@2H9 %]+ #A,.+ 5(@%+_+ .8_C+ MFC@" "@%!@ P :2-;9 /-<=>9 <-X. (0+ <9 #@M>+@ 8_A"H9 ?I. 4/;=>9 H4%+ 56+S9 " 3.8_C+ MFC@" %-]-+ #-A,.+ " /V. P 7%F_E +2;1 M+ #@M>+@ (0_l M0r2 ."@" "2rP ,.+ eO@8.@ A,.+ F! %+ HGD4@ e%`L9 %0 O@ H34C ?@2_d M+ 8.@2H9 M9%Z M+ U@P <X. 58C eC@ #%V. H.<9 " ME M.2i?)A .1 H-@@" M+ U@P 4C O@ X0. g%FZ" " A,.+ H;G@P M9%Z #Ae"@" MT_@ M+ %V. 9@ ."%0i @%G Mr2 "29 HA"U@P M_09O " M-r2 "2-9 #"O H+%N /S0SW " P 8C+ A ,.+ #%&',X e8_A"?I. 8.@2 H9 H+24 M+ 4C (@ .[@e"2). e"zFZ@ .Z@ e"2+ (1997) ." P %i%+ .2 (2004) ?@T)A P 20A P (2002) _Z P gZ 5 (2003) 7A" P ?r@ .3 (2003) 7A" P ?r@ .4 129 '()*( +6(MN4"E OA#> 6, ?@A! ;"BC"D! )E )FG! H!(@I JKKBL u-0SW (-@ " 1."2CH9 m!_9 " P ?SF9. /dD`@ 6wX9 %FI0+ /8C jd+ ME ""%iH9 O+ "2-9 M-.2). " -A,-.+ H-@@" 6-E M-+ ,-.+ %A #AH@@" <X. 5%f" H+%N /;=>9 P%0' %-+ 0T=9 %@ e8AI9 #@%+ (0_n)A ."2CH9 "@P 789 " ,.+ eO@8.@ O@ #0;9 ?@2_d M+ HZ%+ -0T=9 #@%+ %0xF9 (@ .[@e"2). e"zFZ@ #ON9 %0xF9 , O@ AU@P %89 " A,.+ "%TK)d O@ #0-X+ g-Z@%+ Ml%i@ .Z@ e"2). 0F4@ %z1 $O@ H1234 0T=9 P , $O@ HF=P" 6-0KW M-+ M-r2 -+ -9@ 5"2-C H-9 #%F-I0+ U@P ?O jd+ A,.+ HF=P" 0T=9 H+%N /;=>9 8-A@2L. VF.@ O@ P" 58C Mw@@ M=S9 (@ #@8F+@ " ME ?@%@ " A,.+ :2;9 /<=>9 O@ Hz012 M-.2i?-)A .8-C+ #<Fd@ ,X %89 " A,.+ %F*+ "%TK)d jd+ HF=P" 0T=9 %i@ "2+ .8C+ ]0. #%V. 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MVsD9 #%V. js<9 O@ 5H-+%N #-AM-F! #@2F-Z@ 2-V_9 M-+ -A,-.+ #-AH-ikP %+ ?P]!@ {' ."@" @%G ]0. #N "%TK)d M.2fl ME (@ O@ Z@ H2i M.2). HX0Kf.@ %<F;9 ,.+ , 5(Barings) ]f_+ p2SZ .(2011) ]_0T@ P (0TI09 .1 M+ 24 0X+ M9.O@% + #@MXZ}9 O@ @ H=9 MXZ}9 , e9 , 72` " 8.@2H9 @]iE -9%!E MKvX9 P m!_9 " MKv X9 .8_E 68< MFXTCP MXZ}9 , 2. Herfindahl–Hirschman Index 49 56"71 2,34"1 /"0 '()*( +,"%-.( +" !"#$%& 130 .8-.@e8-C $O@%-+ + %@ ?@2_d M+ #N 6T0Z 4C (0I.r #A%0xF9 + ?20Z%i /";9 O@ H-Z%+ "2-9 eP" 72-` " (2 @"2-).) :2-;9 /<=>9 " #i8.9 %@ %+ M<KQ #@%+ (0_n)A e8-C e"zF-Z@ ?20Z%i Z@ )Z " 2' 6.' 789 2ll " :2;9 /<=>9 MzGP + %"S9 .[O@"%'H9 A%0xF9 (@ %u0G" Y0c2 M+ M9@"@ " .Z@ " H-)w@" P G29 /@%00x M]N #@%+ @8F+@ 5:2;9 /<=>9 %+ #N 6T0Z %@ e8AI9 #@%+ ]-r M-E Z@ e8C e"zFZ@ /TZP%'-,8A %FK0! 1357-1387 eP" #@%+ GDP H.9O #%Z M-E #2-`M-+ y t " $ t # ct H_; .Z@ ?R 8.P O@ HK9 80=2 H)Ff= %"S9 ~@%W.@ HKT0Z .8). 6G@8s @ %O M>+@ e89R Z"M+ 8.P %0X9 %"S9 T ! 1 ( yt % $ t )2 # & T %1 ! [($ t #1 % $ t ) % ($ t % $ t %1 )]2 2 B@%-C %-@ e8A-I9 #@%-+ O@ %. P U2 %. 5#T0+ %. 5GDP 8C #A%0xF9 MzGP (0_n)A ?@2_d M+ 8.@2H9 #T0+ %. .8.@e8C W= 789 " ^0G #A%0xF9 ?@2_d M+ @]N9 2`M+ H>0W9 ."%0i @%G Mr2 "29 :2;9 /<=>9 [Ns %+ #N /.Z2. %@ J012 #@%+ #%f" (]fr #-T0+ \@]-!@ @%-O 5"2-CH-9 -A,-.+ :2-;9 /-<=>9 <X. \@]!@ ^r29 %. (@ \@]!@ \A-E ?2-" -4@"%'O+ " @ ?-I@ ?@2- P e"2-). J0;- @ -A?R 8-9R" ?-%r A@2.4 \A-E ^<-Z M-+ ME Z@ 80=2 \AE O@ HF9Dd ]0. Aef_+ e+" #T0+ %. \@]!@ .8A"H9 .C@" 8A@24 7<." M+ @ Aef_+ O@ A,.+ :2;9 /<=>9 \@]!@ (@%+_+ P e"F!@ :z@ cS ?@A! ;"BC"D! +6"mM4"! .2 6(,@74 131 '()*( +6(MN4"E OA#> 6, ?@A! ;"BC"D! )E )FG! H!(@I JKKBL ?-I. @ :2-;9 /-<=>9 %-+ ?D-E "3FG@ B@%C %@ 8.@2H9 ME Z@ #%f" %0xF9 U2 %. %-. \@]-!@ -+ "PH9 VF.@ :2;9 /<=>9 + 69d (@ <9 M>+@ 5u0SW /0+"@ gZ@%+ .8A" %f" /<d M+ .8+ \AE A,.+ O@ HF!" #AU@P bLC@ #AHA8+ HS0Ss $O@ 5U2 %-F)E 4@"%'O+ M+ A?R 609 {' 58.2CH9 mzF_9 HF!" U@P O@ ?i8.%0iU@P 5H92 B@%C " M-E @%-O 58-.O@8.@ u-2; M-+ @ HF!-" #-AU@P -4@"%'O-+ ?-T9@ 8s 8__EH9 H;Z P e8C -+ [@" VF.@ (@%+_+ .8_EH9 e8.%0iU@P Mr2F9 @ H*r2 6+G mz. AU@P (@ %04 + 4@"%'O+ .8+ 8C :2;9 /<=>9 U2 %. \@]!@ @ "2-4 -0=;! M-E ,.+X' ]N+) HF=P" ,.+ 8 69C \APk' (@ " e"zFZ@ "29 M.2). 5HK9 #A,.+ 69C HF=P" #A,.+ .Z@ H1234 ,.+ 4 P (Z@ e"2). OQR 1383 7Z O@ 69-C H-1234 #-A,-.+ P (TX9 P #OPIE 5?@%iE e! 5K9 5/N 5/@"1 5M|Z 1381-1387 M-;=>9 "2-9 eP" " ME Z@ (2. "3FG@ P ?0Z' 5?9Z 5(%!RE #A,.+ O@ 81" 92 @8S9 1387 7Z " 5u0SW (@ " HZ%+ "29 #A,.+ Md2)N9 .8.@MFC@" 0=;! #@8-T.+ \-L+ H.-X.@ #P%-0. O@ 8-1" 95 O@ \0-+ P Ae"%|Z 6E O@ 81" 99 5AH@@" 6E e"@" [0-); 2-IE #-A,-.+ 6-E M-+ 8-.@2H9 ?R yF. 5P (@ O@ .Z@ e8CH9 69C @ 2IE O@ /-dD`@ #PRm-)r #@%-+ P -Z@ #@M-.L+FE /2-1 M-+ /-dD`@ #PR"%-i $P ."2C J0-12 (0_n)A .