I am trying to perform a Quantile Regression with an unbalanced panel data set, to find out whether the effect of IDV on Gini differs in the .25th and the .75th quantile.
Therefore, I downloaded the following package:
Code:
qregpd
Code:
qregpd Gini_DispSWIID IDV GLPIntensity InflationGDPdeflatorannual Ruralpopulation LevelofdemocracyPolityV Populationgrowthannual TradeofGDP Schoolenrollmentsecondary GDPgrowthannual Wageandsalariedworkerstotal Currenthealthexpenditureof Arablelandoftotal, id(Country_ID) fix(Year) quantile(.75)
Code:
. qregpd Gini_DispSWIID IDV GLPIntensity InflationGDPdeflatorannual Ruralpopulation Levelofdemocr > acyPolityV Populationgrowthannual TradeofGDP Schoolenrollmentsecondary GDPgrowthannual Wageands > alariedworkerstotal Currenthealthexpenditureof Arablelandoftotal, id(Country_ID) fix(Year) quan > tile(.75) Nelder-Mead optimization initial: f(p) = -87.285174 rescale: f(p) = -87.285174 Iteration 0: f(p) = -87.285174 Iteration 1: f(p) = -5.0307519 Iteration 2: f(p) = -.78734938 Iteration 3: f(p) = -.78734938 Iteration 4: f(p) = -.78734938 Iteration 5: f(p) = -.78734938 Iteration 6: f(p) = -.78734938 Iteration 7: f(p) = -.78734938 Iteration 8: f(p) = -.78734938 Iteration 9: f(p) = -.78734938 Iteration 10: f(p) = -.78734938 Iteration 11: f(p) = -.78734938 Iteration 12: f(p) = -.78734938 Iteration 13: f(p) = -.78734938 Iteration 14: f(p) = -.78734938 Iteration 15: f(p) = -.78734938 Iteration 16: f(p) = -.78734938 Iteration 17: f(p) = -.6038165 Iteration 18: f(p) = -.6038165 Iteration 19: f(p) = -.6038165 Iteration 20: f(p) = -.6038165 Iteration 21: f(p) = -.6038165 Iteration 22: f(p) = -.6038165 Iteration 23: f(p) = -.6038165 Iteration 24: f(p) = -.6038165 Iteration 25: f(p) = -.6038165 Iteration 26: f(p) = -.6038165 Iteration 27: f(p) = -.6038165 Iteration 28: f(p) = -.6038165 Iteration 29: f(p) = -.6038165 Iteration 30: f(p) = -.6038165 Iteration 31: f(p) = -.6038165 Iteration 32: f(p) = -.6038165 Iteration 33: f(p) = -.6038165 Iteration 34: f(p) = -.6038165 Iteration 35: f(p) = -.6038165 Iteration 36: f(p) = -.6038165 Iteration 37: f(p) = -.6038165 Iteration 38: f(p) = -.6038165 Iteration 39: f(p) = -.6038165 Iteration 40: f(p) = -.6038165 Iteration 41: f(p) = -.6038165 Iteration 42: f(p) = -.6038165 Iteration 43: f(p) = -.6038165 Iteration 44: f(p) = -.6038165 Iteration 45: f(p) = -.6038165 Iteration 46: f(p) = -.6038165 Iteration 47: f(p) = -.6038165 Iteration 48: f(p) = -.6038165 Quantile Regression for Panel Data (QRPD) Number of obs: 301 Number of groups: 19 Min obs per group: 11 Max obs per group: 19 --------------------------------------------------------------------------------------------- Gini_DispSWIID | Coefficient Std. err. z P>|z| [95% conf. interval] ----------------------------+---------------------------------------------------------------- IDV | -.0515317 . . . . . GLPIntensity | -118.5223 . . . . . InflationGDPdeflatorannual | -.2093337 . . . . . Ruralpopulationasaoftota | -23.02208 . . . . . LevelofdemocracyPolityV | .8931058 . . . . . Populationgrowthannual | 1.354606 . . . . . TradeofGDP | .0269068 .235968 0.11 0.909 -.435582 .4893956 Schoolenrollmentsecondary | -.021503 .0731588 -0.29 0.769 -.1648916 .1218855 GDPgrowthannual | .1055383 . . . . . Wageandsalariedworkerstotal | -.3813951 . . . . . Currenthealthexpenditureof | .8699441 . . . . . Arablelandoftotal | 4.889251 . . . . . --------------------------------------------------------------------------------------------- No excluded instruments - standard QRPD estimation.
Thank you so much.
Kind regards,
Matthew
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