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unit tests.log
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unit tests.log
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.
. version 13
.
. set seed 0193284710
.
. foreach julia in "" julia {
2. use collapsed, clear
3.
. qui regress hasinsurance selfemployed post post_self, cluster(year)
4. boottest post_self=.04, `julia' nogr
5. boottest post_self=.04, `julia' weight(webb) noci
6. boottest post_self=.04, `julia' weight(webb) jk nogr
7. boottest post_self=.04, `julia' weight(webb) jk nogr nonull
8. scoretest post_self=.04, `julia' nogr
9.
. boottest post_self post, `julia' reps(999) weight(webb) nogr // wild bootstrap test of joint null, Webb weights, null imposed, 9,999 replications
10. boottest (post_self) (post), `julia' reps(999) weight(webb) nogr // same
11. boottest {post_self=.04} {post}, `julia' nogr // separate tests, no correction for multiple hypotheses
12. boottest {(post) (post_self=.04)} {(post) (post_self=.08)}, `julia' madj(sidak) nogr // separate tests, Sidak correction for multiple hypotheses
13.
. use nlsw88
14.
. qui regress wage tenure ttl_exp collgrad, cluster(industry)
15. boottest tenure, `julia' svmat nogr // wild bootstrap test of joint null, Rademacher weights, null imposed, saving simulated distribution
16.
. constraint 1 ttl_exp = .2
17. qui cnsreg wage tenure ttl_exp collgrad, constr(1) cluster(industry)
18. boottest tenure, `julia' nogr // wild bootstrap test of tenure=0, conditional on ttl_exp=2, Rademacher weights, null imposed, 999 replications
19.
. regress wage tenure ttl_exp collgrad south#union, cluster(industry)
20. margins south
21. boottest, `julia' margins nogr // bootstrap CI of average predicted wage for south = 0 and 1
22. margins, dydx(south)
23. boottest, `julia' margins graphopt(xtitle(Average effect of south)) nogr // bootstrap CI of average impact in sample of changing south from 0 to 1
24.
. qui ivregress 2sls wage ttl_exp collgrad (tenure = union), cluster(industry)
25. boottest tenure, `julia' ptype(equaltail) seed(987654321) nogr // Wald test, wild restricted efficient bootstrap, Rademacher weights, null imposed, 999 reps
26. boottest tenure, `julia' ptype(equaltail) seed(987654321) stat(c) nogr // same but bootstrap-c
27. boottest tenure, `julia' ptype(equaltail) seed(987654321) stat(c) gridmin(-2) gridmax(2) nogr // same but limit graphing range
28. boottest, `julia' ar nogr // same bootstrap, but Anderson-Rubin test (much faster)
29. scoretest tenure, `julia' nogr // Rao/LM test of same
30. waldtest tenure, `julia' nogr // Wald test of same
31.
. qui ivregress liml wage (tenure = collgrad ttl_exp), cluster(industry)
32. boottest tenure, `julia' noci // WRE bootstrap, Rademacher weights, 999 replications
33. boottest tenure, `julia' noci jk // WRE bootstrap, Rademacher weights, 999 replications
34. qui cmp (wage = tenure) (tenure = collgrad ttl_exp), ind(1 1) qui nolr cluster(industry)
35. boottest tenure, `julia' // reasonable match on test statistic and p value
36.
. qui ivreg2 wage collgrad smsa race age (tenure = union married), cluster(industry) fuller(1)
37. boottest tenure, `julia' nograph // Wald test, WRE bootstrap, Rademacher weights, 999 replications
38. boottest tenure, `julia' nograph jk // Wald test, WRE bootstrap, Rademacher weights, 999 replications
39. boottest, `julia' nograph ar // same, but Anderson-Rubin (faster, but CI misleading if instruments invalid)
40.
. qui ivregress liml wage (collgrad tenure = ttl_exp union), cluster(industry)
41. boottest, `julia' ar nogr // Anderson-Rubin test, with contour plot of p value surface
42. boottest, `julia' ar nogr jk // Anderson-Rubin test, with contour plot of p value surface
43. boottest collgrad tenure, `julia' gridpoints(10 10) nogr // WRE boostrap also with contour plot
44.