[@e"%E e"zFZ@ #]E%9 ,.+ Z P 2IE HT.+ UV. "%TK)d #A $@]i O@ H-KE #%2-3 1 7P8-r .-Z@ e8-9R 1 -Z20' " u0SW (@ " MF! EM+ #A%0xF9 #9R .8_EH9 Mw@@ 58.2CH9 J%; #%V. P H+%N /0+"@ gZ@%+ ME :2;9 /<=>9 %+ %}9 #A%0xF9 HF+- @8-S9 789 7D4@ ]r {.@P ME Z@ (@ H>4 ?20Z%i 789 [*9 hP%! O@ HT 8-+ 5-Z@ e8C 60TI H.9O #%Z P H;>S9 #Ae"@" O@ H<0E% #Ae"@" MT_@ M+ Mr2 + .Z@ @%-O "@" U-N.@ H-AP%i H.-X)A. {.-@P "2-rP 0L-I ?29OR ]0. A e"@" t2. (@ "29 " P 8_-C+ MF-C@" %0- %-V. "29 m+ #P %+ 789 HW0c2 #A%0xF9 O@ %0Q M+ HK9@2d Z@ (T)9 8-_A@24 ?-I. HZ%+ "29 789 8.)X' " @ "24 /@%@ A%0xF9 M.2f_@ .8_C<. #%0ieO@8.@ 6+G @]!@U%. O@ e"zFZ@ + M;=>9 (@ " .8_C+ JKFL9 #A{.@P #@@" m`S9 Z@ (T)9 {' ."@" H_<9 ?29OR (@ %z1 M0c%! .Z@ MF!%i UN.@ LR test16.' {.@P H.X)A. ?29OR stata10 {.-@P H.-X)A. "2-rP #-_;9 M-+ ?R 6-+S9 M0-c%! P 6.' #Ae"@" {.@P H.X)A. U8d %+ 1. Likelihood Ratio 49 56"71 2,34"1 /"0 '()*( +,"%-.( +" !"#$%& 132 M-+ hP%-z9 6-.' 78-9 " H 0 M0c%! 52 Z20' M+ Mr2 + ?29OR (@ O@ 61s yF. u<` .Z@ ."2CH9 " {.@P H.X)A. U8d ?@A! ;"BC"D! )E )FG! +")K`-! R&)A! .1 /Me ")K`-! 6, )K`-! O!XI "/M! )K`-! h*)AL ,6@! 6"Y-4( RN4"E V"W +")K`-! H@@" M+ U@P <X. lloas ,.+ eO@8.@ lsize H@E 4C , LOANit ) ' ln(L / Ait ) " ln** ' + ASSETit ( , ASSETit ln( SIZEit ) " ln* * ASSETit + ) ' ' ( P e2S=+ "2<*+ 4C gZ@%+ e8C M<ZW9 ! H0K)d leff HF=P" 0T=9 StateOwner ]E%) Mr" lhhi MEA $P StateOwner=1 ln(HHI t ) " ln,* + O!XI !i "1 Si2 )'( N + +/- +/+/- +,"%-.( 'XU +")K`-! #N 6T0Z 4C HP cy GDP 8C lgdpg U2 %. linf #T0+ %. lue ?R 8.P O@ GDP H)Ff= %"S9 ~@%W.@ (/TZ%' ,8A %FK0!) , GDPt % GDPt %1 ) ' ln(GDPt ) " ln ** ' GDPt %1 + ( , CPI t % CPI t %1 ) ' ln(INFt ) " ln** ' CPI t %1 + ( ln(ue) - + + .80_E M;r@%9 (1389) Mn+"%E M+ $P (@ /0w]r #@%+ M-E 7D-4@ /D-)r (0+ HfFX<A"24 ME Z@ (@ 6.' #2' 789 [*9 hP%! O@ %f" HT " HfF-X<)A"24 U8-d "2rP HZ%+ #@%+ ."@8. "2rP 5"2CH9 "@P M;9r ?20Z%i m+ " 2F-Z" -+ stata10 @]-!@ U%-. " M-E (2002) y8-=PP ?2-9OR O@ 6-.' 789 " 7DF4@ /D)r 133 '()*( +6(MN4"E OA#> 6, ?@A! ;"BC"D! )E )FG! H!(@I JKKBL (0-+ HfF-X<)A"24 U8d %+ H_<9 ?29OR (@ %z1 M0c%! .[@e"2). e"zFZ@ "2C H9 @%r@ xtserial1 + .Z@ 7D4@ /D)r (0+ HfFX<)A"24 "2rP #_;9 M+ 6+S9 M0c%! P ( - " 0 ) 7D4@ /D)r %-0xF9 %-@ ?P8-+ P ?D-E %0xF9 %@ + 789 P" " F e9R 52 Z20' 7P8r O@ 61s yF. M+ Mr2 Y>-Z " 6G@8-s M-E -!%i M-N0F. ?@2H9 5Z@ 0/089 6G@8s + 7";9 P-Value #@@" ?DE 78-9 #@%-+ H M0-c%! 8-1" 90 ?-_0)`@ Y>-Z " M-F<=@ ."2CH). " H 0 M0c%! %5 #@"_;9 " {.-@P H.X)A. 6TI9 "2rP M+ Mr2 + 57s%*+ .8C 8A@24 " ?DE %0xF9 + %@ #@@" e"zF-Z@ 2@2F-Z@ (0-)L $P O@ A789 (0)L " 5HfFX<)A "24 6TI9 "2rP 7)Fs@ P Ae"@" P {.-@P H.-X)A. /DT-I9 M-+ <-X. H-_0)L ^@%-c -0;9 ~@%-W.@ ?R " ME Z@ e8C .Z@ @2FZ@ HfFX<)A"24 ]*"-4 HK$<L JK7gL .4 6-0KW P H-Z%+ 6-<G \-L+ " e8-C Y%-3 78-9 (0-)L O@ e89R Z"M+ yF. 5\L+ (@ " -+ @8-S9 5NPL 578-9 MF-X+@P %-0xF9 -F! " #i8.9 ^<Z M+ 8C MFzi ME M.2i?)A ."2CH9 ?R " M-E 78-9 ,- " MT_@ M+ Mr2 + .[0_EH9 W= 789 " 6SFX9 %0xF9 ?@2_d M+ @ ?R MzGP (0-+ HfF-X<)A "2-rP ?-T9@ 58.2-CH-9 "@P HW0-c2 %0xF9 ?@2_dM+ MFX+@P %0xF9 %04 + %"S9 GMM3$P O@ 78-9 "PR%+ #@%+ 5"@" "2rP $2 6TI9 P >4 MK)r P #%04 MFX+@P %0xF9 " 7P@ M-Ks%9 .[0-_EH-9 e"zFZ@ 5Z@ e8C Mw@@ 2' 6.' #Ae"@" #@%+ 48.+ P 2.R BZ2 ME M-+ M-r2 -+ .-Z@ /"-;9 [F-X0Z ,- /2-1 M-+ 78-9 H-Z_C 8-.+ P 2.R e8__E"PR%+ B-Z2 e8-C M-w@@ $P O@ u-0SW (@ " (1991) 8.+ P 2.R e8__E"PR%+ " e2S=+ #A J;c H)F-X0Z GMM M-E e8-C D1@ $P (@ " .Z@ e8C e"zFZ@ 68.+ P 78.D+ P 52+ P 2.R %-0xF9 "-N@ #A8-_@%! H@8-F+@ B@%-C %-+ H!c@ #A"P8W9 O@ Md2)N9 , "2CH9 e809. .""%iH9 7)d@ MFX+@P ."2C M;r@%9 (Drukker (2003)) %EP" M=S9 M+ %FI0+ M;=>9 #@%+ .1 2. Robust 3. Generalized Methods of Moments 4. Arellano-Bond (1991) 5. Arellano-Bover (1995) 6. Blundell-Bond (1998) 49 56"71 2,34"1 /"0 '()*( +,"%-.( +" !"#$%& 134 :2-;9 /-<=>9 M.-Z %-00x H)F-f= g-0S9 M-E M F-X+@P %0xF9 (0+ M>+@ 2ll (@ " @]-!@U%-. 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" :2-;9 /-<=>9 8-C \@]!@ jd+ HK9 "3FG@ B@%C 94P P HK9 80=2 %FI0+ .Z@ e8C 2IE 5:2-;9 /-<=>9 e-+" H-F=P" #-A,-.