<p>抱歉，我没有重金可以悬赏，但是真心求教！感谢好心人的帮助！</p><p>我用Frontier4.1计算银行的成本效率和利润效率，是照着例子和文献做的，可是结果总是出现得出的效率值非常接近，甚至是都超过1的情况，怎么试算都解决不了，网上看到不少人也是碰到这样的情况，可是只看到提问，没看到回答，不知道大家是怎么解决的？真诚请求过来人帮忙！</p><p>下面是我的ins文件和输出文件；</p><p>1               1=ERROR COMPONENTS MODEL, 2=TE EFFECTS MODEL<br/>06c-dat.txt         DATA FILE NAME<br/>06c-out.txt         OUTPUT FILE NAME<br/>2               1=PRODUCTION FUNCTION, 2=COST FUNCTION<br/>y               LOGGED DEPENDENT VARIABLE (Y/N)<br/>23              NUMBER OF CROSS-SECTIONS<br/>1               NUMBER OF TIME PERIODS<br/>23              NUMBER OF OBSERVATIONS IN TOTAL<br/>14               NUMBER OF REGRESSOR VARIABLES (Xs) <br/>y               MU (Y/N) [OR DELTA0 (Y/N) IF USING TE EFFECTS MODEL]<br/>y               ETA (Y/N) [OR NUMBER OF TE EFFECTS REGRESSORS (Zs)]<br/>n               STARTING VALUES (Y/N)<br/>                IF YES THEN     BETA0              <br/>                                BETA1 TO<br/>                                BETAK            <br/>                                SIGMA SQUARED<br/>                                GAMMA<br/>                                MU              [OR DELTA0<br/>                                ETA                 DELTA1 TO<br/>                                                      DELTAP]</p><p>                                NOTE: IF YOU ARE SUPPLYING STARTING VALUES<br/>                                AND YOU HAVE RESTRICTED MU [OR DELTA0] TO BE<br/>                                ZERO THEN YOU SHOULD NOT SUPPLY A STARTING<br/>                                VALUE FOR THIS PARAMETER.</p><p>输出文件：</p><p>Output from the program FRONTIER (Version 4.1c)</p><p><br/>instruction file = 06c-ins.txt <br/>data file =        06c-dat.txt </p><p><br/> Error Components Frontier (see B&C 1992)<br/> The model is a cost function<br/> The dependent variable is logged</p><p><br/>the ols estimates are :</p><p>                 coefficient     standard-error    t-ratio</p><p>  beta 0         0.15982743E+02  0.13460350E+02  0.11873942E+01<br/>  beta 1         0.25558980E+02  0.22648521E+02  0.11285055E+01<br/>  beta 2         0.11202144E+02  0.10110121E+02  0.11080128E+01<br/>  beta 3         0.18726175E+02  0.19378592E+02  0.96633311E+00<br/>  beta 4         0.20512464E+02  0.18632790E+02  0.11008799E+01<br/>  beta 5         0.39052704E+01  0.37463571E+01  0.10424181E+01<br/>  beta 6         0.10649113E+02  0.13343173E+02  0.79809450E+00<br/>  beta 7         0.19151669E+02  0.17335038E+02  0.11047954E+01<br/>  beta 8         0.29300115E+02  0.35859432E+02  0.81708250E+00<br/>  beta 9         0.13535230E+02  0.13972731E+02  0.96868893E+00<br/>  beta10         0.29954442E+01  0.25647050E+01  0.11679488E+01<br/>  beta11        -0.55080575E+00  0.49502372E+00 -0.11126856E+01<br/>  beta12         0.15388484E+01  0.16613458E+01  0.92626613E+00<br/>  beta13         0.10272185E+01  0.12835530E+01  0.80029302E+00<br/>  beta14         0.67251137E+00  0.23332936E+01  0.28822407E+00<br/>  sigma-squared  