. qui regress wage tenure ttl_exp collgrad, robust // no clustering
45. boottest tenure, `julia' nogr
46. boottest tenure, `julia' nogr jk
47.
. qui ivregress liml wage (collgrad tenure = ttl_exp union), robust // no clustering
48. boottest, `julia' ar nogr
49. boottest collgrad tenure, `julia' gridpoints(10 10) nogr
50.
. qui regress wage ttl_exp collgrad tenure, cluster(industry)
51. waldtest collgrad tenure, cluster(industry age) nogr // multi-way-clustered tests after estimation command not offering such
52. boottest tenure, `julia' cluster(industry age) bootcluster(industry) gridmin(-.2) gridmax(.2) nogr
53.
. qui areg wage ttl_exp collgrad tenure [aw=hours] if occupation<., cluster(age) absorb(industry)
54. boottest tenure, `julia' cluster(age occupation) bootcluster(occupation) seed(999) nograph // override estimate's clustering
55. boottest tenure, `julia' cluster(age occupation) bootcluster(occupation) seed(999) nograph jk // override estimate's clustering
56. qui reg wage ttl_exp collgrad tenure i.industry [aw=hours] if occupation<., cluster(age)
57. boottest tenure, `julia' cluster(age occupation) bootcluster(occupation) seed(999) nograph // should match previous result
58. boottest tenure, `julia' cluster(age occupation) bootcluster(occupation) seed(999) nograph jk // should match previous result
59.
. qui probit c_city tenure wage ttl_exp collgrad, cluster(industry)
60. boottest tenure, `julia' nogr // score bootstrap, Rademacher weights, null imposed, 999 replications
61. boottest tenure, `julia' cluster(industry age) bootcluster(industry) small nogr // multi-way-clustered, finite-sample-corrected test with score bootstrap
62.
. qui gsem (c_city <- tenure wage ttl_exp collgrad), vce(cluster industry) probit // same probit estimate as previous
63. boottest tenure, `julia' // requires Stata 14.0 or later
64. boottest tenure, `julia' cluster(industry age) bootcluster(industry) small // requires Stata 14.0 or later
65.
. sysuse auto, clear
66. ml model lf myprobit (foreign = mpg weight) // define model
67. qui ml max // estimate
68. boottest mpg, `julia' cmdline(ml model lf myprobit (foreign = mpg weight))
69.
. probit foreign i.mpg
70. scoretest 14.mpg
71.
. use collapsed, clear
72.
. qui regress hasinsurance selfemployed post post_self, cluster(year)
73. boottest post_self=.04, `julia' weight(webb) nogr
74. boottest post_self=.04, `julia' weight(webb) reps(9999999) noci
75. boottest post_self=.04, `julia' weight(normal) reps(9999) noci
76. boottest post_self=.04, `julia' weight(gamma) reps(9999) noci svv
77. boottest post_self=.04, `julia' weight(mammen) reps(9999) noci
78. boottest post_self=.04, `julia' weight(mammen) reps(9999) boottype(score) nogr
79.
. qui regress hasinsurance selfemployed post post_self, robust
80. boottest post_self=.04, `julia' weight(webb) nogr
81.
. qui regress hasinsurance selfemployed post post_self, cluster(year)
82. boottest (post_self=.05) (post=-.02), `julia' reps(9999) weight(webb) nogr
83. boottest (post_self=.05) (post=-.02) (selfemployed=-.15), `julia' reps(9999) weight(webb) nogr
84.
. qui regress hasinsurance selfemployed post post_self
85. boottest post_self=.04, `julia' weight(webb) nogr
86. boottest (post_self=.05) (post=-.02), `julia' reps(9999) weight(webb) nogr
87. scoretest (post_self=.05), `julia' nogr
88. scoretest (post_self=.05) (post=-.02), `julia' nogr
89. boottest (post_self=.08), `julia' boottype(score) reps(9999) nogr
90. boottest (post_self=.05) (post=-.02), `julia' boottype(score) reps(9999) nogr
91.
. use nlsw88, clear
92. constraint 1 ttl_exp = .2
93. qui cnsreg wage tenure ttl_exp collgrad, constr(1) cluster(industry)
94. boottest tenure, `julia' nogr
95.