+ %-F*+ "%-TK)d 8-0 u-0SW (@ #AMF! O@ HT M-+ %F-I0+ ME A,.+ (@ HF4@"%' #AU@P t2. )Fs@ Ml%i@ .Z@ %f" B@%C /< M+ hP%z9 HZ%+ P u0SW 9@ 58C+ #@MN0F. (0_l #@%+ HW0c2 8.@2H9 5Z@ HF=P" #A"*. P AE%C P H-1234 5H-F=P" #-A\-L+ O@ ,- %-A ,-0Tz M-+ -A U@P t@2.@ #A e"@" gZ@%+ #%u0G" M--+ #@M--;=>9 (0-_l U--N.@ .8--). M-w@@ H=--)Fs@ (0--_l #@%-+ H=8F--X9 --Z' 8-.@2H--9 -A@2.4 ."2CH9 M012 t2c29 (@ ?@8_9MGDd (0-_l[-A P -A,-.+ %8-9 -0F4@ " #8_)-CO@ /-dD`@ 8.@2 H9 u0SW (@ #AMF! P "%-TK)d 4-C (0-+ H-z_9 e8-C e8AI9 M>+@ M.2). ?@2_d M+ .8A" @%G HT.+ UV. " (%. /DT-I9 %-+ 8.@2H9 ?R J;c ME HIS. P OP%9@ "%TK)d M+ Mr2 0)A@ M+ :2;9 /<=>9 8C M-+ 8-_.@2H-9 "%-TK)d #-A4-C g-Z@ (@ %+ .8_EH9 eC@ 8C+ MFC@" HT.+ UV. " e8_R -F! [0SF-X9 P @"_;9 M>+@ .8_C+ e8_R " (%!RMKvX9 #A,.+ 4_C #@%+ H)_A@ ?@2_d ?@%8-9 M-+ @ #-<Fd@ ,-X %8-9 M-+ M-r2 0)A@ :2;9 /<=>9 8C P A,.+ HTX .8_EH9 "]C2i %FI0+ H=9 /< #@%G%+ #@%+ HV. 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Wooldridge, Jeffrey (2002), Econometric Analysis of Cross Section and Panel Data, Cambridge, MA: MIT Press. 49 234' 5#/67' () *+,-+ ./01+ .#"#$%& ! 142 #I)9E& Variable Mean (G 3/ 0!3 3N? .#,EK0 .3L MEJ9F .1 I)9E& Std. Dev. Min Max Observations npl overall between within 7350.536 8726.219 4770.651 7418.425 0 1217 -3653.036 39134 152 99 35026.54 N = n = T = 84 12 7 eff overall between within .8730952 .1522362 .1147811 .1046591 .38 .58 .4859524 1 .9728571 1 .095952 N = n = T = 84 12 7 loas overall between within .6752047 .7240268 .2846721 .6701006 .0626991 .3998748 -.769851 6.95965 1 .543002 6 .091853 N = n = T = 84 12 7 size overall between within .9998673 .8561805 .8506635 .2484497 .0167833 .093451 -.4670721 3.033118 2 .767415 1.546039 N = n = T = 84 12 7 Cygdp overall between within 26877.34 3 2428.02 3.80e-12 32428.02 -18982.27 26877.34 -18982.27 7 1408.47 2 6877.34 71408.47 N = n = T = 84 12 7 gdpgro~h overall between within .0674209 .0065056 0 .0065056 .06 .0674209 .06 . 07881 .0674209 . 07881 N = n = T = 84 12 7 ue overall between within 11.15714 .7251566 0 .7251566 10.4 11.15714 10.4 12 .5 11.15714 12 .5 N = n = T = 84 12 7 inf overall between within .161 .0 45479 0 .0 45479 .104 .161 .104 .254 .1 61 .254 N = n = T = 84 12 7 hhi overall between within 1387.767 1 80.1599 0 180.1599 1202.94 1387.767 1202.94 1 663.538 1387.767 1 663.538 N = n = T = 84 12 7 143 *+,-+ .3+GH7? I:J 3/ 89: ;<=> ,? ,@A B+9C DEE<F 2/U0)+ /39 B7& .#2/+/ 3/ QR0S<4#/9T $ Q7S4#7 .#*9OL P-07 .2 I)9E& . log us ing "H etr o a nd Aut oco rre lat ion pa nel te st" l og: C:\U sers\S imin\My Stude nts\POR DEL\Dat a\Hetr o and A utocor relatio n p > an el tes t.smcl lo g t ype : smcl op ene d o n: 18 N ov 201 1, 03:2 6:39 . *He tro ske das tis ity . * Mod el 1 w it hou t pa nel te st ma cr o e ff ect . xtg ls lnp lge L. lnp lge > osk eda sti c) It er ati on 1: to ler an ce = It er ati on 2: to ler an ce = It er ati on 3: to ler an ce = It er ati on 4: to ler an ce = le ff llo as lsi ze sts teO wne r l loa ng , i gls pa nel s ( het er .1788 5842 .1060 9526 .0670 9063 .0698 9424 Cr oss -se cti ona l t ime -se rie s F GLS re gre ssi on Co eff ici ent s: Pa nel s: Co rre lat ion : gene rali zed least squ ares hete rosk edas tic no a utoc orre latio n Es tim ate d c ova ria nce s = Es tim ate d a uto cor rel ati ons = Es tim ate d c oef fic ien ts = Lo g l ike lih ood 12 0 8 N umb er of obs = 53 N umb er of gro ups = 12 O bs per gr oup : m in = 4 av g = 4.4 16667 5 ma x = W ald c hi2( 7) = 6 40.96 Pr ob > ch i2 = 0 .0000 =-27 .244 96 ln plg e C oe f. ln plg e L1. le ff ll oas ls ize st ste Own er ll oan g lg dpg _c ons .0 8808 82 -.2 4945 36 .2 3974 27 -.0 3194 96 -.4 1308 19 -.0 6868 45 .2 0709 89 1. 