0.56420285E-02</p><p>log likelihood function =   0.39050404E+02</p><p>the estimates after the grid search were :</p><p>  beta 0         0.15974710E+02<br/>  beta 1         0.25558980E+02<br/>  beta 2         0.11202144E+02<br/>  beta 3         0.18726175E+02<br/>  beta 4         0.20512464E+02<br/>  beta 5         0.39052704E+01<br/>  beta 6         0.10649113E+02<br/>  beta 7         0.19151669E+02<br/>  beta 8         0.29300115E+02<br/>  beta 9         0.13535230E+02<br/>  beta10         0.29954442E+01<br/>  beta11        -0.55080575E+00<br/>  beta12         0.15388484E+01<br/>  beta13         0.10272185E+01<br/>  beta14         0.67251137E+00<br/>  sigma-squared  0.20269650E-02<br/>  gamma          0.50000000E-01<br/>  mu             0.00000000E+00<br/>  eta            0.00000000E+00<br/> <br/> <br/> iteration =     0  func evals =     20  llf =  0.39050047E+02<br/>     0.15974710E+02 0.25558980E+02 0.11202144E+02 0.18726175E+02 0.20512464E+02<br/>     0.39052704E+01 0.10649113E+02 0.19151669E+02 0.29300115E+02 0.13535230E+02<br/>     0.29954442E+01-0.55080575E+00 0.15388484E+01 0.10272185E+01 0.67251137E+00<br/>     0.20269650E-02 0.50000000E-01 0.00000000E+00 0.00000000E+00<br/> gradient step<br/> iteration =     5  func evals =     47  llf =  0.39050064E+02<br/>     0.15974639E+02 0.25558812E+02 0.11202028E+02 0.18725682E+02 0.20512530E+02<br/>     0.39052783E+01 0.10649170E+02 0.19151968E+02 0.29300223E+02 0.13535607E+02<br/>     0.29954108E+01-0.55079631E+00 0.15388639E+01 0.10271559E+01 0.67244817E+00<br/>     0.20275054E-02 0.48962334E-01-0.80205568E-03 0.00000000E+00<br/> iteration =    10  func evals =     80  llf =  0.39050272E+02<br/>     0.15979016E+02 0.25558587E+02 0.11201989E+02 0.18725728E+02 0.20511829E+02<br/>     0.39050782E+01 0.10648940E+02 0.19150615E+02 0.29299539E+02 0.13535967E+02<br/>     0.29964321E+01-0.55084638E+00 0.15399252E+01 0.10275002E+01 0.67346100E+00<br/>     0.20025592E-02 0.23300840E-01-0.53656796E-02 0.00000000E+00<br/> iteration =    15  func evals =    142  llf =  0.39050393E+02<br/>     0.15980650E+02 0.25558074E+02 0.11201781E+02 0.18726123E+02 0.20511903E+02<br/>     0.39051952E+01 0.10648850E+02 0.19150809E+02 0.29299148E+02 0.13535747E+02<br/>     0.29961402E+01-0.55092577E+00 0.15391375E+01 0.10275996E+01 0.67266240E+00<br/>     0.19791309E-02 0.89017251E-02-0.83946839E-02 0.00000000E+00<br/> pt better than entering pt cannot be found<br/> iteration =    16  func evals =    150  llf =  0.39050393E+02<br/>     0.15980650E+02 0.25558074E+02 0.11201781E+02 0.18726123E+02 0.20511903E+02<br/>     0.39051952E+01 0.10648850E+02 0.19150809E+02 0.29299148E+02 0.13535747E+02<br/>     0.29961402E+01-0.55092577E+00 0.15391375E+01 0.10275996E+01 0.67266240E+00<br/>     0.19791309E-02 0.89017251E-02-0.83946839E-02 0.00000000E+00</p><p><br/>the final mle estimates are :</p><p>                 coefficient     standard-error    t-ratio</p><p>  beta 0         0.15980650E+02  0.50880756E+00  0.31408044E+02<br/>  