. keep if e(sample)
96. gen id = _n - cond(_n>1000, 1000, 0)
97. qui cnsreg wage tenure ttl_exp collgrad, constr(1) cluster(id) // granular but not pure robust
98. boottest tenure, `julia' reps(9999) nogr
99.
. qui areg wage tenure ttl_exp collgrad, cluster(id) a(industry)
100. boottest tenure, `julia' reps(9999) nogr
101.
. use d:\OneDrive\Documents\Macros\nlsw88, clear
102. qui ivregress liml wage ttl_exp collgrad (tenure = union), cluster(industry)
103. boottest tenure, `julia' ptype(equaltail) reps(9999) nogr
104. boottest tenure, `julia' nonull reps(99999) matsize(.1) nogr
105. boottest tenure, `julia' ptype(upper) svmat(t) reps(9999) nogr
106. boottest tenure, `julia' ptype(lower) svmat(numer) reps(9999) nogr
107.
. qui ivregress liml wage ttl_exp collgrad (tenure = union), cluster(industry)
108. boottest tenure, `julia' ptype(equaltail) reps(9999) nogr
109.
. qui ivregress liml wage ttl_exp collgrad (tenure = union) if industry<., robust
110. boottest tenure, `julia' ptype(equaltail) reps(99) noci
111.
. qui ivregress liml wage ttl_exp collgrad (tenure = union) if industry<., robust
112. boottest tenure, ptype(equaltail) reps(99) noci
113. boottest collgrad tenure, `julia' ptype(equaltail) reps(99) noci
114.
. qui ivregress 2sls wage ttl_exp collgrad (tenure = union) if industry<.
115. boottest tenure, `julia' ptype(equaltail) reps(99) noci
116. boottest tenure collgrad, `julia' ptype(equaltail) reps(99) noci
117.
. qui ivregress 2sls wage ttl_exp collgrad (tenure = union), cluster(industry)
118. boottest tenure, `julia' ptype(equaltail) weight(webb) stat(c) gridmin(-5) gridmax(5) gridpoints(100) nogr
119. boottest tenure, `julia' ptype(equaltail) weight(webb) stat(c) gridmin(-5) gridmax(5) gridpoints(100) matsize(.01) nogr
120.
. qui ivregress 2sls wage ttl_exp collgrad (tenure = union) if industry<., robust
121. boottest tenure, `julia' ptype(equaltail) matsize(.005) noci weight(webb)
122.
. preserve
123. keep if e(sample)
124. gen id = _n - cond(_n>1000, 1000, 0)
125. boottest tenure, `julia' cluster(id) ptype(equaltail) matsize(.005) noci weight(webb)
126. restore
127.
. qui ivregress 2sls wage ttl_exp collgrad (tenure = union), cluster(industry)
128. boottest, `julia' ar nogr
129. boottest, `julia' ar nonull nogr
130. scoretest tenure, `julia' nogr
131. waldtest tenure, `julia' ptype(upper) nogr
132.
. qui ivregress liml wage (tenure = collgrad ttl_exp), cluster(industry)
133. boottest tenure, `julia' nogr
134.
. qui ivreg2 wage collgrad smsa race age (tenure = union married), cluster(industry) fuller(1)
135. boottest tenure, `julia' nograph weight(webb) reps(9999)
136. qui gen individual = _n
137. boottest tenure, `julia' noci bootcluster(individual) weight(webb)
138. boottest tenure, `julia' nograph bootcluster(collgrad) cluster(collgrad industry) weight(webb) reps(9999)
139.
. qui areg wage ttl_exp collgrad tenure [aw=hours] if occupation<. & grade<. & union<., cluster(age) absorb(industry)
140. boottest tenure, `julia' nograph cluster(age occupation) bootcluster(occupation)
141.
. qui areg wage ttl_exp collgrad tenure if occupation<. & grade<. & union<. & hours<., robust absorb(industry)
142. boottest tenure, `julia' nograph
143.
. qui areg wage ttl_exp collgrad tenure [aw=hours] if occupation<. & grade<. & union<., robust absorb(industry)
144. boottest tenure, `julia' nograph
145.
