I intend to measure the TFP of manufacturing firms for 23 firms through Cobb - Douglas Production Function Approach using Prodest code in Stata for the period 2015-2017.
I am using Levinsohn and Petrin (2003) approach with the attached Stata dataset for the same. However, I got negative coefficients of logL and logK in case of Levinsohn and Petrin (2003) approach. Results have been attached in the form of the image below. These individual TFP Values as dependent variables are regressed with infrastructure stocks as an independent variable.
Stata Code:

prodest lnGVA, method (lp) free(lnL) proxy(lnInput) state(lnK) poly(3) valueadded reps(250)

predict TFP

Can anyone help to overcome this issue in the result? Please respond.
Famid year lnGVA lnK lnL lnInput
1 2015 13.34451139 14.43711069 13.82499642 14.94789177
2 2015 10.90103056 11.39432509 12.00363817 12.56028455
3 2015 10.52884158 10.90823019 11.74051512 12.56156862
4 2015 11.71408167 12.96707595 11.86919333 13.19120632
5 2015 10.78025708 10.57660072 11.29931136 12.61370021
6 2015 11.30195799 10.79404052 10.71557266 12.07061138
7 2015 13.89161883 14.69274188 13.91372923 15.59004602
8 2015 12.68505841 13.08795162 13.27239071 14.34357928
9 2015 12.17481436 12.49800879 11.90358822 13.0876142
10 2015 10.37213186 10.36546633 10.85971028 11.51539809
11 2015 11.89178185 12.93752458 11.87475212 13.15976277
12 2015 12.88529455 13.74124074 13.52565546 14.57748312
13 2015 11.26551282 11.99049128 12.59243988 13.35132999
14 2015 11.91772596 13.18896836 12.4565356 13.65617625
15 2015 14.08798489 14.43171365 14.08198176 15.39174269
16 2015 11.84720763 14.04701543 12.27763023 13.27226267
17 2015 11.84987474 12.32818954 13.05611887 13.71366131
18 2015 12.2899301 12.94906791 12.83675914 13.8142172
19 2015 13.31159488 14.0107976 14.37021545 14.99198913
20 2015 7.930242796 7.485056583 10.17564981 8.622648785
21 2015 12.58255248 13.30226199 13.42014082 14.52482222
22 2015 12.45019737 12.54385883 12.59546596 13.57663852
23 2015 11.82869258 13.06866314 13.13062515 14.08698579
1 2016 13.48373632 14.55212104 13.81087483 14.87352281
2 2016 11.09011635 12.09660422 12.06294103 12.5021024
3 2016 10.43491923 10.92367085 11.54507315 12.3586785
4 2016 11.26804467 13.0838402 11.83438558 13.05207522
5 2016 10.7530443 10.4765827 11.23227197 12.73695465
6 2016 11.41296781 10.8661762 10.80685514 12.14263568
7 2016 13.9810047 14.90279048 13.99064468 15.47850878
8 2016 12.75290698 13.25330751 13.23466654 14.43921191
9 2016 12.17262238 12.62311498 11.81393335 12.95478697
10 2016 10.49895051 10.55531489 10.86526777 11.58579992
11 2016 11.52578885 12.93922703 11.86191193 13.20034564
12 2016 12.99481134 13.78688988 13.55250289 14.51278126
13 2016 11.53622211 12.27852734 12.51736626 13.27750928
14 2016 12.20480924 13.55141515 12.49875718 13.69012924
15 2016 14.14469829 14.47629798 14.13122964 15.45322844
16 2016 11.80136403 14.22621284 12.25082132 13.35906515
17 2016 11.91459215 12.41077943 13.10543896 13.69703396
18 2016 12.39233098 13.06787942 12.88085472 13.9162249
19 2016 13.50721806 14.10146753 14.47325385 14.97086086
20 2016 7.590132471 7.815032882 10.07225939 8.626449627
21 2016 12.79879814 13.47297179 13.50166245 14.52501448
22 2016 12.73090918 12.64631381 12.64053977 13.87087559
23 2016 12.05060545 13.1477183 13.11664506 14.11136877
1 2017 13.37820655 14.57031481 13.87654921 14.99791944
2 2017 11.31482949 11.94872061 12.1067936 12.48835749
3 2017 10.48552974 11.50844226 11.50258216 12.33606399
4 2017 11.45326146 13.4451234 11.89512877 13.13305958
5 2017 10.53909949 10.41244812 11.2286642 12.71520385
6 2017 11.32904661 10.91523748 10.70495088 11.98065828
7 2017 13.91086343 15.06872287 14.03587425 15.54622029
8 2017 12.98490547 13.39216318 13.3848061 14.65935721
9 2017 12.08523011 12.39343758 11.86197541 12.95263912
10 2017 10.6732993 10.92220152 10.98576719 11.73012375
11 2017 11.90939933 13.25324863 11.88186489 13.18303098
12 2017 13.19309476 13.82810032 13.62542212 14.61800501
13 2017 11.72306923 12.43306157 12.42895616 13.41005668
14 2017 12.21973581 13.62180352 12.54387614 13.72163599
15 2017 14.10537468 14.43870418 14.12643932 15.34049703
16 2017 12.04438547 14.43877548 12.31398041 13.40533761
17 2017 11.98871848 12.33912744 13.18484604 13.69294326
18 2017 12.53221462 13.23962174 12.93065551 14.01079714
19 2017 13.5782688 14.25990109 14.51053547 15.04941816
20 2017 7.673050689 7.846913183 10.08397409 8.60560012
21 2017 13.23123952 13.50385468 13.57157867 14.59337051
22 2017 12.61675213 12.68825504 12.74948936 13.66224058
23 2017 12.22915794 13.35048112 13.11830917 14.14173054