beta 1         0.25558074E+02  0.61741217E+00  0.41395482E+02<br/>  beta 2         0.11201781E+02  0.54424918E+00  0.20582081E+02<br/>  beta 3         0.18726123E+02  0.62695623E+00  0.29868310E+02<br/>  beta 4         0.20511903E+02  0.85984165E+00  0.23855442E+02<br/>  beta 5         0.39051952E+01  0.31486198E+00  0.12402879E+02<br/>  beta 6         0.10648850E+02  0.99532374E+00  0.10698881E+02<br/>  beta 7         0.19150809E+02  0.70541353E+00  0.27148343E+02<br/>  beta 8         0.29299148E+02  0.97832017E+00  0.29948424E+02<br/>  beta 9         0.13535747E+02  0.83141173E+00  0.16280438E+02<br/>  beta10         0.29961402E+01  0.76133533E+00  0.39353752E+01<br/>  beta11        -0.55092577E+00  0.15858451E+00 -0.34740200E+01<br/>  beta12         0.15391375E+01  0.51103572E+00  0.30118002E+01<br/>  beta13         0.10275996E+01  0.38790907E+00  0.26490733E+01<br/>  beta14         0.67266240E+00  0.94739928E+00  0.71000941E+00<br/>  sigma-squared  0.19791309E-02  0.19304460E-02  0.10252195E+01<br/>  gamma          0.89017251E-02  0.12162222E+01  0.73191599E-02<br/>  mu            -0.83946839E-02  0.27825428E+00 -0.30169110E-01<br/>  eta            0.00000000E+00  0.10000000E+01  0.00000000E+00</p><p>log likelihood function =   0.39050393E+02</p><p>the likelihood value is less than that obtained<br/>using ols! - try again using different starting values</p><p>number of iterations =     16</p><p>(maximum number of iterations set at :   100)</p><p>number of cross-sections =     23</p><p>number of time periods =      1</p><p>total number of observations =     23</p><p>thus there are:      0  obsns not in the panel</p><p><br/>covariance matrix :</p><p>  0.25888514E+00  0.16930603E+00  0.22165924E+00  0.25776521E-01  0.69158799E-02<br/>  0.10927149E+00  0.78832694E-02  0.19830197E+00 -0.48412985E-02  0.69662743E-02<br/> -0.56690881E-01  0.22036931E-02 -0.31129843E-01 -0.24078056E-01 -0.78816644E-01<br/> -0.36898215E-03 -0.23933903E+00 -0.50070416E-01  0.00000000E+00<br/>  0.16930603E+00  0.38119778E+00  0.83509096E-01  0.43169987E-02  0.37387344E+00<br/>  0.11008841E-01 -0.19547022E-03  0.21843855E+00  0.82621752E-02  0.20280183E-02<br/>  0.26236986E-01 -0.92395219E-02  0.65189845E-02  0.18478564E-01 -0.23192967E-01<br/>  0.57846909E-04  0.35188533E-01  0.81973708E-02  0.00000000E+00<br/>  0.22165924E+00  0.83509096E-01  0.29620717E+00  0.41677864E-03 -0.15168625E+00<br/>  0.16263613E+00  0.23946569E-02  0.26002459E+00  0.14460334E-02 -0.98878770E-02<br/> -0.66345404E-01  0.59600148E-02 -0.34257159E-01 -0.37814886E-01  0.71495216E-03<br/> -0.26539580E-04  0.67836937E-02  0.39273340E-02  0.00000000E+00<br/>  0.25776521E-01  0.43169987E-02  0.41677864E-03  0.39307411E+00 -0.14767329E-01<br/> -0.13023170E-01  0.72609834E-01 -0.11542039E-01  0.15893829E+00  0.42788730E+00<br/> -0.33962463E-01  0.17032806E-01 -0.36683028E-01  0.19645067E-02 -0.94702765E-01<br/>  0.28486587E-04  0.52110522E-02 -0.19628880E-02  0.00000000E+00<br/>  0.69158799E-02  