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From: arg@ccvr1.cc.ncsu.edu (Ron Gallant) Newsgroups: comp.sources.misc Subject: v16i064: nlmdl - Estimate nonlinear statistical models, Part02/06 Message-ID: <1991Jan12.050546.8628@sparky.IMD.Sterling.COM> Date: 12 Jan 91 05:05:46 GMT Approved: kent@sparky.imd.sterling.com X-Checksum-Snefru: 6f34cb32 6a1e6f5f 64181494 7c1cfcab Submitted-by: arg@ccvr1.cc.ncsu.edu (Ron Gallant) Posting-number: Volume 16, Issue 64 Archive-name: nlmdl/part02 # This is a shell archive. Remove anything before this line, then unpack # it by saving it into a file and typing "sh file". To overwrite existing # files, type "sh file -c". You can also feed this as standard input via # unshar, or by typing "sh <file", e.g.. If this archive is complete, you # will see the following message at the end: # "End of archive 2 (of 6)." # Contents: ch5eg1/detail.bak ch5eg1/end3l.bak ch5eg1/ending.bak # ch6eg1/ending.bak ch6eg2/detail3l.bak display.cc nlmdl.h realmat.h # Wrapped by arg@sparc on Fri Jan 11 20:14:53 1991 PATH=/bin:/usr/bin:/usr/ucb ; export PATH if test -f 'ch5eg1/detail.bak' -a "${1}" != "-c" ; then echo shar: Will not clobber existing file \"'ch5eg1/detail.bak'\" else echo shar: Extracting \"'ch5eg1/detail.bak'\" $5719 characters$ sed "s/^X//" >'ch5eg1/detail.bak' <<'END_OF_FILE' XMissing observation at t = 142. XMissing observation at t = 170. XMissing observation at t = 172. XMissing observation at t = 193. XWarning, status::to, var is not initialized, tmp.dat incomplete. X X X ********************************************************************** X * * X * nlmdl 2.0 * X * * X ********************************************************************** X X X ********************************************************************** X * * X * Parameter settings * X * * X ********************************************************************** X X Gallant, "Nonlinear Statistical Models," Chapter 5, Figure 3, p. 324-326 X SUR What estimation method? Code SUR, TSLS, or GMM. X 224 Number of observations, t = 1, ..., n. X 2 Number of equations, i.e. dimension of e. X 0 Number of instruments, i.e. dimension of Z. X 8 Number of parameters, i.e. dimension of theta. X 50 Upper limit on Gauss-Newton iterations. X 1 Number var iterates, ivar=0 means none. X homoskedastic Code homoskedastic or heteroskedastic. X 0 Number of moving average terms MA for var estimate. X none Code none or Parzen, none when MA>0 is unwise. X 1.000000e-13 Convergence tolerance, tol=1.0e-8 is reasonable. X 1.000000e-10 Inversion tolerance, eps=1.0e-13 is reasonable X minimal How much output? Code none, minimal, or full. X X X ********************************************************************** X * * X * Starting theta * X * * X ********************************************************************** X X X Col 1 X X Row 1 -2.90000 X Row 2 -1.30000 X Row 3 0.820000 X Row 4 0.360000 X Row 5 -1.50000 X Row 6 -1.00000 X Row 7 -0.0300000 X Row 8 -0.470000 X X X ********************************************************************** X * * X * theta * X * * X ********************************************************************** X X X Col 1 X X Row 1 -2.92458 X Row 2 -1.28675 X Row 3 0.818570 X Row 4 0.361158 X Row 5 -1.53759 X Row 6 -1.04896 X Row 7 0.0300867 X Row 8 -0.467420 X X X X ********************************************************************** X * * X * var * X * (no degrees of freedom corrections) * X * * X ********************************************************************** X X X Col 1 Col 2 X X Row 1 0.164925 0.0920057 X Row 2 0.0920057 0.0896426 X X X X ********************************************************************** X * * X * V * X * (rank = 8) * X * * X ********************************************************************** X X X Col 1 Col 2 Col 3 Col 4 Col 5 Col 6 X X Row 1 0.0760485 -0.0394416 -0.00813632 0.00225515 0.0151611 -0.00086387 X Row 2 -0.0394416 0.0506098 0.00219396 0.00071347 -0.00244522 0.0122632 X Row 3 -0.00813632 0.00219396 0.00644155 0.00078138 -0.00603532 -0.00238599 X Row 4 0.00225515 0.00071347 0.00078138 0.00090344 -0.00048087 0.00101291 X Row 5 0.0151611 -0.00244522 -0.00603532 -0.00048087 0.00832137 0.00200847 X Row 6 -0.00086387 0.0122632 -0.00238599 0.00101291 0.00200847 0.00689444 X Row 7 -0.00293607 0.00321764 -0.00075345 -0.00079074 0.00110681 0.00015596 X Row 8 -0.00218205 0.00172663 0.00080830 0.00027549 -0.00115374 0.00049917 X X X Col 7 Col 8 X X Row 1 -0.00293607 -0.00218205 X Row 2 0.00321764 0.00172663 X Row 3 -0.00075345 0.00080830 X Row 4 -0.00079074 0.00027549 X Row 5 0.00110681 -0.00115374 X Row 6 0.00015596 0.00049917 X Row 7 0.00128616 -0.00024276 X Row 8 -0.00024276 0.00036532 X END_OF_FILE if test 5719 -ne `wc -c <'ch5eg1/detail.bak'`; then echo shar: \"'ch5eg1/detail.bak'\" unpacked with wrong size! fi # end of 'ch5eg1/detail.bak' fi if test -f 'ch5eg1/end3l.bak' -a "${1}" != "-c" ; then echo shar: Will not clobber existing file \"'ch5eg1/end3l.bak'\" else echo shar: Extracting \"'ch5eg1/end3l.bak'\" $5775 characters$ sed "s/^X//" >'ch5eg1/end3l.bak' <<'END_OF_FILE' XGallant, "Nonlinear Statistical Models," Chapter 5, Figure 3, p. 324-326 XSUR What estimation method? Code SUR, TSLS, or GMM. X224 