2006 65 Std . Err . .115 0304 .220 8037 .035 3376 .081 2177 .161 9534 .030 4349 .166 9812 .355 4761 z 0.7 7 -1.1 3 6.7 8 -0.3 9 -2.5 5 -2.2 6 1.2 4 3.3 8 P>| z| 0.44 4 0.25 9 0.00 0 0.69 4 0.01 1 0.02 4 0.21 5 0.00 1 [95% C on f. In ter va l] -.13 7367 2 -.68 2220 9 .17 0482 3 -.19 1133 5 -.73 0504 8 -.12 8335 8 -.12 0178 2 .50 3944 6 .31 35437 .18 33137 .3 09003 .12 72342 -.09 56589 -.00 90332 .5 34376 1.8 97386 . es tim at es st ore h ete ro s1 . xtg ls lnp lge L. lnp lge le ff llo as lsi ze sts teO wne r l loa ng lgd pg, ig ls It er ati on 1: to ler an ce = 6. 040e- 14 Cr oss -se cti ona l t ime -se rie s F GLS re gre ssi on Co eff ici ent s: Pa nel s: Co rre lat ion : gene rali zed least squ ares homo sked asti c no a utoc orre latio n Es tim ate d c ova ria nce s = Es tim ate d a uto cor rel ati ons = Es tim ate d c oef fic ien ts = Lo g l ike lih ood 1 0 8 N umb er of obs = 53 N umb er of gro ups = 12 O bs per gr oup : m in = 4 av g = 4.4 16667 ma x = 5 22.24 W ald c hi2( 7) = Pr ob > ch i2 = 0 .0023 =-50 .327 27 ln plg e C oe f. ln plg e L1. le ff ll oas ls ize st ste Own er ll oan g lg dpg _c ons -.0 4150 81 .7 1693 92 .0 2671 96 .0 9061 22 -.8 3606 09 .0 5926 76 1. 3525 99 3. 3294 28 Std . Err . .136 1492 .675 8215 .163 8981 .121 6054 .302 6448 .084 4587 1.02 2864 1.4 1628 z -0.3 0 1.0 6 0.1 6 0.7 5 -2.7 6 0.7 0 1.3 2 2.3 5 P>| z| 0.76 0 0.28 9 0.87 0 0.45 6 0.00 6 0.48 3 0.18 6 0.01 9 [95% C on f. In ter va l] -.30 8355 5 -.60 7646 6 -.29 4514 8 -.14 7729 9 -1.4 2923 4 -.10 6268 4 -.65 2177 3 .55 3569 4 .22 53393 2.0 41525 .3 47954 .32 89543 -.24 28879 .22 48036 3.3 57375 6.1 05286 . lo cal d f = e (N_ g) - 1 . lr tes t het er os1 . , df (` df' ) Li ke lih oo d-r at io te st (A ssu mpt ion : . n est ed in he te ros 1) LR c hi2( 11 ) = Pr ob > chi 2 = 46.16 0 .0000 . *M ode l 2 w it hou t mac ro ef fe ct . xtg ls lnp lge L. lnp lge > osk eda sti c) It er ati on 1: to ler an ce = It er ati on 2: to ler an ce = It er ati on 3: to ler an ce = le ff llo as lsi ze sts teO wne r l loa ng , i gls pa nel s ( het er .1 78858 42 .1 06095 26 .0 67090 63 49 234' 5#/67' () *+,-+ ./01+ .#"#$%& ! 144 Cross-se cti ona l Coeffici ent s: Panels: Correlat ion : Estimate d Estimate d Estimate d Log . t ime -se rie s c ova ria nce s a uto cor rel ati ons c oef fic ien ts 12 0 7 C oe f. ln plg e L1. le ff lloas lsize st steOwner ll oan g _cons .0 6 22 74 8 - .2 1 31 49 3 .2 3 21 70 5 - .0 0 58 92 1 - .4 8 16 37 2 - .0 5 99 34 5 .9 9 31 16 4 Std . Err . use es tim at es Cross-se cti ona l Coeffici ent s: Panels: Correlat ion : llo as lsize le ff llo as 7 . 08 4e -1 6 t ime -se rie s C oe f. ln plg e L1. le ff lloas lsize st steOwner ll oan g _cons -. 0 78 01 3 .7 2 69 12 2 .0 2 28 17 5 .0 8 21 16 7 - .8 3 91 50 4 .0 5 63 26 4 1. 5 07 55 9 . lo cal d f . lrtes t het er os2 . , = e (N_ g) - Li ke lih oo d-r at io te st (Assumpt ion : . nested F GLS lsize . 2 99 42 59 . 22 85 39 . 3 22 53 61 . 1 56 73 76 -. 1 26 79 82 . 00 58 22 1 . 40 63 67 l loa ng , i gls cl ear sts teOwne r l loa ng , i gls re gression 1 0 7 = = = Std . Err . N umb er of obs = 53 N umb er of gro ups = 12 O bs per gr oup : m in = 4 4 . 41 66 67 av g = ma x = 5 1 9. 84 W ald c hi2( 6 ) = Pr ob > ch i2 = 0. 00 30 [95% C on f. -. 3 43 59 3 2 -. 6 19 26 3 8 -. 3 03 62 0 3 - . 15 97 8 7 -1 . 44 20 1 1 - .1 11 8 6 . 8 53 83 8 8 LR c hi2( 1 1 ) = Pr ob > chi 2 = in he te ros 2) *Mode l 1 w it h . xtseri al (2002) Pa nen T est : wri tt en by Da vi d effec t le ff llo as lsi ze Wooldrid ge tes t f or aut oco rre lat ion H0: no fi rs t-o rd er au tocorrelation 3. 47 8 F( 1, 11) = Pro b > F = 0. 08 91 2 w it hou t mac ro lnp lge P>| z| 0. 56 5 0. 29 0 0. 89 1 0. 50 6 0. 00 6 0. 51 2 0. 00 0 In ter va l] . 1 87 56 72 2 . 07 30 88 . 3 49 25 53 . 3 24 02 05 -. 2 36 28 94 . 2 24 51 27 2 . 16 12 78 1 . macro - 0. 58 1. 06 0. 14 0. 67 - 2. 73 0. 66 4. 52 df(`df') * Aut oc oro la tio n Woo ld rid ge ) lnp lge z . 13 55 0 26 . 68 68 3 71 .1 66 5 53 . 12 34 2 26 . 30 75 8 78 . 08 58 1 09 . 33 35 3 66 . > xtseri al sts teOwne r =-5 1. 1 87 48 ln plg e * Mod el -. 1 74 87 6 4 -. 6 54 83 7 7 . 1 41 80 4 8 -. 1 68 52 1 8 -. 8 36 47 6 3 -. 1 25 69 1 1 . 