0.37387344E+00 -0.15168625E+00 -0.14767329E-01  0.73932766E+00<br/> -0.93732231E-01  0.57862404E-03 -0.10203269E+00 -0.19086224E-02  0.18828306E-02<br/> -0.29769340E-01 -0.37624904E-02  0.24667516E-02 -0.25467522E-01  0.30218720E-01<br/>  0.17533263E-04  0.91184551E-02  0.60024950E-03  0.00000000E+00<br/>  0.10927149E+00  0.11008841E-01  0.16263613E+00 -0.13023170E-01 -0.93732231E-01<br/>  0.99138070E-01  0.12376745E-02  0.10516620E+00  0.89759445E-02 -0.22093494E-01<br/> -0.60575425E-01  0.53899946E-02 -0.27249674E-01 -0.37947916E-01  0.23647182E-01<br/> -0.37969010E-04 -0.49302719E-02  0.52492772E-03  0.00000000E+00<br/>  0.78832694E-02 -0.19547022E-03  0.23946569E-02  0.72609834E-01  0.57862404E-03<br/>  0.12376745E-02  0.99066935E+00 -0.80944497E-02 -0.19186912E-01 -0.51615397E-01<br/>  0.84698911E-02  0.32848558E-02  0.27730557E-02  0.52688554E-02  0.18337588E-01<br/>  0.17349823E-04  0.61160630E-02  0.62287677E-03  0.00000000E+00<br/>  0.19830197E+00  0.21843855E+00  0.26002459E+00 -0.11542039E-01 -0.10203269E+00<br/>  0.10516620E+00 -0.80944497E-02  0.49760826E+00 -0.77874559E-02  0.35477740E-02<br/>  0.95616874E-01 -0.80046230E-02  0.23565477E-01  0.56899506E-01  0.50035715E-01<br/>  0.10910613E-03  0.62363308E-01  0.11624584E-01  0.00000000E+00<br/> -0.48412985E-02  0.82621752E-02  0.14460334E-02  0.15893829E+00 -0.19086224E-02<br/>  0.89759445E-02 -0.19186912E-01 -0.77874559E-02  0.95711035E+00 -0.10945681E+00<br/>  0.40572495E-02 -0.10048817E-01  0.38646246E-01 -0.18949885E-01  0.32490503E-01<br/>  0.38235646E-04  0.22451062E-01  0.50596639E-02  0.00000000E+00<br/>  0.69662743E-02  0.20280183E-02 -0.98878770E-02  0.42788730E+00  0.18828306E-02<br/> -0.22093494E-01 -0.51615397E-01  0.35477740E-02 -0.10945681E+00  0.69124546E+00<br/>  0.36139422E-01  0.10015911E-01 -0.24554483E-01  0.37799725E-01  0.63993168E-01<br/> -0.22220446E-04 -0.14354165E-02  0.18486518E-02  0.00000000E+00<br/> -0.56690881E-01  0.26236986E-01 -0.66345404E-01 -0.33962463E-01 -0.29769340E-01<br/> -0.60575425E-01  0.84698911E-02  0.95616874E-01  0.40572495E-02  0.36139422E-01<br/>  0.57963148E+00 -0.66668622E-01  0.36231705E+00  0.28396366E+00  0.17077527E-01<br/>  0.39431294E-04  0.32860842E-01  0.66855858E-02  0.00000000E+00<br/>  0.22036931E-02 -0.92395219E-02  0.59600148E-02  0.17032806E-01 -0.37624904E-02<br/>  0.53899946E-02  0.32848558E-02 -0.80046230E-02 -0.10048817E-01  0.10015911E-01<br/> -0.66668622E-01  0.25149048E-01 -0.40062191E-01 -0.24329993E-01 -0.40501944E-01<br/>  0.29125084E-04  0.13745006E-02 -0.28121913E-02  0.00000000E+00<br/> -0.31129843E-01  0.65189845E-02 -0.34257159E-01 -0.36683028E-01  0.24667516E-02<br/> -0.27249674E-01  0.27730557E-02  0.23565477E-01  0.38646246E-01 -0.24554483E-01<br/>  0.36231705E+00 -0.40062191E-01  0.26115751E+00  0.16987552E+00 -0.67423269E-01<br/>  0.37848425E-04  0.22983888E-01  0.36292261E-02  0.00000000E+00<br/> -0.24078056E-01  0.18478564E-01 -0.37814886E-01  0.19645067E-02 -0.25467522E-01<br/> -0.37947916E-01  