. qui ivreghdfe wage ttl_exp collgrad tenure (occupation = union married) [aw=hours] if grade<., liml cluster(industry) absorb(industry)
146. boottest tenure, `julia' nograph
147. boottest occupation, `julia' nograph
148.
. qui ivreghdfe wage ttl_exp collgrad tenure (occupation = union married) [aw=hours] if grade<., liml cluster(industry) absorb(age)
149. boottest tenure, `julia' nograph
150. boottest collgrad tenure, `julia' nograph
151. boottest occupation, `julia' gridmin(-1) gridmax(1) nograph
152.
. constraint 1 [wage]collgrad
153. qui ivreghdfe wage ttl_exp /*collgrad*/ tenure (occupation = union married) [aw=hours], liml cluster(industry) absorb(age) // approximate contrained LIML with ivreghdfe
154. boottest tenure, `julia' nograph
155.
. use d:\OneDrive\Documents\Macros\abdata, clear
156. qui areg n w k, absorb(ind)
157. boottest k, `julia' cluster(id year) nograph
158. qui areg n w k [aw=ys], absorb(ind)
159. boottest k, `julia' cluster(id year) nograph
160.
. use pixel-level-baseline-final, clear
161. global pix lnkm pixpetro pixdia pixwaterd pixcapdist pixmal pixsead pixsuit pixelev pixbdist
162. global geo lnwaterkm lnkm2split mean_elev mean_suit malariasuit petroleum diamondd
163. global poly capdistance1 seadist1 borderdist1
164. qui encode pixwbcode, gen(ccode) // make numerical country identifier
165. qui areg lnl0708s centr_tribe lnpd0 \$pix \$geo \$poly, absorb(ccode)
166. boottest centr_tribe, `julia' nogr reps(999) clust(ccode pixcluster) bootcluster(ccode)
167. boottest centr_tribe, `julia' nogr reps(999) clust(ccode pixcluster) bootcluster(pixcluster)
168. boottest centr_tribe, `julia' nogr reps(999) clust(ccode pixcluster) bootcluster(ccode pixcluster)
169.
. infile coll merit male black asian year state chst using regm.raw, clear
170. qui regress coll merit male black asian i.year i.state if !inlist(state,34,57,59,61,64,71,72,85,88), cluster(state)
171. generate individual = _n // unique ID for each observation
172. boottest merit, `julia' nogr reps(999) gridpoints(10) // defaults to bootcluster(state)
173. boottest merit, `julia' nogr reps(999) gridpoints(10) nonull
174. boottest merit, `julia' nogr reps(999) gridpoints(10) bootcluster(state year)
175. boottest merit, `julia' nogr reps(999) gridpoints(10) nonull bootcluster(state year)
176. boottest merit, `julia' nogr reps(999) gridpoints(10) bootcluster(individual)
177. boottest merit, `julia' nogr reps(999) gridpoints(10) nonull bootcluster(individual)
178. boottest merit, `julia' nogr reps(999) gridpoints(10) nonull bootcluster(individual) matsize(.1)
179. }
Warning: with 8 bootstrap clusters, the number of replications, 999, exceeds the universe of Rademacher draws, 2^8 = 256. Sampling each once.
Consider Webb weights instead, using weight(webb).
Wild bootstrap-t, null imposed, 256 replications, Wald test, bootstrap clustering by year, Rademacher weights:
post_self=.04
t(7) = 2.0194
Prob>|t| = 0.0742
95% confidence set for null hypothesis expression: [.03874, .07098]
Wild bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by year, Webb weights:
post_self=.04
t(7) = 2.0194
Prob>|t| = 0.0921
Wild bootstrap-t, null imposed, jackknifed residuals, 999 replications, Wald test, bootstrap clustering by year, Webb weights:
post_self=.04
t(7) = 2.0194
Prob>|t| = 0.0991
95% confidence set for null hypothesis expression: [.03571, .07199]
Wild bootstrap-t, null not imposed, jackknifed residuals, 999 replications, Wald test, bootstrap clustering by year, Webb weights:
post_self=.04
t(7) = 2.0194
Prob>|t| = 0.0911
95% confidence set for null hypothesis expression: [.03672, .073]
Rao score (Lagrange multiplier) test:
post_self=.04
t(7) = 1.8479
Prob>|t| = 0.1071
95% confidence set for null hypothesis expression: [.03455, .07517]
Wild bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by year, Webb weights:
post_self post
F(2, 7) = 72.4798
Prob > F = 0.0030
Wild bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by year, Webb weights:
post_self
post
F(2, 7) = 72.4798
Prob > F = 0.0010
Warning: with 8 bootstrap clusters, the number of replications, 999, exceeds the universe of Rademacher draws, 2^8 = 256. Sampling each once.