Number of observations, t = 1, ..., n. X2 Number of equations, i.e. dimension of e. X0 Number of instruments, i.e. dimension of Z. X8 Number of parameters, i.e. dimension of theta. X50 Upper limit on Gauss-Newton iterations. X1 Number var iterates, ivar=0 means none. Xheteroskedastic Code homoskedastic or heteroskedastic. X3 Number of moving average terms MA for var estimate. XParzen Code none or Parzen, none when MA>0 is unwise. X1.000000e-13 Convergence tolerance, tol=1.0e-8 is reasonable. X1.000000e-10 Inversion tolerance, eps=1.0e-13 is reasonable Xfull How much output? Code none, minimal, or full. X8 Computed rank of V. Xuncorrected Divisor of var, corrected or uncorrected. X Blank line. X Blank line. X Blank line. X -2.9245814501616803e+00 -2.92458145 theta(1) X -1.2867460434063589e+00 -1.28674604 theta(2) X 8.1856990357452608e-01 0.81856990 theta(3) X 3.6115785200404138e-01 0.36115785 theta(4) X -1.5375885808695555e+00 -1.53758858 theta(5) X -1.0489590970263374e+00 -1.04895910 theta(6) X 3.0086700241162653e-02 0.03008670 theta(7) X -4.6742011797582117e-01 -0.46742012 theta(8) X 1.6492462994112111e-01 0.16492463 var(1,1) X 9.2005729031321579e-02 0.09200573 var(2,1) X 9.2005729031321579e-02 0.09200573 var(1,2) X 8.9642642316875812e-02 0.08964264 var(2,2) X 6.4151871786190293e-02 0.06415187 V(1,1) X -2.6808435093990508e-02 -0.02680844 V(2,1) X -1.0481302083617307e-02 -0.01048130 V(3,1) X 2.4179792658493493e-03 0.00241798 V(4,1) X 1.5271744257123141e-02 0.01527174 V(5,1) X 3.6217216495842341e-03 0.00362172 V(6,1) X -2.2745052058113957e-03 -0.00227451 V(7,1) X -2.2282353784641093e-03 -0.00222824 V(8,1) X -2.6808435093990942e-02 -0.02680844 V(1,2) X 3.9204445770723149e-02 0.03920445 V(2,2) X 3.5239211150354388e-03 0.00352392 V(3,2) X 1.9044116642894958e-03 0.00190441 V(4,2) X -2.5090660931227499e-03 -0.00250907 V(5,2) X 9.9860387081928022e-03 0.00998604 V(6,2) X 1.0763522857786497e-03 0.00107635 V(7,2) X 1.4608657346846976e-03 0.00146087 V(8,2) X -1.0481302083617045e-02 -0.01048130 V(1,3) X 3.5239211150353313e-03 0.00352392 V(2,3) X 5.9499339084749959e-03 0.00594993 V(3,3) X 3.8379282539160748e-04 0.00038379 V(4,3) X -5.5488697133901426e-03 -0.00554887 V(5,3) X -2.3181283618853221e-03 -0.00231813 V(6,3) X -3.0995472128008387e-04 -0.00030995 V(7,3) X 7.9619528555448433e-04 0.00079620 V(8,3) X 2.4179792658493345e-03 0.00241798 V(1,4) X 1.9044116642895203e-03 0.00190441 V(2,4) X 3.8379282539158981e-04 0.00038379 V(3,4) X 1.1185997609060533e-03 0.00111860 V(4,4) X -8.2948235409772357e-05 -0.00008295 V(5,4) X 1.8128096548391446e-03 0.00181281 V(6,4) X -8.6907934492451726e-04 -0.00086908 V(7,4) X 2.3704685064746336e-04 0.00023705 V(8,4) X 1.5271744257122990e-02 0.01527174 V(1,5) X -2.5090660931226623e-03 -0.00250907 V(2,5) X -5.5488697133901886e-03 -0.00554887 V(3,5) X -8.2948235409779458e-05 -0.00008295 V(4,5) X 7.2303542854823596e-03 0.00723035 V(5,5) X 2.4292056516279819e-03 0.00242921 V(6,5) X 6.6272949286796750e-04 0.00066273 V(7,5) X -1.0628753376875112e-03 -0.00106288 V(8,5) X 3.6217216495840446e-03 0.00362172 V(1,6) X 9.9860387081929288e-03 0.00998604 V(2,6) X -2.3181283618853659e-03 -0.00231813 V(3,6) X 1.8128096548391255e-03 0.00181281 V(4,6) X 2.4292056516280035e-03 0.00242921 V(5,6) X 6.9725212588786700e-03 0.00697252 V(6,6) X -8.0090995756335992e-04 -0.00080091 V(7,6) X 2.8957023926462434e-04 0.00028957 V(8,6) X -2.2745052058114465e-03 -0.00227451 V(1,7) X 1.0763522857786777e-03 0.00107635 V(2,7) X -3.0995472128007016e-04 -0.00030995 V(3,7) X -8.6907934492452008e-04 -0.00086908 V(4,7) X 6.6272949286796273e-04 0.00066273 V(5,7) X -8.0090995756337260e-04 -0.00080091 V(6,7) X 1.1126181202496821e-03 0.00111262 V(7,7) X -2.5799316642020757e-04 -0.00025799 V(8,7) X -2.2282353784641279e-03 -0.00222824 V(1,8) X 1.4608657346847011e-03 0.00146087 V(2,8) X 7.9619528555449908e-04 0.00079620 V(3,8) X 2.3704685064746418e-04 0.00023705 V(4,8) X -1.0628753376875236e-03 -0.00106288 V(5,8) X 2.8957023926462423e-04 0.00028957 V(6,8) X -2.5799316642020887e-04 -0.00025799 V(7,8) X 3.6558708385757817e-04 0.00036559 V(8,8) X 7.1212458944209244e-09 0.00000001 D(1) X -9.5291010557382897e-09 -0.00000001 D(2) X -1.5778368693623276e-09 -0.00000000 D(3) X -6.4433263552306315e-10 -0.00000000 D(4) X 1.5722264246975941e-09 0.00000000 D(5) X -2.4312439036197547e-09 -0.00000000 D(6) X -1.5845329475213192e-10 -0.00000000 D(7) X -8.7707819833213637e-10 -0.00000000 D(8) X 4.4685695288184939e+02 446.85695288 obj END_OF_FILE if test 5775 -ne `wc -c <'ch5eg1/end3l.bak'`; then echo shar: \"'ch5eg1/end3l.bak'\" unpacked with wrong size! fi # end of 'ch5eg1/end3l.bak' fi if test -f 'ch5eg1/ending.bak' -a "${1}" != "-c" ; then echo shar: Will not clobber existing file \"'ch5eg1/ending.bak'\" else echo shar: Extracting \"'ch5eg1/ending.bak'\" $5775 characters$ sed "s/^X//" >'ch5eg1/ending.bak' <<'END_OF_FILE' XGallant, "Nonlinear Statistical Models," Chapter 5, Figure 3, p. 324-326 XSUR What estimation method? Code SUR, TSLS, or GMM. X224 Number of observations, t = 1, ..., n. X2 Number of equations, i.e. dimension of e. X0 Number of instruments, i.e. dimension of Z. X8 Number of parameters, i.e. dimension of theta. X50 Upper limit on Gauss-Newton iterations. X1 Number var iterates, ivar=0 