5 79 86 5 6 In ter va l] ge ne r al iz ed le as t s qu ar e s ho mo s ke da st i c no a u to co rr e la ti on like lih ood . 0. 60 7 0. 34 4 0. 00 0 0. 94 3 0. 00 8 0. 07 4 0. 00 0 S tud ent s\P ORDEL\Data\NPLd ata set .dt a", c ova ria nce s a uto cor rel ati ons c oef fic ien ts . [95% C on f. st ore h ete ro s2 . xtg ls lnp lge L. lnp lge It er ati on 1: to ler an ce = Log 0. 51 - 0. 95 5. 04 - 0. 07 - 2. 66 - 1. 79 4. 71 P>| z| st ore h ete ro s2 "C :\U ser s\S imi n\M y Estimate d Estimate d Estimate d z . 12 09 9 77 . 22 53 5 53 . 04 61 0 58 . 08 29 7 58 . 18 10 4 37 . 03 35 4 99 . 21 08 4 61 . xtg ls lnp lge L. lnp lge le ff v ar ia bl e l lo a ng n ot fo un d r(111); . N umb er of obs = 53 12 N umb er of gro ups = 4 O bs per gr oup : m in = 4 . 41 66 67 av g = ma x = 5 W ald c hi2( 6 ) = 7 1. 40 0. 00 00 Pr ob > ch i2 = =-3 2. 0 86 01 ln plg e . re gression = = = like lih ood es tim at es F GLS ge ne r al iz ed le as t s qu ar e s he te r os ke da s ti c no a u to co rr e la ti on le ff ststeOwner in panel lloa ng data ef fe ct llo as lsi ze Wooldrid ge tes t f or aut oco rre lat ion H0: no fi rs t-o rd er au tocorrelation F( 1, 11) = 0. 00 0 Pro b > F = 0. 99 25 ststeOwner in panel lloa ng data lgd pg 3 8. 20 0. 00 01 D ru kke r (2003 145 *+,-+ .3+GH7? I:J 3/ 89: ;<=> ,? ,@A B+9C DEE<F . . . log us ing 6 F 1 Q40SE) GMM .#(G DE4VF P-07 .3 I)9E& "The res ult s o f t he spe cif ied GM M Models" l og: C: \ U se r s \S i m in \ My S tu d e nt s \ PO R D EL \ D at a \ Th e r e s ul t s o f th e sp e c if i e > d G MM M od e l s. s m cl lo g t ype: sm c l opened o n: 18 N ov 2 01 1 , 0 9 :2 6 : 17 . . *Re sul ts of NPL de ter min ant s in I ran ian ba nking sys tem . . *De pen den t variabl e i s t he logari thm ic sca le of NPL gr owt h . . * T he ban k effect var iab les inclu de: , log arithmic sca le of effici enc y, log ar > ith mic sc ale of lo ans to as sets r ati o, log arithmic sca le of size o wne rsh ip st > at us wh ich is a d ummy wit h fig ur e 1 f or st at e o wn ers hi p . . > > > > * F or rob ustness o f t he emp irical fi ndi ngs , the reg res sio ns wer e e sti mat ed wi th alt ern ative mea sur es of busine ss cyc le variable: th e d eviation of gdp ov er lo gar ith m scale f rom it s t rend ( usi ng Hod rick-Pres cot t) method as cy cli cal o utp ut, th e lag act ual GD P g rowth rat e, the lag of l oga rit hm of une mpl oym ent r ate an d e lag of l oga rit hm of inf lat ion r ate . . * Mod el I: > ct Syst em GMM mo del wi th rob ust standard er ror s without ma cro . . xt dpd sy s l nplge l eff llo as ls iz e sts teO wn er , maxld ep( ) la gs(2) > vc e(r obu st) note: st ste Owner dro ppe d f rom div() be cau se of collin ear ity Syste m d yna mic panel -da ta est imatio n Group va ria ble: i d Time var iab le: y e ar Numbe r o f i nstrument s = One-s tep a rte st s(2) Number o f o bs Numbe r o f g roups Obs per 17 gro up: ef fe = = 47 12 min = 3 3 .9 1 6 66 7 avg = 4 max = Wa ld chi2( 6) Pr ob > ch i2 = = 4 3 .2 8 0 . 0 00 0 re sults ln plg e C oe f. ln plg e L1. L2. leff ll oas ls ize st ste Owner _c ons - .3 5 7 63 5 9 -. 3 0 58 3 9 - 1. 2 8 02 0 6 .2 5 9 68 9 6 .3 6 4 30 7 9 - 1. 7 9 18 9 7 2. 5 5 22 5 2 Ro bus t Std . Err . .1 8 4 59 1 9 .1 4 7 38 0 1 .5 9 2 22 0 8 .1 5 1 32 4 5 .3 2 3 88 9 5 .8 6 2 34 4 3 .8 2 6 51 8 5 z P>| z| -1 . 9 4 -2 . 0 8 -2 . 1 6 1.72 1.12 -2 . 0 8 3.09 [95% C on f. In ter va l] 0. 0 5 3 0. 0 3 8 0. 0 3 1 0. 0 8 6 0. 2 6 1 0. 0 3 8 0. 0 0 2 - . 71 9 4 29 4 - . 59 4 6 98 7 - 2 .4 4 0 93 7 - . 03 6 9 00 9 - . 27 0 5 03 9 - 3 .4 8 2 06 1 . 93 2 3 05 9 . 00 4 1 57 6 - . 01 6 9 79 2 - . 11 9 4 74 4 . 55 6 2 80 1 . 99 9 1 19 7 - . 