Consider Webb weights instead, using weight(webb).
Wild bootstrap-t, null imposed, 256 replications, Wald test, bootstrap clustering by year, Rademacher weights:
post_self=.04
t(7) = 2.0194
Prob>|t| = 0.0742
95% confidence set for null hypothesis expression: [.03874, .07098]
Wild bootstrap-t, null imposed, 256 replications, Wald test, bootstrap clustering by year, Rademacher weights:
post
t(7) = -5.5821
Prob>|t| = 0.0000
95% confidence set for null hypothesis expression: [−.02978, −.01306]
Warning: with 8 bootstrap clusters, the number of replications, 999, exceeds the universe of Rademacher draws, 2^8 = 256. Sampling each once.
Consider Webb weights instead, using weight(webb).
Wild bootstrap-t, null imposed, 256 replications, Wald test, bootstrap clustering by year, Rademacher weights:
post
post_self=.04
F(2, 7) = 26.6776
Prob > F = 0.0000
Sidak-adjusted prob = 0.0000
Wild bootstrap-t, null imposed, 256 replications, Wald test, bootstrap clustering by year, Rademacher weights:
post
post_self=.08
F(2, 7) = 16.3553
Prob > F = 0.0000
Sidak-adjusted prob = 0.0000
(NLSW, 1988 extract)
Wild bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by industry, Rademacher weights:
tenure
t(11) = 1.0772
Prob>|t| = 0.2843
95% confidence set for null hypothesis expression: [−.03531, .1968]
Wild bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by industry, Rademacher weights:
tenure
t(11) = 2.1254
Prob>|t| = 0.0000
95% confidence set for null hypothesis expression: [.008795, .1846]
Linear regression Number of obs = 1,855
F(6, 11) = 466.17
Prob > F = 0.0000
R-squared = 0.2744
Root MSE = 3.5578
(Std. err. adjusted for 12 clusters in industry)
------------------------------------------------------------------------------
| Robust
wage | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
tenure | .0371771 .0144888 2.57 0.026 .0052874 .0690668
ttl_exp | .2672039 .0213283 12.53 0.000 .2202605 .3141472
collgrad | 3.152462 .3625251 8.70 0.000 2.35455 3.950374
|
south#union |
0#union | .4785269 .3349975 1.43 0.181 -.2587976 1.215851
1#nonunion | -1.438916 .2353751 -6.11 0.000 -1.956973 -.9208588
1#union | -.3029596 .6704026 -0.45 0.660 -1.778506 1.172586
|
_cons | 3.581387 .220271 16.26 0.000 3.096574 4.0662
------------------------------------------------------------------------------
Predictive margins Number of obs = 1,855
Model VCE: Robust
Expression: Linear prediction, predict()
------------------------------------------------------------------------------
| Delta-method
| Margin std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
south |
0 | 8.164246 .3020966 27.03 0.000 7.499336 8.829156
1 | 6.887295 .4175234 16.50 0.000 5.968332 7.806258
------------------------------------------------------------------------------
Wild bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by industry, Rademacher weights:
0bn.south 1.south
t(11) = 27.0253
Prob>|t| = 0.0010
95% confidence set for null hypothesis expression: [7.628, 9.428]
Wild bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by industry, Rademacher weights:
t(11) = 16.4956
Prob>|t| = 0.0020
95% confidence set for null hypothesis expression: [6.193, 8.846]
Average marginal effects Number of obs = 1,855
Model VCE: Robust
Expression: Linear prediction, predict()
dy/dx wrt: 1.south
------------------------------------------------------------------------------
| Delta-method
| dy/dx std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
1.south | -1.276951 .2201867 -5.80 0.000 -1.761578 -.7923233
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
Wild bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by industry, Rademacher weights:
1.south
t(11) = -5.7994
Prob>|t| = 0.0170
95% confidence set for null hypothesis expression: [−1.872, −.2555]
Wild bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by industry, Rademacher weights:
tenure
z = 2.6040
2 * min(Prob>|z|, Prob<-|z|) = 0.0601
95% confidence set for null hypothesis expression: [−.1988, 2.294]
Wild bootstrap-c, null imposed, 999 replications, Wald test, bootstrap clustering by industry, Rademacher weights:
tenure
z = 1.6561
2 * min(Prob>|z|, Prob<-|z|) = 0.1361
95% confidence set for null hypothesis expression: (−∞, ∞)
(A confidence set could not be bounded. Try widening the search range with the gridmin() and gridmax() options.)