means none. Xhomoskedastic Code homoskedastic or heteroskedastic. X0 Number of moving average terms MA for var estimate. Xnone Code none or Parzen, none when MA>0 is unwise. X1.000000e-13 Convergence tolerance, tol=1.0e-8 is reasonable. X1.000000e-10 Inversion tolerance, eps=1.0e-13 is reasonable Xminimal How much output? Code none, minimal, or full. X8 Computed rank of V. Xuncorrected Divisor of var, corrected or uncorrected. X Blank line. X Blank line. X Blank line. X -2.9245814501616803e+00 -2.92458145 theta(1) X -1.2867460434063589e+00 -1.28674604 theta(2) X 8.1856990357452608e-01 0.81856990 theta(3) X 3.6115785200404138e-01 0.36115785 theta(4) X -1.5375885808695555e+00 -1.53758858 theta(5) X -1.0489590970263374e+00 -1.04895910 theta(6) X 3.0086700241162653e-02 0.03008670 theta(7) X -4.6742011797582117e-01 -0.46742012 theta(8) X 1.6492462994112111e-01 0.16492463 var(1,1) X 9.2005729031321579e-02 0.09200573 var(2,1) X 9.2005729031321579e-02 0.09200573 var(1,2) X 8.9642642316875812e-02 0.08964264 var(2,2) X 7.6048540298630313e-02 0.07604854 V(1,1) X -3.9441553460601381e-02 -0.03944155 V(2,1) X -8.1363244658375945e-03 -0.00813632 V(3,1) X 2.2551454088996347e-03 0.00225515 V(4,1) X 1.5161071040894071e-02 0.01516107 V(5,1) X -8.6386978244942293e-04 -0.00086387 V(6,1) X -2.9360670383682128e-03 -0.00293607 V(7,1) X -2.1820501983515709e-03 -0.00218205 V(8,1) X -3.9441553460601381e-02 -0.03944155 V(1,2) X 5.0609776285336930e-02 0.05060978 V(2,2) X 2.1939598380520749e-03 0.00219396 V(3,2) X 7.1346736728890531e-04 0.00071347 V(4,2) X -2.4452170498858623e-03 -0.00244522 V(5,2) X 1.2263248643774908e-02 0.01226325 V(6,2) X 3.2176432662395357e-03 0.00321764 V(7,2) X 1.7266330377801262e-03 0.00172663 V(8,2) X -8.1363244658375945e-03 -0.00813632 V(1,3) X 2.1939598380520749e-03 0.00219396 V(2,3) X 6.4415535695106418e-03 0.00644155 V(3,3) X 7.8138028913347761e-04 0.00078138 V(4,3) X -6.0353186597764865e-03 -0.00603532 V(5,3) X -2.3859940684536310e-03 -0.00238599 V(6,3) X -7.5345196703219372e-04 -0.00075345 V(7,3) X 8.0829924747999706e-04 0.00080830 V(8,3) X 2.2551454088996347e-03 0.00225515 V(1,4) X 7.1346736728890531e-04 0.00071347 V(2,4) X 7.8138028913347761e-04 0.00078138 V(3,4) X 9.0343959846035443e-04 0.00090344 V(4,4) X -4.8087021200371650e-04 -0.00048087 V(5,4) X 1.0129070210761102e-03 0.00101291 V(6,4) X -7.9073548924408663e-04 -0.00079074 V(7,4) X 2.7549163836087631e-04 0.00027549 V(8,4) X 1.5161071040894071e-02 0.01516107 V(1,5) X -2.4452170498858623e-03 -0.00244522 V(2,5) X -6.0353186597764865e-03 -0.00603532 V(3,5) X -4.8087021200371650e-04 -0.00048087 V(4,5) X 8.3213669176229327e-03 0.00832137 V(5,5) X 2.0084691585193232e-03 0.00200847 V(6,5) X 1.1068054871987483e-03 0.00110681 V(7,5) X -1.1537431897854821e-03 -0.00115374 V(8,5) X -8.6386978244942293e-04 -0.00086387 V(1,6) X 1.2263248643774908e-02 0.01226325 V(2,6) X -2.3859940684536310e-03 -0.00238599 V(3,6) X 1.0129070210761102e-03 0.00101291 V(4,6) X 2.0084691585193232e-03 0.00200847 V(5,6) X 6.8944369024945473e-03 0.00689444 V(6,6) X 1.5596012364267730e-04 0.00015596 V(7,6) X 4.9917336963915457e-04 0.00049917 V(8,6) X -2.9360670383682128e-03 -0.00293607 V(1,7) X 3.2176432662395357e-03 0.00321764 V(2,7) X -7.5345196703219372e-04 -0.00075345 V(3,7) X -7.9073548924408663e-04 -0.00079074 V(4,7) X 1.1068054871987483e-03 0.00110681 V(5,7) X 1.5596012364267730e-04 0.00015596 V(6,7) X 1.2861607343363046e-03 0.00128616 V(7,7) X -2.4275726609718795e-04 -0.00024276 V(8,7) X -2.1820501983515709e-03 -0.00218205 V(1,8) X 1.7266330377801262e-03 0.00172663 V(2,8) X 8.0829924747999706e-04 0.00080830 V(3,8) X 2.7549163836087631e-04 0.00027549 V(4,8) X -1.1537431897854821e-03 -0.00115374 V(5,8) X 4.9917336963915457e-04 0.00049917 V(6,8) X -2.4275726609718795e-04 -0.00024276 V(7,8) X 3.6531994032499421e-04 0.00036532 V(8,8) X 7.1212458944209244e-09 0.00000001 D(1) X -9.5291010557382897e-09 -0.00000001 D(2) X -1.5778368693623276e-09 -0.00000000 D(3) X -6.4433263552306315e-10 -0.00000000 D(4) X 1.5722264246975941e-09 0.00000000 D(5) X -2.4312439036197547e-09 -0.00000000 D(6) X -1.5845329475213192e-10 -0.00000000 D(7) X -8.7707819833213637e-10 -0.00000000 D(8) X 4.4685695288184939e+02 446.85695288 obj END_OF_FILE if test 5775 -ne `wc -c <'ch5eg1/ending.bak'`; then echo shar: \"'ch5eg1/ending.bak'\" unpacked with wrong size! fi # end of 'ch5eg1/ending.bak' fi if test -f 'ch6eg1/ending.bak' -a "${1}" != "-c" ; then echo shar: Will not clobber existing file \"'ch6eg1/ending.bak'\" else echo shar: Extracting \"'ch6eg1/ending.bak'\" $5772 characters$ sed "s/^X//" >'ch6eg1/ending.bak' <<'END_OF_FILE' XGallant, "Nonlinear Statistical Models," Chapter 6, Figure 1, p. 436. XTSLS What estimation method? Code SUR, TSLS, or GMM. X220 Number of observations, t = 1, ..., n. X2 Number of equations, i.e. dimension of e. X17 Number of instruments, i.e. dimension of Z. X8 Number of parameters, i.e. dimension of theta. X20 Upper limit on Gauss-Newton iterations. X1 Number var iterates, ivar=0 means none. Xhomoskedastic Code homoskedastic or heteroskedastic. X0 Number of moving average terms MA for var estimate. Xnone Code none or Parzen, none when MA>0 is unwise. X1.000000e-08 Convergence tolerance, tol=1.0e-8 is reasonable. X1.000000e-10 