Dear Statalists,
I am trying to implement Kripfganz & Schwarz (2019) variance correction for a sequential 2SLS estimation in which the first step involves a first-differenced equation. However, I am not able to run the second step succesfully either because: 1) variable names do not macth in the second step or 2) the command xtseqreg does not estimate correctly the first step when using the option iv(,model(diff))
Let me illustrate the error I get using the public data base abdata.dta.
This is the first step I would like to replicate with XTSEQREG:
In a first attempt, I am able to replicate the point estimates obtained with the command IVREG2. In the second step, on the other hand, I would like to ignore my first estimate of the capital coefficient and re-estimate this parameter using only cross sectional variation, that is, using the levels equation. However, Stata throws an error saying the variables in the first step do not macth:
In a second attempt, I try to make use of the option iv(,model(diff)). As you can see below, I am now able to run the second step. However, the first step changes and does not match the results obtained with IVREG2 or with the previous first step using XTSEQREG with the variables manually first differenced and making use of the option iv(,model(lev)).
.
Thanks!
Santiago
I am trying to implement Kripfganz & Schwarz (2019) variance correction for a sequential 2SLS estimation in which the first step involves a first-differenced equation. However, I am not able to run the second step succesfully either because: 1) variable names do not macth in the second step or 2) the command xtseqreg does not estimate correctly the first step when using the option iv(,model(diff))
Let me illustrate the error I get using the public data base abdata.dta.
Code:
webuse abdata xtset id year gen sample = (L.n != . & L.w != . & L.k != . & L2.w != . & L2.k != .)
Code:
. ivreg2 D.n (D.w D.k = L2.w L2.k) i.year if sample == 1, robust IV (2SLS) estimation -------------------- Estimates efficient for homoskedasticity only Statistics robust to heteroskedasticity Number of obs = 751 F( 8, 742) = 8.48 Prob > F = 0.0000 Total (centered) SS = 13.73614077 Centered R2 = -0.6556 Total (uncentered) SS = 16.06429854 Uncentered R2 = -0.4156 Residual SS = 22.74127488 Root MSE = .174 ------------------------------------------------------------------------------ | Robust D.n | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- w | D1. | 1.146626 1.024929 1.12 0.263 -.862198 3.15545 | k | D1. | .1041816 .8474889 0.12 0.902 -1.556866 1.765229 | year | 1979 | -.0031767 .0369074 -0.09 0.931 -.0755138 .0691605 1980 | -.033016 .0842578 -0.39 0.695 -.1981583 .1321262 1981 | -.1403377 .1671101 -0.84 0.401 -.4678675 .1871922 1982 | -.1298584 .1888818 -0.69 0.492 -.50006 .2403432 1983 | -.0918314 .1470443 -0.62 0.532 -.380033 .1963702 1984 | -.0454144 .1310702 -0.35 0.729 -.3023074 .2114785 | _cons | -.0033702 .0537219 -0.06 0.950 -.1086632 .1019228 ------------------------------------------------------------------------------ Underidentification test (Kleibergen-Paap rk LM statistic): 1.387 Chi-sq(1) P-val = 0.2388 ------------------------------------------------------------------------------ Weak identification test (Cragg-Donald Wald F statistic): 0.928 (Kleibergen-Paap rk Wald F statistic): 0.690 Stock-Yogo weak ID test critical values: 10% maximal IV size 7.03 15% maximal IV size 4.58 20% maximal IV size 3.95 25% maximal IV size 3.63 Source: Stock-Yogo (2005). Reproduced by permission. NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors. ------------------------------------------------------------------------------ Hansen J statistic (overidentification test of all instruments): 0.000 (equation exactly identified) ------------------------------------------------------------------------------ Instrumented: D.w D.k Included instruments: 1979.year 1980.year 1981.year 1982.year 1983.year 1984.year Excluded instruments: L2.w L2.k ------------------------------------------------------------------------------