Note: denominator for z statistic computed from the bootstrap replications of the numerator.
Wild bootstrap-c, null imposed, 999 replications, Wald test, bootstrap clustering by industry, Rademacher weights:
tenure
z = 1.6561
2 * min(Prob>|z|, Prob<-|z|) = 0.1361
95% confidence set for null hypothesis expression: (−∞, ∞)
(A confidence set could not be bounded. Try widening the search range with the gridmin() and gridmax() options.)
Note: denominator for z statistic computed from the bootstrap replications of the numerator.
Wild bootstrap-t, null imposed, 999 replications, Anderson-Rubin Wald test, bootstrap clustering by industry, Rademacher weights:
tenure
z = 2.4454
Prob>|z| = 0.0230
95% confidence set for null hypothesis expression: [.07155, 1.247]
Rao score (Lagrange multiplier) test:
tenure
z = 2.3279
Prob>|z| = 0.0199
95% confidence set for null hypothesis expression: [.1367, 1.324]
Wald test:
tenure
z = 2.6040
Prob>|z| = 0.0092
95% confidence set for null hypothesis expression: [.182, 1.29]
Wild bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by industry, Rademacher weights:
tenure
z = 7.5847
Prob>|z| = 0.0070
Wild bootstrap-t, null imposed, jackknifed residuals, 999 replications, Wald test, bootstrap clustering by industry, Rademacher weights:
tenure
z = 7.5847
Prob>|z| = 0.0020
Re-running regression with null imposed.
Fitting individual models as starting point for full model fit.
Fitting full model.
Mixed-process regression Number of obs = 2,217
Wald chi2(2) = 317.77
Log pseudolikelihood = -13512.755 Prob > chi2 = 0.0000
( 1) [wage]tenure = 0
(Std. err. adjusted for 12 clusters in industry)
------------------------------------------------------------------------------
| Robust
| Coefficient std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
wage |
tenure | 0 (omitted)
_cons | 7.809235 .4073256 19.17 0.000 7.010891 8.607578
-------------+----------------------------------------------------------------
tenure |
collgrad | .2294204 .4219757 0.54 0.587 -.5976368 1.056478
ttl_exp | .6828446 .0405263 16.85 0.000 .6034146 .7622746
_cons | -2.642845 .3257173 -8.11 0.000 -3.28124 -2.004451
-------------+----------------------------------------------------------------
/lnsig_1 | 1.752935 .0714927 24.52 0.000 1.612812 1.893059
/lnsig_2 | 1.504619 .0258663 58.17 0.000 1.453922 1.555316
/atanhrho_12 | .0271501 .0263105 1.03 0.302 -.0244175 .0787177
-------------+----------------------------------------------------------------
sig_1 | 5.771519 .4126217 5.0169 6.639645
sig_2 | 4.502438 .1164613 4.279868 4.736583
rho_12 | .0271434 .0262911 -.0244127 .0785555
------------------------------------------------------------------------------
Score bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by industry, Rademacher weights:
tenure
z = 8.2785
Prob>|z| = 0.0000
Wild bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by industry, Rademacher weights:
tenure
z = 4.0267
Prob>|z| = 0.0300
95% confidence set for null hypothesis expression: [−.8324, −.602] ∪ [.2065, 1.205]
Wild bootstrap-t, null imposed, jackknifed residuals, 999 replications, Wald test, bootstrap clustering by industry, Rademacher weights:
tenure
z = 4.0267
Prob>|z| = 0.0170
95% confidence set for null hypothesis expression: [.2572, .9766]