Inversion tolerance, eps=1.0e-13 is reasonable Xfull How much output? Code none, minimal, or full. X8 Computed rank of V. Xuncorrected Divisor of var, corrected or uncorrected. X Blank line. X Blank line. X Blank line. X -2.1378778275860273e+00 -2.13787783 theta(1) X -1.9893911556433312e+00 -1.98939116 theta(2) X 7.0939153916745556e-01 0.70939154 theta(3) X 3.3663399323270310e-01 0.33663399 theta(4) X -1.4019968694481941e+00 -1.40199687 theta(5) X -1.1388969627624823e+00 -1.13889696 theta(6) X 2.9131488559477250e-02 0.02913149 theta(7) X -5.0050229673416280e-01 -0.50050230 theta(8) X 1.6847307354905444e-01 0.16847307 var(1,1) X 9.4991006557633997e-02 0.09499101 var(2,1) X 9.4991006557633997e-02 0.09499101 var(1,2) X 9.3711564655296287e-02 0.09371156 var(2,2) X 3.4123357521137065e-01 0.34123358 V(1,1) X -4.0515026192173231e-01 -0.40515026 V(2,1) X -7.4443670981270527e-02 -0.07444367 V(3,1) X -1.7075137202523048e-02 -0.01707514 V(4,1) X 7.3616408974018430e-02 0.07361641 V(5,1) X -8.0520944995638929e-02 -0.08052094 V(6,1) X -1.3988025621517654e-02 -0.01398803 V(7,1) X -2.2077927314748533e-02 -0.02207793 V(8,1) X -4.0515026192173231e-01 -0.40515026 V(1,2) X 5.6591782097119825e-01 0.56591782 V(2,2) X 9.3113487046268872e-02 0.09311349 V(3,2) X 2.8410019934882371e-02 0.02841002 V(4,2) X -8.0617530766411080e-02 -0.08061753 V(5,2) X 1.2644653136927941e-01 0.12644653 V(6,2) X 1.9325013502098160e-02 0.01932501 V(7,2) X 2.9896132263088925e-02 0.02989613 V(8,2) X -7.4443670981270527e-02 -0.07444367 V(1,3) X 9.3113487046268872e-02 0.09311349 V(2,3) X 2.4067454001684946e-02 0.02406745 V(3,3) X 5.7835699332500186e-03 0.00578357 V(4,3) X -2.1581815564116969e-02 -0.02158182 V(5,3) X 1.7403224408106693e-02 0.01740322 V(6,3) X 1.9150094956931823e-03 0.00191501 V(7,3) X 5.9076727860237991e-03 0.00590767 V(8,3) X -1.7075137202523048e-02 -0.01707514 V(1,4) X 2.8410019934882371e-02 0.02841002 V(2,4) X 5.7835699332500186e-03 0.00578357 V(3,4) X 2.5487432759845568e-03 0.00254874 V(4,4) X -4.7950486640943122e-03 -0.00479505 V(5,4) X 7.3439708547267122e-03 0.00734397 V(6,4) X -5.1094810414861448e-05 -0.00005109 V(7,4) X 1.8674253117946520e-03 0.00186743 V(8,4) X 7.3616408974018430e-02 0.07361641 V(1,5) X -8.0617530766411080e-02 -0.08061753 V(2,5) X -2.1581815564116969e-02 -0.02158182 V(3,5) X -4.7950486640943122e-03 -0.00479505 V(4,5) X 2.2762057613803403e-02 0.02276206 V(5,5) X -1.5195066143983841e-02 -0.01519507 V(6,5) X -9.9415605596577862e-04 -0.00099416 V(7,5) X -5.6251801394123823e-03 -0.00562518 V(8,5) X -8.0520944995638929e-02 -0.08052094 V(1,6) X 1.2644653136927941e-01 0.12644653 V(2,6) X 1.7403224408106693e-02 0.01740322 V(3,6) X 7.3439708547267122e-03 0.00734397 V(4,6) X -1.5195066143983841e-02 -0.01519507 V(5,6) X 3.3346276344180567e-02 0.03334628 V(6,6) X 3.7331467729901690e-03 0.00373315 V(7,6) X 6.9269333322694495e-03 0.00692693 V(8,6) X -1.3988025621517654e-02 -0.01398803 V(1,7) X 1.9325013502098160e-02 0.01932501 V(2,7) X 1.9150094956931823e-03 0.00191501 V(3,7) X -5.1094810414861448e-05 -0.00005109 V(4,7) X -9.9415605596577862e-04 -0.00099416 V(5,7) X 3.7331467729901690e-03 0.00373315 V(6,7) X 2.0417788436814010e-03 0.00204178 V(7,7) X 6.0905830809060847e-04 0.00060906 V(8,7) X -2.2077927314748533e-02 -0.02207793 V(1,8) X 2.9896132263088925e-02 0.02989613 V(2,8) X 5.9076727860237991e-03 0.00590767 V(3,8) X 1.8674253117946520e-03 0.00186743 V(4,8) X -5.6251801394123823e-03 -0.00562518 V(5,8) X 6.9269333322694495e-03 0.00692693 V(6,8) X 6.0905830809060847e-04 0.00060906 V(7,8) X 2.0036106220501352e-03 0.00200361 V(8,8) X 1.6787206308357473e-07 0.00000017 D(1) X -2.4625529712808257e-07 -0.00000025 D(2) X -4.6504414197150224e-08 -0.00000005 D(3) X -1.4610224422905096e-08 -0.00000001 D(4) X 4.0400010629413197e-08 0.00000004 D(5) X -5.8821932254927893e-08 -0.00000006 D(6) X -8.2632941559150507e-09 -0.00000001 D(7) X -1.6640001385280539e-08 -0.00000002 D(8) X 3.5611514196184793e+01 35.61151420 obj END_OF_FILE if test 5772 -ne `wc -c <'ch6eg1/ending.bak'`; then echo shar: \"'ch6eg1/ending.bak'\" unpacked with wrong size! fi # end of 'ch6eg1/ending.bak' fi if test -f 'ch6eg2/detail3l.bak' -a "${1}" != "-c" ; then echo shar: Will not clobber existing file \"'ch6eg2/detail3l.bak'\" else echo shar: Extracting \"'ch6eg2/detail3l.bak'\" $7043 characters$ sed "s/^X//" >'ch6eg2/detail3l.bak' <<'END_OF_FILE' X X X ********************************************************************** X * * X * nlmdl 2.0 * X * * X ********************************************************************** X X X ********************************************************************** X * * X * Parameter settings * X * * X ********************************************************************** X X Gallant, "Nonlinear Statistical Models," Chapter 6, Figure 2, p. 449-450. X GMM What estimation method? Code SUR, TSLS, or GMM. X 238 Number of observations, t = 1, ..., n. X 1 Number of equations, i.e. dimension of e. X 3 Number of instruments, i.e. dimension of Z. X 2 Number of parameters, i.e. dimension of theta. X 20 Upper limit on Gauss-Newton iterations. X 1 Number var iterates, ivar=0 means none. X heteroskedastic Code homoskedastic or heteroskedastic. X 3 Number of moving average terms