Code:
. xtseqreg D.n D.w D.k if sample == 1, iv(L2.w L2.k, model(level)) teffects vce(robust) 1979bn.year 1980.year 1981.year 1982.year 1983.year 1984.year Group variable: id Number of obs = 751 Time variable: year Number of groups = 140 Obs per group: min = 5 avg = 5.364286 max = 7 Number of instruments = 9 (Std. Err. adjusted for 140 clusters in id) ------------------------------------------------------------------------------ | Robust D.n | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- w | D1. | 1.146626 1.112832 1.03 0.303 -1.034484 3.327736 | k | D1. | .1041816 .6833247 0.15 0.879 -1.23511 1.443473 | year | 1979 | -.0031767 .0314504 -0.10 0.920 -.0648183 .058465 1980 | -.033016 .070716 -0.47 0.641 -.1716169 .1055849 1981 | -.1403377 .1428372 -0.98 0.326 -.4202935 .1396181 1982 | -.1298584 .1657704 -0.78 0.433 -.4547625 .1950456 1983 | -.0918314 .1339453 -0.69 0.493 -.3543594 .1706966 1984 | -.0454144 .1081419 -0.42 0.675 -.2573686 .1665397 | _cons | -.0033702 .0442384 -0.08 0.939 -.0900759 .0833355 ------------------------------------------------------------------------------ . xtseqreg n (w) k if sample == 1, iv(LD.k, model(level)) teffects vce(robust) 1979bn.year 1980.year 1981.year 1982.year 1983.year 1984.year option first() incorrectly specified -- variable names do not match r(322);
Code:
. xtseqreg n w k if sample == 1, iv(L2.w L2.k, model(diff)) teffects vce(robust) 1979bn.year 1980.year 1981.year 1982.year 1983.year 1984.year Group variable: id Number of obs = 751 Time variable: year Number of groups = 140 Obs per group: min = 5 avg = 5.364286 max = 7 Number of instruments = 9 (Std. Err. adjusted for 140 clusters in id) ------------------------------------------------------------------------------ | Robust n | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- w | .5550703 .529953 1.05 0.295 -.4836185 1.593759 k | .7902225 .0450446 17.54 0.000 .7019367 .8785083 | year | 1979 | .0190451 .043757 0.44 0.663 -.066717 .1048073 1980 | .0086869 .044635 0.19 0.846 -.0787962 .0961699 1981 | -.0482938 .0462004 -1.05 0.296 -.138845 .0422573 1982 | -.0722319 .062388 -1.16 0.247 -.1945102 .0500464 1983 | -.0696385 .0915159 -0.76 0.447 -.2490063 .1097294 1984 | -.1923327 .1193695 -1.61 0.107 -.4262926 .0416273 | _cons | -.329578 1.672226 -0.20 0.844 -3.60708 2.947924 ------------------------------------------------------------------------------ . xtseqreg n (w) k if sample == 1, iv(LD.k, model(level)) teffects vce(robust) 1979bn.year 1980.year 1981.year 1982.year 1983.year 1984.year Group variable: id Number of obs = 751 Time variable: year Number of groups = 140 ------------------------------------------------------------------------------ Equation _first Equation _second Number of obs = 751 Number of obs = 751 Number of groups = 140 Number of groups = 140 Obs per group: min = 5 Obs per group: min = 5 avg = 5.364286 avg = 5.364286 max = 7 max = 7 Number of instruments = 9 Number of instruments = 8 (Std. Err. adjusted for clustering on id) ------------------------------------------------------------------------------ | Robust n | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _first | w | .5550703 .529953 1.05 0.295 -.4836185 1.593759 _cons | -.329578 1.672226 -0.20 0.844 -3.60708 2.947924 -------------+---------------------------------------------------------------- _second | k | .5487759 .1122811 4.89 0.000 .328709 .7688428 | year | 1979 | -.0018788 .0517203 -0.04 0.971 -.1032487 .0994911 1980 | -.0198237 .0534126 -0.37 0.711 -.1245105 .0848632 1981 | -.1003511 .0590729 -1.70 0.089 -.216132 .0154297 1982 | -.1546863 .0761061 -2.03 0.042 -.3038516 -.0055211 1983 | -.2072187 .1211814 -1.71 0.087 -.4447299 .0302925 1984 | -.4274209 .1510207 -2.83 0.005 -.7234161 -.1314258 | _cons | -.0528342 .0459619 -1.15 0.250 -.1429178 .0372494 ------------------------------------------------------------------------------
Thanks!
Santiago
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