Wild bootstrap-t, null imposed, 999 replications, Anderson-Rubin Wald test, bootstrap clustering by industry, Rademacher weights:
tenure
chi2(2) = 18.4337
Prob > chi2 = 0.0000
95% confidence set for null hypothesis expression: [.3354, 5.393]
Wild bootstrap-t, null imposed, 999 replications, Anderson-Rubin Wald test, bootstrap clustering by industry, Rademacher weights:
collgrad tenure
chi2(2) = 477.2687
Prob > chi2 = 0.0000
Wild bootstrap-t, null imposed, jackknifed residuals, 999 replications, Anderson-Rubin Wald test, bootstrap clustering by industry, Rademacher weights:
collgrad tenure
chi2(2) = 477.2687
Prob > chi2 = 0.0000
Wild bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by industry, Rademacher weights:
collgrad tenure
chi2(2) = 65.9990
Prob > chi2 = 0.0040
Wild bootstrap-t, null imposed, 999 replications, Wald test, Rademacher weights:
tenure
t(2227) = 1.2941
Prob>|t| = 0.2002
95% confidence set for null hypothesis expression: [−.0196, .08308]
Wild bootstrap-t, null imposed, jackknifed residuals, 999 replications, Wald test, Rademacher weights:
tenure
t(2227) = 1.2941
Prob>|t| = 0.1892
95% confidence set for null hypothesis expression: [−.01224, .07775]
Wild bootstrap-t, null imposed, 999 replications, Anderson-Rubin Wald test, Rademacher weights:
collgrad tenure
chi2(2) = 428.6568
Prob > chi2 = 0.0000
Wild bootstrap-t, null imposed, 999 replications, Wald test, Rademacher weights:
collgrad tenure
chi2(2) = 261.5326
Prob > chi2 = 0.0000
Overriding estimator's cluster/robust settings with cluster(industry age)
Wald test, clustering by industry age:
collgrad tenure
F(2, 11) = 40.5984
Prob > F = 0.0000
Overriding estimator's cluster/robust settings with cluster(industry age)
Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by industry age, bootstrap clustering by industry, Rademacher weights:
tenure
t(11) = 1.0951
Prob>|t| = 0.2663
95% confidence set for null hypothesis expression: [−.05127, .1275]
Overriding estimator's cluster/robust settings with cluster(age occupation)
Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by age occupation, bootstrap clustering by occupation, Rademacher weights:
tenure
t(12) = 0.4874
Prob>|t| = 0.6266
95% confidence set for null hypothesis expression: [−.04575, .1122]
Overriding estimator's cluster/robust settings with cluster(age occupation)
Wild bootstrap-t, null imposed, jackknifed residuals, 999 replications, Wald test, clustering by age occupation, bootstrap clustering by occupation, Rademacher weights:
tenure
t(12) = 0.4874
Prob>|t| = 0.6216
95% confidence set for null hypothesis expression: [−.04623, .1141]
Overriding estimator's cluster/robust settings with cluster(age occupation)
Wild bootstrap-t, null imposed, 999 replications, Wald test, clustering by age occupation, bootstrap clustering by occupation, Rademacher weights:
tenure
t(12) = 0.4874
Prob>|t| = 0.6266
95% confidence set for null hypothesis expression: [−.04575, .1122]
Overriding estimator's cluster/robust settings with cluster(age occupation)
Wild bootstrap-t, null imposed, jackknifed residuals, 999 replications, Wald test, clustering by age occupation, bootstrap clustering by occupation, Rademacher weights:
tenure
t(12) = 0.4874
Prob>|t| = 0.6186
95% confidence set for null hypothesis expression: [−.04341, .1151]
Re-running regression with null imposed.