MA for var estimate. X Parzen Code none or Parzen, none when MA>0 is unwise. X 1.000000e-05 Convergence tolerance, tol=1.0e-8 is reasonable. X 1.000000e-13 Inversion tolerance, eps=1.0e-13 is reasonable X full How much output? Code none, minimal, or full. X X X ********************************************************************** X * * X * Starting theta * X * * X ********************************************************************** X X X Col 1 X X Row 1 -0.400000 X Row 2 0.900000 X Xvar_loop 0 X 2.3800000000000000e+02 238.00000000 var(1,1) X 2.3848513503508150e+02 238.48513504 var(2,1) X 2.3866807009536583e+02 238.66807010 var(3,1) X 2.3848513503508150e+02 238.48513504 var(1,2) X 2.3897620940526255e+02 238.97620941 var(2,2) X 2.3916481122626234e+02 239.16481123 var(3,2) X 2.3866807009536583e+02 238.66807010 var(1,3) X 2.3916481122626234e+02 239.16481123 var(2,3) X 2.3975964092305995e+02 239.75964092 var(3,3) X Xtheta_loop 0 X -4.0000000000000002e-01 -0.40000000 theta(1) X 9.0000000000000002e-01 0.90000000 theta(2) X 2.2977593855825589e+00 2.29775939 obj X -4.9910043429446205e-01 -0.49910043 D(1) X 9.8933364963875864e-02 0.09893336 D(2) XStep length = 1 X Xtheta_loop 1 X -8.9910043429446207e-01 -0.89910043 theta(1) X 9.9893336496387586e-01 0.99893336 theta(2) X 2.7751760221041621e-03 0.00277518 obj X 5.0366043831498272e-02 0.05036604 D(1) X -4.4414610501568677e-06 -0.00000444 D(2) XStep length = 1 X Xtheta_loop 2 X -8.4873439046296384e-01 -0.84873439 theta(1) X 9.9892892350282569e-01 0.99892892 theta(2) X 2.7717868638761308e-03 0.00277179 obj X -1.1734797022229638e-04 -0.00011735 D(1) X 2.1096605255709332e-07 0.00000021 D(2) XStep length = 1 X Xtheta_loop 3 X -8.4885173843318618e-01 -0.84885174 theta(1) X 9.9892913446887821e-01 0.99892913 theta(2) X 2.7717868580610532e-03 0.00277179 obj X 2.6798358348564710e-07 0.00000027 D(1) X -5.5350439549602871e-10 -0.00000000 D(2) XTolerence check passed. X Xvar_loop 1 X 4.3978220854904504e-01 0.43978221 var(1,1) X 4.4050039895600307e-01 0.44050040 var(2,1) X 4.3420956263910121e-01 0.43420956 var(3,1) X 4.4050039895600307e-01 0.44050040 var(1,2) X 4.4122700259798669e-01 0.44122700 var(2,2) X 4.3493663977747488e-01 0.43493664 var(3,2) X 4.3420956263910121e-01 0.43420956 var(1,3) X 4.3493663977747488e-01 0.43493664 var(2,3) X 4.2967081964723475e-01 0.42967082 var(3,3) X Xtheta_loop 0 X -8.4885173843318618e-01 -0.84885174 theta(1) X 9.9892913446887821e-01 0.99892913 theta(2) X 1.2014636301992196e+00 1.20146363 obj X -6.4020486065461402e-03 -0.00640205 D(1) X -8.8353145570584189e-04 -0.00088353 D(2) XStep length = 1 X Xtheta_loop 1 X -8.5525378703973232e-01 -0.85525379 theta(1) X 9.9804560301317236e-01 0.99804560 theta(2) X 1.0861341198092420e+00 1.08613412 obj X 2.6747016119385693e-06 0.00000267 D(1) X -1.7073275785211286e-08 -0.00000002 D(2) XTolerence check passed. X X X ********************************************************************** X * * X * theta * X * * X ********************************************************************** X X X Col 1 X X Row 1 -0.855254 X Row 2 0.998046 X X X X ********************************************************************** X * * X * var * X * (no degrees of freedom corrections) * X * * X ********************************************************************** X X X Col 1 Col 2 Col 3 X X Row 1 0.439782 0.440500 0.434210 X Row 2 0.440500 0.441227 0.434937 X Row 3 0.434210 0.434937 0.429671 X X X X ********************************************************************** X * * X * V * X * (rank = 2) * X * * X ********************************************************************** X X X Col 1 Col 2 X X Row 1 3.60663 -0.00715768 X Row 2 -0.00715768 2.117e-05 X END_OF_FILE if test 7043 -ne `wc -c <'ch6eg2/detail3l.bak'`; then echo shar: \"'ch6eg2/detail3l.bak'\" unpacked with wrong size! fi # end of 'ch6eg2/detail3l.bak' fi if test -f 'display.cc' -a "${1}" != "-c" ; then echo shar: Will not clobber existing file \"'display.cc'\" else echo shar: Extracting \"'display.cc'\" $4567 characters$ sed "s/^X//" >'display.cc' <<'END_OF_FILE' X/* ---------------------------------------------------------------------------- X Xnlmdl: display.cc X Xnlmdl is a C++ implementation of the statistical methods in A. Ronald XGallant, "Nonlinear Statistical Models," New York: John Wiley and Sons, X1987, ISBN 0-471-80260-3, using a matrix class realmat that is distributed Xwith it. The header files nlmdl.h and realmat.h describe the use of the Xprogram and matrix class, respectively. X XCopyright (C) 1990. X XA. Ronald Gallant XP.O. Box 5513 XRaleigh NC 27650-5513 XUSA X XPermission to use, copy, modify, and distribute this software and its Xdocumentation for any purpose and without fee is hereby granted, provided Xthat the above copyright notice appear in all copies and that both that Xcopyright notice and this permission notice appear in supporting Xdocumentation. X XThis software is provided "as is" without any expressed or implied warranty. X X---------------------------------------------------------------------------- */ X/* display.cc is a piece of status.cc that was split */ X/* out to reduce Turbo C++ memory requirements. */ X X#include "status.h" X Xint status::display(display_mode mode) X{ X X INTEGER i,j,len; X char temp[MAX_STATUS_LINE]; X char* pad = " "; X X X switch (mode) { X X case START_UP: X X cout << starbox("/Parameter settings//_") << "\n"; X X cout<<pad<< switches << "\n"; X X sprintf(temp,"%-19s", method); X cout<<pad<<temp<< "What estimation method? Code SUR, TSLS, or GMM.