10 1 7 33 2 4 .1 7 2 19 9 Instr ume nts for d iff ere nce d e quatio n GMM -type: L ( 2 /. ) . ln p lg e Sta ndard: D . l ef f D. l lo a s D . l si z e Instr ume nts for leve l e qua tio n GMM -type: L D . ln p l ge Sta ndard: _ c o ns . . * Mod el II: Sys tem GM M m odel w ith ro bus t standar d e rro rs with m acr o e ffe ct > wh ich is cyclica l o utp ut . . xt dpd sy s l nplge l eff llo as ls iz e sts teO wn er lcygdp , l ag s(2) > (ro bus t) note: st ste Owner dro ppe d f rom div() be cau se of collin ear ity Syste m d yna mic panel -da ta est imatio n Group va ria ble: i d Time var iab le: y e ar Number o f o bs Numbe r o f g roups Obs per Numbe r o f i nstrument s = One-s tep 18 gro up: = = 47 12 3 min = 3 .9 1 6 66 7 avg = max = 4 Wa ld chi2( 7) Pr ob > ch i2 = = 1 8 .1 0 0 . 0 11 5 re sults ln plg e C oe f. ln plg e L1. L2. leff ll oas ls ize st ste Owner lc ygd p _c ons -. 3 7 09 1 7 - .3 9 8 59 2 6 - 2. 9 3 51 4 6 .2 9 8 14 7 3 .3 8 2 64 9 3 - 2. 5 0 39 2 7 2. 1 1 33 8 1 5. 8 6 28 8 1 Ro bus t Std . Err . .1 8 2 81 1 6 .1 6 8 15 9 5 1. 5 9 40 8 8 .1 5 8 32 0 4 . 3 0 07 3 6 1. 3 2 60 3 1 1. 5 9 26 1 5 2. 9 9 37 6 8 z -2 . 0 3 -2 . 3 7 -1 . 8 4 1.88 1.27 -1 . 8 9 1.33 1.96 P>| z| 0. 0 4 2 0. 0 1 8 0. 0 6 6 0. 0 6 0 0. 2 0 3 0. 0 5 9 0. 1 8 5 0. 0 5 0 Instr ume nts for d iff ere nce d e quatio n GMM -type: L ( 2 /. ) . ln p lg e Sta ndard: D . l ef f D. l lo a s D . l si z e D . l cy g d p Instr ume nts for leve l e qua tio n GMM -type: L D . ln p l ge Sta ndard: _ c o ns . a rte st s(2) vc e [95% C on f. In ter va l] - . 72 9 2 21 1 - . 72 8 1 79 2 -6 . 0 59 5 - . 01 2 1 55 1 - . 20 6 7 82 5 - 5 .1 0 2 90 1 - 1 .0 0 8 08 8 - . 00 4 7 96 5 - . 01 2 6 12 9 - .0 6 9 00 6 . 18 9 2 08 3 . 60 8 4 49 6 .9 7 2 08 1 . 09 5 0 46 7 5 .2 3 4 84 9 1 1. 7 3 05 6 49 234' 5#/67' () *+,-+ ./01+ .#"#$%& ! 146 . . . * Mod el III: System GMM model with robust sta nda rd errors with macro effec > t w hic h is the lag of DGP growth . xtdpdsys lnplge leff lloas lsiz e s tsteOwner L .gd pg, lags(2) > (ro bus t) no te: st steOwner dropped from div() because of col lin ear ity Sy ste m d ynamic panel-data estimation Gr oup va riable: i d Ti me var iable: ye ar Nu mbe r o f o bs Number of groups Ob s p er gro up: Nu mbe r o f instruments = a rte sts(2) vce 18 = = 47 12 min = 3 avg =3. 916 667 max = 4 Wald ch i2( 7) Prob > chi2 = = 17 .11 0.0 167 On e-s tep results ln plge ln plge L1. L2. leff lloas lsize ststeOwner gdpgt L1. _cons Coef. Robust Std. Err. z P>|z| [95% Conf. Inter val] - .29 40 939 - .36 69 013 - 2.1 84 739 .30 23 093 .44 97 999 - 2.1 30 817 .15 405 74 .14 903 92 1.1 435 58 .13 958 09 .30 371 98 1.0 004 68 -1. 91 -2. 46 -1. 91 2. 17 1. 48 -2. 13 0 .05 6 0 .01 4 0 .05 6 0.03 0 0 .13 9 0 .03 3 - .59 604 09 - .65 901 28 - 4.4 260 72 .02 873 57 - .14 547 98 - 4.0 916 98 .0 078 531 -.0 747 899 .0 565 941 .5 758 829 1 .04 508 -.1 699 353 - 10. 29 945 5.3 66 013 4.9 487 27 1.8 973 93 -2. 08 2. 83 0 .03 7 0 .00 5 - 19. 998 78 1. 647 19 -.6 001 215 9. 084 835 In str ume nts for differenced equation GMM-type: L( 2/ .). lnp lge Standard: D. le ff D.l loa s D.ls ize LD .gd pgt In str ume nts for level equation GMM-type: LD .l npl ge Standard: _c on s . . * Mod el IV: System GMM model with robust s tan dar d e rrors with macro effect > wh ich is the lag of the logarithm scale of une mpl oym ent rate . xtdpdsys lnplge leff lloas lsiz e s tsteOwner L .lu , lags(2) artests(2) vce(r > obu st) no te: st steOwner dropped from div() because of col lin ear ity Sy ste m d ynamic panel-data estimation Gr oup va riable: i d Ti me var iable: ye ar Nu mbe r o f o bs Number of groups Ob s p er gro up: Nu mbe r o f instruments = 18 = = 47 12 3 min = avg =3. 916 667 4 max = Wald ch i2( 7) Prob > chi2 = = 30 .57 0.0 001 On e-s tep results ln plge ln plge L1. L2. leff lloas lsize ststeOwner lu e L1. _cons Coef. Robust Std. Err. z [95% Conf. Inter val] - .42 13 356 - .31 44 735 - 1.9 22 629 .25 29 637 .34 86 971 - 2.2 33 512 .19 023 77 .15 373 24 .98 017 41 .12 878 53 .29 188 38 1.1 387 72 -2. 21 -2. 05 -1. 96 1. 96 1. 19 -1. 96 0 .02 7 0 .04 1 0 .05 0 0.05 0 0 .23 2 0 .05 0 - .79 419 45 - .61 578 35 - 3.8 437 35 .00 054 91 - .22 338 46 - 4.4 654 63 -.0 484 766 -.0 131 636 -.0 015 226 .5 053 783 .9 207 787 -.0 015 604 3.4 35 014 - 5.4 02 318 2.5 694 07 5.6 518 36 1. 