hkswen,我把我的试验分析数据(按照friontier4.1的输入格式,面板数据,17个行业,6年的数据)
也附上,用frontier求得的系数估计值(假设超越对数生产函数是lny=a +b1*lnk +b2*lnl +0.5*b3*lnk*lnk+0.5*b4*lnl*lnl+b5*lnk*lnl)分别为
b1= -0.4811375, b2= 0.9954747 ,b3= -0.409819, b4= -0.1788467, b5= 0.2915615
资本要素的产出弹性=b1+b3*lnk+b5*lnl
劳动要素的产出弹性=b2+b4*lnl+b5*lnk
不知这弹性到底是单一值,还是一个序列,单一值如何求,序列值如何求?
industry year lny lnk lnl
1.0 1.0 2.292256071 2.382557322 5.872864557
2.0 1.0 2.045322979 2.135450699 5.532858801
3.0 1.0 1.612783857 1.703291378 4.677725546
4.0 1.0 1.812913357 1.903089987 5.805125531
5.0 1.0 0.954242509 1.045322979 5.037322892
6.0 1.0 2.255272505 2.345569756 5.491023934
7.0 1.0 1.041392685 1.130333768 4.924537718
8.0 1.0 0.77815125 0.86923172 4.543757723
9.0 1.0 2.096910013 2.18723862 5.476772231
10.0 1.0 2.045322979 2.135450699 5.785073454
11.0 1.0 2.1430148 2.23325001 5.551148571
12.0 1.0 1.230448921 1.320146286 4.902008334
13.0 1.0 1.662757832 1.752816431 4.747543863
14.0 1.0 1.886490725 1.976808337 4.810245963
15.0 1.0 1.113943352 1.204119983 4.386819886
16.0 1.0 1.491361694 1.582063363 5.196441491
17.0 1.0 1.919078092 2.009450896 5.778486975
1.0 2.0 2.324282455 2.421768401 5.866211109
2.0 2.0 2.120573931 2.155032229 5.496098992
3.0 2.0 1.755874856 1.728353782 4.75629371
4.0 2.0 1.949390007 2.199480915 5.664071943
5.0 2.0 0.954242509 1.075546961 4.88112206
6.0 2.0 2.324282455 2.262925469 5.515422101
7.0 2.0 0.698970004 1.230448921 4.855634225
8.0 2.0 0.602059991 0.851258349 4.527346293
9.0 2.0 2.064457989 2.204391332 5.459270325
10.0 2.0 2.158362492 2.179838928 5.755114556
11.0 2.0 2.346352974 2.419294722 5.541889878
12.0 2.0 1.431363764 1.346352974 4.890079933
13.0 2.0 1.612783857 1.752816431 4.816645569
14.0 2.0 2.100370545 1.98721923 4.912822318
15.0 2.0 1.146128036 1.222716471 4.370494585
16.0 2.0 1.556302501 1.630427875 5.231082019
17.0 2.0 1.929418926 2.01745073 5.742560964
1.0 3.0 2.392696953 2.474216264 5.820293433
2.0 3.0 2.206825876 2.198106999 5.473152544
3.0 3.0 1.544068044 1.718501689 4.656548561
4.0 3.0 1.913813852 2.059941888 5.632425908
5.0 3.0 0.954242509 1.096910013 4.853333105
6.0 3.0 2.525044807 2.364738555 5.503245771
7.0 3.0 0.84509804 1.127104798 4.828692113
8.0 3.0 0.698970004 0.826074803 4.484982782
9.0 3.0 2.056904851 2.434888121 5.413153857
10.0 3.0 2.243038049 2.295127085 5.745397268
11.0 3.0 2.374748346 2.319314304 5.534887491
12.0 3.0 1.568201724 1.434568904 4.894332681
13.0 3.0 2.10720997 1.722633923 4.871438657
14.0 3.0 2.079181246 2.068556895 5.011227778
15.0 3.0 1.477121255 1.245512668 4.579074432
16.0 3.0 1.568201724 1.654176542 5.092394275
17.0 3.0 1.995635195 2.079181246 5.599399405
1.0 4.0 2.488550717 2.497758718 5.802834591
2.0 4.0 2.294466226 2.186673867 5.456359959
3.0 4.0 1.544068044 1.80685803 4.668162188
4.0 4.0 1.939519253 2.119255889 5.632939911
5.0 4.0 1.079181246 1.086359831 4.810353512
6.0 4.0 2.619093331 2.389874558 5.500139008
7.0 4.0 0.954242509 1.301029996 4.749396165
8.0 4.0 0.77815125 0.812913357 4.397348966
9.0 4.0 2.285557309 2.581835924 5.416765287
10.0 4.0 2.403120521 2.345765693 5.764832684
11.0 4.0 2.44870632 2.389166084 5.507877451
12.0 4.0 1.653212514 1.432969291 5.003594074
13.0 4.0 2.06069784 1.565847819 4.824418737
14.0 4.0 2.056904851 2.135450699 5.01891676
15.0 4.0 1.477121255 1.276461804 4.57721597
16.0 4.0 1.633468456 1.602059991 5.064865885
17.0 4.0 2.017033339 2.05804623 5.560474545
1.0 5.0 2.553883027 2.542700969 5.786886941
2.0 5.0 2.361727836 2.206825876 5.478036651
3.0 5.0 1.799340549 1.880241776 4.727955835
4.0 5.0 1.982271233 2.234517284 5.595690178
5.0 5.0 1.146128036 1.093421685 4.794871284
6.0 5.0 2.767155866 2.486996888 5.510633469
7.0 5.0 1 1.36361198 4.733686674
8.0 5.0 0.954242509 0.826074803 4.330494942
9.0 5.0 2.414973348 2.626237685 5.429123794
10.0 5.0 2.45331834 2.404833717 5.809600786
11.0 5.0 2.477121255 2.410777233 5.501408475
12.0 5.0 1.939519253 1.478566496 5.029241696
13.0 5.0 2.220108088 1.632457292 4.794704094
14.0 5.0 2.255272505 2.174641193 5.098885894
15.0 5.0 1.643452676 1.394451681 4.706649519
16.0 5.0 1.707570176 1.615950052 5.068668143
17.0 5.0 2.113943352 1.967547976 5.549208093
1.0 6.0 2.617000341 2.62211036 5.798640289
2.0 6.0 2.418301291 2.259593879 5.483294816
3.0 6.0 1.86332286 1.913813852 4.840777101
4.0 6.0 2.056904851 2.343999069 5.562164342
5.0 6.0 1.230448921 1.130333768 4.719662683
6.0 6.0 2.893761762 2.639884742 5.533630998
7.0 6.0 1.230448921 1.257678575 4.693515904
8.0 6.0 0.903089987 0.755874856 4.365768688
9.0 6.0 2.369215857 2.818489822 5.420477249
10.0 6.0 2.505149978 2.616370472 5.830729775
11.0 6.0 2.424881637 2.451632947 5.483014992
12.0 6.0 1.924279286 1.743509765 5.040329337
13.0 6.0 2.245512668 1.716003344 4.919391927
14.0 6.0 2.369215857 2.261976191 5.222586423
15.0 6.0 1.653212514 1.550228353 4.649870094
16.0 6.0 1.755874856 1.646403726 5.10356428
17.0 6.0 2.167317335 2.076640444 5.539044718