\n"; X X sprintf(temp,"%-19d", n); X cout<<pad<<temp<< "Number of observations, t = 1, ..., n.\n"; X X sprintf(temp,"%-19d",M); X cout<<pad<<temp<< "Number of equations, i.e. dimension of e.\n"; X X sprintf(temp,"%-19d",K); X cout<<pad<<temp<<"Number of instruments, i.e. dimension of Z.\n"; X X sprintf(temp,"%-19d",p); X cout<<pad<<temp<<"Number of parameters, i.e. dimension of theta.\n"; X X sprintf(temp,"%-19d",itheta); X cout<<pad<<temp<< "Upper limit on Gauss-Newton iterations.\n"; X X sprintf(temp,"%-19d",ivar); X cout<<pad<<temp<< "Number var iterates, ivar=0 means none.\n"; X X sprintf(temp,"%-19s",vartype); X cout<<pad<<temp<< "Code homoskedastic or heteroskedastic.\n"; X X sprintf(temp,"%-19d",MA); X cout<<pad<<temp<< "Number of moving average terms MA for var estimate.\n"; X X sprintf(temp,"%-19s",weights); X cout<<pad<<temp<< "Code none or Parzen, none when MA>0 is unwise.\n"; X X sprintf(temp,"%-19.6e",tol); X cout<<pad<<temp<< "Convergence tolerance, tol=1.0e-8 is reasonable.\n"; X X sprintf(temp,"%-19.6e",eps); X cout<<pad<<temp<< "Inversion tolerance, eps=1.0e-13 is reasonable\n"; X X sprintf(temp,"%-19s",detail); X cout<<pad<<temp<< "How much output? Code none, minimal, or full.\n"; X X break; X X case VAR_ITERATE: X X if ( rows(var) > 0 && cols(var) > 0 ){ X if ( strcmp(method,"SUR") == 0 || strcmp(method,"TSLS") == 0){ X for (j=1; j<=M; j++){ X for (i=1; i<=M; i++){ X sprintf(temp,"%25.16e % 20.8f ",var.check2(i,j),var.elem(i,j)); X cout<<temp<< " var(" << i << "," << j << ")\n"; X } X } X } X else{ X len = M*K; X for (j=1; j<=len; j++){ X for (i=1; i<=len; i++){ X sprintf(temp,"%25.16e % 20.8f ",var.check2(i,j),var.elem(i,j)); X cout<<temp<< " var(" << i << "," << j << ")\n"; X } X } X } X } X else{ X cout << "Error, status::display, var is not initialized.\n"; X } X X break; X X case THETA_ITERATE: X X if ( rows(theta) > 0 && cols(theta) > 0 ){ X for (i=1; i<=p; i++){ X sprintf(temp,"%25.16e % 20.8f ",theta[i],theta[i]); X cout<<temp<< " theta(" << i << ")\n"; X } X } X else{ X cout << "Error, status::display, theta is not initialized.\n"; X } X X sprintf(temp,"%25.16e % 20.8f ",obj,obj); X cout<<temp<< " obj\n"; X X if (strcmp(detail,"full")!=0) break; X X if ( rows(D) > 0 && cols(D) > 0 ){ X for (i=1; i<=p; i++){ X sprintf(temp,"%25.16e % 20.8f ",D[i],D[i]); X cout<<temp<< " D(" << i << ")\n"; X } X } X else{ X cout << "Error, status::display, D is not initialized.\n"; X } X X break; X X case TERMINATION: X X cout << starbox("/theta//_") << theta << "\n"; X X if (strcmp(df,"corrected") == 0) X cout << starbox("/var/(corrected for degrees of freedom)//_"); X else X cout << starbox("/var/(no degrees of freedom corrections)//_"); X X cout << var << "\n"; X X sprintf(temp,"/V/(rank = %d)//_",rank); X cout << starbox(temp) << V << "\n"; X X break; X X } X X cout.flush(); X X return 0; X X} END_OF_FILE if test 4567 -ne `wc -c <'display.cc'`; then echo shar: \"'display.cc'\" unpacked with wrong size! fi # end of 'display.cc' fi if test -f 'nlmdl.h' -a "${1}" != "-c" ; then echo shar: Will not clobber existing file \"'nlmdl.h'\" else echo shar: Extracting \"'nlmdl.h'\" $5856 characters$ sed "s/^X//" >'nlmdl.h' <<'END_OF_FILE' X/* --------------------------------------------------------------------------- X Xnlmdl: nlmdl.h X Xnlmdl is a C++ implementation of the statistical methods in A. Ronald XGallant, "Nonlinear Statistical Models," New York: John Wiley and Sons, X1987, ISBN 0-471-80260-3, using a matrix class realmat that is distributed Xwith it. The header files nlmdl.h and realmat.h describe the use of the Xprogram and matrix class, respectively. X XCopyright (C) 1990. X XA. Ronald Gallant XP.O. Box 5513 XRaleigh NC 27650-5513 XUSA X XPermission to use, copy, modify, and distribute this software and its Xdocumentation for any purpose and without fee is hereby granted, provided Xthat the above copyright notice appear in all copies and that both that Xcopyright notice and this permission notice appear in supporting Xdocumentation. X XThis software is provided "as is" without any expressed or implied warranty. X X----------------------------------------------------------------------------- X XThis header describes the program nlmdl.cc for estimating theta of the model Xe=q(t,theta). The user supples a file named starting.dat whose contents are Xdescribed by the template: X X switches This line is passed to class model as a string. X method What estimation method? Code SUR, TSLS, or GMM. X n Number of observations, t = 1, ..., n. X M Number of equations in the system, i.e. dimension of e. X K Number of instruments, i.e. dimension of Z, code K=0 with SUR. X p Number of parameters, i.e. dimension of theta. X itheta Upper limit on Gauss-Newton iterations. X ivar Number of var iterates, ivar=0 means none. X vartype Code homoskedastic or heteroskedastic. X MA Number of moving average terms for var estimate, code MA=0 if e iid. X weights