0.52688554E-02  0.56899506E-01 -0.18949885E-01  0.37799725E-01<br/>  0.28396366E+00 -0.24329993E-01  0.16987552E+00  0.15047344E+00 -0.47516314E-01<br/>  0.25981308E-04  0.11315752E-01  0.77331899E-03  0.00000000E+00<br/> -0.78816644E-01 -0.23192967E-01  0.71495216E-03 -0.94702765E-01  0.30218720E-01<br/>  0.23647182E-01  0.18337588E-01  0.50035715E-01  0.32490503E-01  0.63993168E-01<br/>  0.17077527E-01 -0.40501944E-01 -0.67423269E-01 -0.47516314E-01  0.89756539E+00<br/> -0.31447095E-04  0.51837138E-01  0.22600143E-01  0.00000000E+00<br/> -0.36898215E-03  0.57846909E-04 -0.26539580E-04  0.28486587E-04  0.17533263E-04<br/> -0.37969010E-04  0.17349823E-04  0.10910613E-03  0.38235646E-04 -0.22220446E-04<br/>  0.39431294E-04  0.29125084E-04  0.37848425E-04  0.25981308E-04 -0.31447095E-04<br/>  0.37266219E-05  0.21695856E-02  0.44787234E-03  0.00000000E+00<br/> -0.23933903E+00  0.35188533E-01  0.67836937E-02  0.52110522E-02  0.91184551E-02<br/> -0.49302719E-02  0.61160630E-02  0.62363308E-01  0.22451062E-01 -0.14354165E-02<br/>  0.32860842E-01  0.13745006E-02  0.22983888E-01  0.11315752E-01  0.51837138E-01<br/>  0.21695856E-02  0.14791966E+01  0.32499178E+00  0.00000000E+00<br/> -0.50070416E-01  0.81973708E-02  0.39273340E-02 -0.19628880E-02  0.60024950E-03<br/>  0.52492772E-03  0.62287677E-03  0.11624584E-01  0.50596639E-02  0.18486518E-02<br/>  0.66855858E-02 -0.28121913E-02  0.36292261E-02  0.77331899E-03  0.22600143E-01<br/>  0.44787234E-03  0.32499178E+00  0.77425446E-01  0.00000000E+00<br/>  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00<br/>  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00<br/>  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00<br/>  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.10000000E+01</p><p></p><p>cost efficiency estimates :</p><p></p><p>efficiency estimates for year      1 :</p><p>     firm             eff.-est.</p><p>       1           0.10015505E+01<br/>       2           0.10016478E+01<br/>       3           0.10016502E+01<br/>       4           0.10015258E+01<br/>       5           0.10015751E+01<br/>       6           0.10015889E+01<br/>       7           0.10015778E+01<br/>       8           0.10015344E+01<br/>       9           0.10015650E+01<br/>      10           0.10016416E+01<br/>      11           0.10015363E+01<br/>      12           0.10015072E+01<br/>      13           0.10015685E+01<br/>      14           0.10015159E+01<br/>      15           0.10014724E+01<br/>      16           0.10015881E+01<br/>      17           0.10015400E+01<br/>      18           0.10016119E+01<br/>      19           0.10016276E+01<br/>      20           0.10015759E+01<br/>      21           0.10015863E+01<br/>      22           0.10015675E+01<br/>      23           0.10015265E+01</p><p><br/> mean eff. in year   1 =  0.10015687E+01</p><p>我想问题一定是跟这个有关：the likelihood value is less than that obtained<br/>using ols! - try again using different starting values</p><p>可是实在不知道该怎么解决，请大家帮忙看看！</p><p></p><p></p>