34 -0. 96 0 .18 1 0 .33 9 - 1.6 009 32 - 16. 479 71 8 .47 096 5. 675 077 In str ume nts for differenced equation GMM-type: L( 2/ .). lnp lge Standard: D. le ff D.l loa s D.ls ize LD .lu e In str ume nts for level equation GMM-type: LD .l npl ge Standard: _c on s . P>|z| 147 *+,-+ .3+GH7? I:J 3/ 89: ;<=> ,? ,@A B+9C DEE<F . . * Mod el V: S ystem GMM model with robust standard errors with ma cro effect > which is the lag of the logarithm s cale of inflati on rate . . xt dpdsys lnplge leff lloas lsize s tsteO wn er L.linf, lags(2) > (robus t) note: st steOwner droppe d from div() be cause of collin earity System d ynamic panel-da ta estimation Group va riable: i d Time var iable: ye ar Number o f obs Nu mber o f groups Obs per group: Number o f instruments = a rtests(2) vce 18 = = 47 12 min = 3 3.9166 67 avg = 4 max = Wald chi2( 7 ) Prob > chi2 = = 40. 82 0.00 00 One-step results ln plge ln plge L1. L2. le ff ll oas ls ize st steOwner li nf L1. _c ons Coe f. Ro bust Std. Err . z P>|z| [95% Conf. Interva l] - .41841 06 - .27446 05 - 1.3062 38 .23654 46 .35639 16 - 2.0173 39 .1 766404 .1 463142 .6 560257 . 121407 . 295508 .9 691686 -2. 37 -1. 88 -1. 99 1. 95 1. 21 -2. 08 0. 018 0. 061 0. 046 0. 051 0. 228 0. 037 -.7646 194 -.561 231 -2.592 024 -.0014 088 -.2227 935 -3.916 874 -.07220 19 .012 31 -.02045 09 .47449 79 .93557 67 -.11780 33 -.7690 09 1.1631 24 .4 664287 .8 769426 -1. 65 1. 33 0. 099 0. 185 -1.683 192 -.5556 517 .14517 44 2.88 19 Instrume nts for differe nced equation GMM-type: L(2/. ).lnplg e Standard: D.lef f D.llo as D.ls ize LD .linf Instrume nts for level e quation GMM-type: LD.ln plge Standard: _cons . . * Mod el VI: > ect wh ich is System GMM m odel w ith robust standar d errors with i ndustry eff the lo garithm scale o f concentration ratio=hhi . xt dpdsys lnplge leff llo as lsize s tsteO wn er lhhi , lags(2) artests(2) vc e(r > obust) note: st steOwner droppe d from div() be cause of collin earity System d ynamic panel-da ta estimation Group va riable: i d Time var iable: ye ar Number o f obs Nu mber o f groups Obs per group: Number o f instruments = 18 = = 47 12 min = 3 avg = 3.9166 67 4 max = Wald chi2( 7 ) Prob > chi2 = = 35. 95 0.00 00 One-step results ln plge Coe f. ln plge L1. L2. le ff ll oas ls ize st steOwner lh hi _c ons - .36558 04 - .34065 88 - 1.8279 09 .27184 74 .36362 02 - 2.0161 73 - 1.6317 18 14.305 29 Ro bust Std. Err . .1 794824 .1 407569 1. 396601 .1 727658 .3 174157 1. 094288 4. 303719 31 .18648 z -2. 04 -2. 42 -1. 31 1. 57 1. 15 -1. 84 -0. 38 0. 46 P>|z| 0. 042 0. 016 0. 191 0. 116 0. 252 0. 065 0. 705 0. 646 [95% Conf. Interva l] -.7173 595 -.6165 373 -4.565 197 -.0667 674 -.2585 031 -4.160 937 -10.06 685 -46.8 191 -.01380 12 -.06478 04 .90937 91 .61046 22 .98574 35 .12859 16 6.8034 17 75.429 67 Instrume nts for differe nced equation GMM-type: L(2/. ).lnplg e Standard: D.lef f D.llo as D.ls ize D. lhhi Instrume nts for level e quation GMM-type: LD.ln plge Standard: _cons . log cl ose lo g: C:\U sers\S imin\My Studen ts\POR DEL\Dat a\The results of th e speci fie > d GMM Mo dels.sm cl lo g type: smcl cl osed on: 18 N ov 201 1, 09:2 8:48 49 234' 5#/67' () *+,-+ ./01+ .#"#$%& ! 148 =W 3/ 2/U0)+ /39 QJ+ .#2/+/ .4 I)9E& HHI Inflation Unemployment GDPgrowth Efficiency Asset loan NPL year 1664 0/158 12/5 0/079 0/73 163090 88586 2781 81 !" 1664 0/158 12/5 0/079 0/69 62868 35678 1102 81 #$% 1664 0/158 12/5 0/079 0/63 120766 62705 5188 81 &'()* 1664 0/158 12/5 0/079 0/85 0/81 87881 103467 62705 61821 0 1901 81 &(+, 1664 0/158 12/5 0/079 0/38 21379 10000 38566 398 1573 81 81 012" 1664 0/158 12/5 0/079 1 36028 30713 358 81 34(567 1664 0/158 12/5 0/079 0/88 0/63 981 1791 689 10 81 ;"% 1664 0/158 12/5 0/079 0/6 909 392 0 81 1626 0/156 11/5 0/075 0/83 215200 121884 3406 82 82 #$% 1626 0/156 11/5 0/075 0/54 149607 76362 7850 82 &'()* 1626 0/156 11/5 0/075 1 147292 104247 2027 82 -!" 