Code none or Parzen, coding none when MA>0 is asking for trouble. X tol Convergence tolerance, tol=1.0e-8 is reasonable. X eps Inversion tolerance, eps=1.0e-13 is reasonable (if REAL is double). X detail How much output? Code none, minimal, or full. X Blank line. X Blank line. X Blank line. X Blank line. X Blank line. X theta(1) Starting values for theta. These must be supplied. X X to X X theta(p) X var(1) Starting values for variance estimate. These can be omitted. X Matrix is stored columnwise. If method = SUR or TSLS then then X to l=M and var corresponds to C(e,e'). If method = GMM then l=M*K X and var corresponds to the variance of sum on t of e Kronecker X var(l*l) product Z. X X XSee class status for more detail, especially status::from. The file Xstarting.dat is read twice. At the first reading it must contain at least Xthe first line, switches. Class model, described next, can supply entries Xfor the second reading. X XThe user also supplies the class model as files model.h and model.cc. The Xclass model is declared in model.h which should match the following template: X X #include "status.h" X external status s; X X class model X { X protected: X //... X X public: X model(); X ~model(); X realmat e_t; // e_t is s.M by 1 X realmat dele_t; // dele_t is s.M by s.p X realmat Z_t; // Z_t is s.K by 1, null if s.K = 0; X int initialize(); X int terminate(); X void e(INTEGER t); // e fills in e_t X void dele(INTEGER t); // dele fills in dele_t = del e_t wrt theta X void Z(INTEGER t); // Z fills in Z_t X } X XFor SUR, code the function Z as follows: X X realmat model::Z(INTEGER t) { } X XIn writing the class model, the relevant facts regarding program flow in Xnlmdl.cc are as follows: X X 1. First, s is tentatively filled in by reading starting.dat using X X status s; X s.from(s.starting); X X The primary purpose of this read is to get the first line from X starting.dat so as to make it available to model as the string X s.switches. Nonetheless, everything in starting.dat is read and X put in s. X X 2. Next, initialize() of class model is called. The function initialize() X can read starting.dat, can read other data, etc. Since all of class X status's data is public, any of it can be filled in or changed by X initialize(). One could, for example, fill in all of class status's X data and write it using X X s.to(s.starting); X X One could also switch to a different starting status file using X X s.starting="filename"; X X or a different ending status file using X X s.ending="filename"; X X If initialize() returns 0, execution stops. X X 3. Then, class status's data is definitively filled in by the call X X s.from(s.starting); X X and m.e_t, m.dele_t, and m.Z_t are dimensioned as X X m.e_t.resize(s.M,1); X m.dele_t.resize(s.M,s.p); X if (s.K > 0) m.Z_t.resize(s.K,1); X X and estimation proceeds using these settings and dimensions. X X 4. Finally, terminate() of class model is called; terminate() can read data, X write data, etc. If terminate() returns 0 execution stops, otherwise X initialize() is called again and the cycle repeats (from Step 2). One X can use this feature to loop over a grid of starting values. X X XReference: X XGallant, A. Ronald (1987), "Nonlinear Statistical Models," New York: John XWiley and Sons. ISBN 0-471-80260-3. X X--------------------------------------------------------------------------- */ X X X#ifndef __FILE_NLMDL_H_SEEN__ X#pragma once X#define __FILE_NLMDL_H_SEEN__ 1 X X#include "usual.h" //This header is also in status.h X#include "realmat.h" //This header is also in status.h X#include "tools.h" //This header is also in status.h X X#include "status.h" X#include "model.h" X X#define VERSION "2.0" X X#endif X END_OF_FILE if test 5856 -ne `wc -c <'nlmdl.h'`; then echo shar: \"'nlmdl.h'\" unpacked with wrong size! fi # end of 'nlmdl.h' fi if test -f 'realmat.h' -a "${1}" != "-c" ; then echo shar: Will not clobber existing file \"'realmat.h'\" else echo shar: Extracting \"'realmat.h'\" $5870 characters$ sed "s/^X//" >'realmat.h' <<'END_OF_FILE' X/* ---------------------------------------------------------------------------- X Xrealmat: realmat.h X Xrealmat is a C++ matrix class. The header file realmat.h describes its use. X XCopyright (C) 1990. X XA. Ronald Gallant XP.O. Box 5513 XRaleigh NC 27650-5513 XUSA X XPermission to use, copy, modify, and distribute this software and its Xdocumentation for any purpose and without fee is hereby granted, provided Xthat the above copyright notice appear in all copies and that both that Xcopyright notice and this permission notice appear in supporting Xdocumentation. X XThis software is provided "as is" without any expressed or implied warranty. X X------------------------------------------------------------------------------- X XThis header defines a matrix class. A matrix X with r rows, and c columns is Xconstructed using realmat X(r,c). X is stored columnwise with no wasted space Xwhich is to say that vec(X) is what is stored. Indexing of the elements starts Xwith 1, not zero. The protected members of realmat are rows, cols, len, and x. X XAs an illustration, suppose X is constructed using realmat X(r,c). Then XX.rows=r, X.cols=c, X.len=r*c, the pointer X.x points to the first element of Xvec(X), X[i] returns the i-th element of vec(X), i=1,...,X.len, X.elem(i,j) Xreturns the (i,j)-th element of X, i=1,...,X.rows, j=1,...,.X.cols. An element Xof X is of type REAL and X.rows, X.cols, and X.len are of type INTEGER. The Xtypedef of REAL and INTEGER is in usual.h. To allocate store at run time, use Xrealmat X; X.resize(r,c);. X XThe operators +, -, *, =, +=, -=, ++, --, ==, !=, <, <=, >, >= and << are Xdefined. T(X) transposes X. X.check1(i) and X.check2(i,j) access the elements Xof vec(X) and X respectively with range checking. invpsd(X) or invpsd(X,eps) Xreturns a g-inverse of a positive semi-definite, symmetric matrix X with Xminimal error checking; eps is the tolerance used in rank determination and Xdefaults to 1.e-13. rows(X) returns X.rows and cols(X) returns X.cols. The Xoperator ++ acts like ++X, not like X++; similarly for --. The operators ==, X<, <=, >, and >= evaluate to true if the matrices compared are the same size Xand the relation is true for each element; (A!=B) = !(A==B). X X-----------------------------------------------------------------------------*/ X X#ifndef __FILE_REALMAT_H_SEEN__ X#pragma once X#define __FILE_REALMAT_H_SEEN__ 1 X X#include "usual.h" X#include "tools.h" X Xclass realmat X{ Xprotected: X X INTEGER rows; X INTEGER cols; X INTEGER len; X REAL *x; X X realmat(INTEGER r, INTEGER c, REAL* a); X void resize(INTEGER r, INTEGER c, REAL* a); //expects this.x == 0 X X Xpublic: X realmat(); X X realmat(INTEGER r, INTEGER c); X realmat(INTEGER r, INTEGER c, REAL fill_value); X X realmat(realmat&); X X ~realmat(); X Xvoid resize(INTEGER r, INTEGER c); Xvoid resize(INTEGER r, INTEGER c, REAL fill_value); X Xrealmat& operator=(realmat& a); Xrealmat& operator+=(realmat& a); Xrealmat& operator-=(realmat& a); Xrealmat& operator++(); Xrealmat& operator--(); X XREAL& operator[](INTEGER i); XREAL& elem(INTEGER i, INTEGER j); XREAL& check1(INTEGER i); XREAL& check2(INTEGER i, INTEGER j); X Xvoid error(const char* msg); X Xfriend int operator==(realmat& a, realmat& b); Xfriend int operator!=(realmat& a, realmat& b); X Xfriend int operator<(realmat& a, realmat& b); Xfriend int operator>(realmat& a, realmat& b); X Xfriend int operator<=(realmat& a, realmat& b); Xfriend int operator>=(realmat& a, realmat& b); X Xfriend INTEGER rows(realmat& a); Xfriend INTEGER cols(realmat& a); X Xfriend ostream& operator<<(ostream& stream, realmat& a); X Xfriend realmat operator+(realmat& a, realmat& b); Xfriend realmat operator+(realmat& a); X Xfriend realmat operator-(realmat& a, realmat& b); Xfriend realmat operator-(realmat& a); X Xfriend realmat operator*(realmat& a, realmat& b); Xfriend realmat operator*(REAL& a, realmat& b); Xfriend realmat operator*(INTEGER& a, realmat& b); X Xfriend realmat T(realmat& a); X Xfriend realmat invpsd(realmat& a, REAL eps = 1.0e-13); //a is psd, symmetric X X}; X X X//protected: X Xinline realmat::realmat(INTEGER r, INTEGER c, REAL* a) X {rows=r; cols=c; len=r*c; x = a;} X Xinline void realmat::resize(INTEGER r, INTEGER c, REAL* a) X {rows=r; cols=c; len=r*c; x = a;} //Note: No delete, this.x == 0 expected. X X X//public: X Xinline int operator!=(realmat& a, realmat& b) X {return !(a == b);} X Xinline REAL& realmat::operator[](INTEGER i) X {return x[i-1];} X Xinline REAL& realmat::elem(INTEGER i, INTEGER j) X {return x[i + rows*j - rows - 1];} // returns x[rows*(j-1)+i-1] X Xinline INTEGER rows(realmat& a) X {return a.rows;} X Xinline INTEGER cols(realmat& a) X {return a.cols;} X Xinline realmat::~realmat() X {delete x;} X Xinline realmat::realmat() X {rows=0; cols=0; len=0; x = 0;} X Xinline realmat::realmat(INTEGER r, INTEGER c) X{ X if (r<=0) error("Error, realmat, realmat, Number of rows not positive"); X if (c<=0) error("Error, realmat, realmat, Number of columns not positive"); X rows=r; X cols=c; X len=r*c; X x = new REAL[len]; X if (x == 0) error("Error, realmat, realmat, Operator new failed"); X} X Xinline void realmat::resize(INTEGER r, INTEGER c) X{ X if (r<=0) error("Error, realmat, resize, Number of rows not positive"); X if (c<=0) error("Error, realmat, resize, Number of columns not positive"); X delete x; X rows=r; X cols=c; X len=r*c; X x = new REAL[len]; X if (x == 0) error("Error, realmat, resize, Operator new failed"); X} X Xextern void default_realmat_error_handler(const char* msg); Xextern ONE_ARG_ERROR_HANDLER_T realmat_error_handler; Xextern ONE_ARG_ERROR_HANDLER_T X set_realmat_error_handler(ONE_ARG_ERROR_HANDLER_T f); X X#endif END_OF_FILE if test 5870 -ne `wc -c <'realmat.h'`; then echo shar: \"'realmat.h'\" unpacked with wrong size! fi # end of 'realmat.h' fi echo shar: End of archive 2 $of 6$. cp /dev/null ark2isdone MISSING="" for I in 1 2 3 4 5 6 ; do if test ! -f ark${I}isdone ; then MISSING="${MISSING} ${I}" fi done if test "${MISSING}" = "" ; then echo You have unpacked all 6 archives. rm -f ark[1-9]isdone else echo You still need to unpack the following archives: echo " " ${MISSING} fi ## End of shell archive. exit 0 exit 0 # Just in case... -- Kent Landfield INTERNET: kent@sparky.IMD.Sterling.COM Sterling Software, IMD UUCP: uunet!sparky!kent Phone: (402) 291-8300 FAX: (402) 291-4362 Please send comp.sources.misc-related mail to kent@uunet.uu.net.