1626 0/156 11/5 0/075 0/53 27945 59549 17111 74/483 742 1695 82 82 012" 1626 0/156 11/5 0/075 0/96 42512 35351 966 82 34(567 1626 0/156 11/5 0/075 0/9 2410 1793 6297 142 31 82 1626 0/156 11/5 0/075 0/91 2709 1859 0 82 1446 0/152 10/4 0/062 0/77 0/94 1257968 290700 169324 3790 83 83 #$% 1446 0/152 10/4 0/062 0/64 176606 171283 92904 119986 13409 83 &'()* 1446 0/152 10/4 0/062 0/062 1 0/99 206557 149354 3285 83 -!" 1446 0/152 10/4 0/062 0/99 88973 62491 4668 83 012" 1446 0/152 10/4 0/062 0/78 63441 43909 1944 83 34(567 1446 0/152 10/4 0/062 0/85 4227 126 83 1664 1664 1664 1664 1626 1626 1626 1626 1626 1446 1446 1446 1446 1446 0/158 0/158 0/158 0/158 0/156 0/156 0/156 0/156 0/156 0/152 0/152 0/152 0/152 0/152 12/5 12/5 12/5 12/5 11/5 11/5 11/5 11/5 11/5 10/4 10/4 10/4 10/4 10/4 0/079 0/079 0/079 0/079 0/075 0/075 0/075 0/075 0/075 0/062 0/062 0/062 0/062 1 1 0/9 0/93 1 0/59 1 1 0/7 1 49001 1771 84260 115269 3435 8758 41328 6471 5926 31415 1159 621 54496 80223 1574 84659 30593 3901 22205 40 44 1806 707 96 2750 2520 1394 238 932 81 81 81 82 82 82 83 83 83 83 -!" ./( 089/:(7 ;<%(= )?@A' 08B !" &(+, ./( 089/:(7 ;"% ;<%(= )?@A' 08B !" &(+, ./( 089/:(7 ;"% ;<%(= 149 *+,-+ .3+GH7? I:J 3/ 89: ;<=> ,? ,@A B+9C DEE<F HHI Inflation Unemployment GDPgrowth Efficiency Asset loan NPL year 1446 0/152 10/4 0/062 1 6780 4767 5 83 1309 0/104 11/5 0/067 0/72 335151 191281 6402 84 84 #$% 1309 0/104 11/5 0/067 0/52 1 23939 204844 22689 148696 15843 84 &'()* 1309 0/104 11/5 0/067 0/91 231325 27815 6045 84 -!" 1309 0/104 11/5 0/067 0/067 0/88 1 120043 87934 11256 84 84 34(567 1309 0/104 11/5 0/067 0/72 12555 7180 579 84 089/:(7 1309 0/104 11/5 0/067 1 81060 57765 1508 84 ;<%(= 1309 0/104 11/5 0/067 1 14552 9558 15 84 1226 0/119 11/3 0/064 0/79 407862 245643 21156 85 85 #$% 1226 0/119 11/3 0/064 0/6 301415 242029 178046 24727 15058 85 &'()* 1226 0/119 11/3 0/064 0/93 294622 35993 17166 85 -!" 1226 0/119 11/3 0/064 0/94 1 122800 14523 101075 13838 85 85 34(567 1226 0/119 11/3 0/064 0/87 18210 11049 1575 85 089/:(7 1226 0/119 11/3 0/064 1 117316 76764 7103 85 ;<%(= 1226 0/119 11/3 0/064 1 41341 28512 1266 85 1240 0/184 10/5 0/064 0/88 510666 346895 27824 86 86 #$% 1240 0/184 10/5 0/064 0/55 370943 294898 215623 37050 23594 86 &'()* 1240 0/184 10/5 0/064 0/98 384591 289099 19070 86 -!" 1240 0/184 10/5 0/064 0/95 163882 116217 7384 86 86 012" 34(567 1240 0/184 10/5 0/064 0/88 25735 17354 1635 86 089/:(7 1240 0/184 10/5 0/064 1 162213 107050 17846 86 ;<%(= 1309 1309 1309 1309 1309 1226 1226 1226 1226 1226 1240 1240 1240 1240 1240 0/104 0/104 0/104 0/104 0/104 0/119 0/119 0/119 0/119 0/119 0/184 0/184 0/184 0/184 0/184 11/5 11/5 11/5 11/5 11/5 11/3 11/3 11/3 11/3 11/3 10/5 10/5 10/5 10/5 10/5 0/067 0/067 0/067 0/067 0/064 0/064 0/064 0/064 0/064 0/064 0/064 0/064 0/064 0/064 0/93 0/91 0/83 1 1 0/91 0/88 1 0/97 0/8 1 1 153216 44568 90423 14290 213982 53255 26209 233721 63454 173927 34846 106026 33647 66260 9784 157946 35867 10786 15269 171722 40160 14099 23989 6165 3058 5474 3346 339 8145 21307 9906 4333 561 21104 20972 10107 4359 1413 84 84 84 85 85 85 86 86 86 )?@A' 08B !" &(+, ./( 012" ;"% )?@A' 08B !" &(+, ./( 012" ;"% )?@A' 08B !" &(+, ./( ;"% 49 234' 5#/67' () *+,-+ ./01+ .#"#$%& ! 150 HHI Inflation Unemployment GDPgrowth Efficiency Asset loan NPL year 1240 0/184 10/5 0/064 1 74501 53612 3400 86 1203 0/254 10/4 0/060 0/89 538515 365657 25837 87 87 #$% 1203 0/254 10/4 0/060 0/58 404588 339914 46012 226670 26151 87 &'()* 1203 0/254 10/4 0/060 0/97 422678 303161 18181 87 -!" 1203 0/254 10/4 0/060 0/93 183346 189592 159830 17958 7869 87 87 34(567 1203 0/254 10/4 0/060 0/92 35249 22047 4356 87 089/:(7 1203 0/254 10/4 0/060 1 197467 12381 23070 87 ;<%(= 1203 0/254 10/4 0/060 1 101601 64164 12637 87 1203 1203 1203 1203 1203 0/254 0/254 0/254 0/254 0/254 10/4 10/4 10/4 10/4 10/4 0/060 0/060 0/060 0/060 0/060 0/99 0/96 0/85 0/98 0/97 218745 66986 42376 28448 45359 27311 39134 28461 10596 6008 7064 87 87 87 )?@A' 08B !" &(+, ./( 012" ;"% )?@A' 08B