Iteration 0: Log pseudolikelihood = -1338.5964
Iteration 1: Log pseudolikelihood = -1332.3403
Iteration 2: Log pseudolikelihood = -1332.339
Iteration 3: Log pseudolikelihood = -1332.339
Probit regression Number of obs = 2,217
Wald chi2(3) = 11.80
Log pseudolikelihood = -1332.339 Prob > chi2 = 0.0081
( 1) [c_city]tenure = 0
(Std. err. adjusted for 12 clusters in industry)
------------------------------------------------------------------------------
| Robust
c_city | Coefficient std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
tenure | 0 (omitted)
wage | .0068643 .0043506 1.58 0.115 -.0016627 .0153913
ttl_exp | -.0021496 .007035 -0.31 0.760 -.015938 .0116389
collgrad | .1904642 .0767865 2.48 0.013 .0399654 .340963
_cons | -.6223347 .0939856 -6.62 0.000 -.8065432 -.4381262
------------------------------------------------------------------------------
Score bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by industry, Rademacher weights:
tenure
z = 0.2980
Prob>|z| = 0.7708
Overriding estimator's cluster/robust settings with cluster(industry age)
Re-running regression with null imposed.
Iteration 0: Log pseudolikelihood = -1338.5964
Iteration 1: Log pseudolikelihood = -1332.3403
Iteration 2: Log pseudolikelihood = -1332.339
Iteration 3: Log pseudolikelihood = -1332.339
Probit regression Number of obs = 2,217
Wald chi2(3) = 11.80
Log pseudolikelihood = -1332.339 Prob > chi2 = 0.0081
( 1) [c_city]tenure = 0
(Std. err. adjusted for 12 clusters in industry)
------------------------------------------------------------------------------
| Robust
c_city | Coefficient std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
tenure | 0 (omitted)
wage | .0068643 .0043506 1.58 0.115 -.0016627 .0153913
ttl_exp | -.0021496 .007035 -0.31 0.760 -.015938 .0116389
collgrad | .1904642 .0767865 2.48 0.013 .0399654 .340963
_cons | -.6223347 .0939856 -6.62 0.000 -.8065432 -.4381262
------------------------------------------------------------------------------
Score bootstrap-t, null imposed, 999 replications, Wald test, clustering by industry age, bootstrap clustering by industry, Rademacher weights:
tenure
t(11) = 0.2803
Prob>|t| = 0.7486
Re-running regression with null imposed.
Iteration 0: Log pseudolikelihood = -1334.9102
Iteration 1: Log pseudolikelihood = -1332.3392
Iteration 2: Log pseudolikelihood = -1332.339
Iteration 3: Log pseudolikelihood = -1332.339
Generalized structural equation model Number of obs = 2,217
Response : c_city
Family : Bernoulli
Link : probit
Log pseudolikelihood = -1332.339
( 1) [c_city]tenure = 0
(Std. err. adjusted for 12 clusters in industry)
------------------------------------------------------------------------------
| Robust
| Coefficient std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
c_city |
tenure | 0 (omitted)
wage | .0068643 .0043506 1.58 0.115 -.0016627 .0153913
ttl_exp | -.0021496 .007035 -0.31 0.760 -.015938 .0116389
collgrad | .1904642 .0767865 2.48 0.013 .0399654 .340963
_cons | -.6223347 .0939856 -6.62 0.000 -.8065432 -.4381262
------------------------------------------------------------------------------
Score bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by industry, Rademacher weights:
tenure
z = 0.2980
Prob>|z| = 0.7808
Overriding estimator's cluster/robust settings with cluster(industry age)
Re-running regression with null imposed.
Iteration 0: Log pseudolikelihood = -1334.9102
Iteration 1: Log pseudolikelihood = -1332.3392
Iteration 2: Log pseudolikelihood = -1332.339
Iteration 3: Log pseudolikelihood = -1332.339
Generalized structural equation model Number of obs = 2,217
Response : c_city
Family : Bernoulli
Link : probit
Log pseudolikelihood = -1332.339
( 1) [c_city]tenure = 0
(Std. err. adjusted for 12 clusters in industry)
------------------------------------------------------------------------------
| Robust
| Coefficient std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
c_city |
tenure | 0 (omitted)
wage | .0068643 .0043506 1.58 0.115 -.0016627 .0153913
ttl_exp | -.0021496 .007035 -0.31 0.760 -.015938 .0116389
collgrad | .1904642 .0767865 2.48 0.013 .0399654 .340963
_cons | -.6223347 .0939856 -6.62 0.000 -.8065432 -.4381262
------------------------------------------------------------------------------
Score bootstrap-t, null imposed, 999 replications, Wald test, clustering by industry age, bootstrap clustering by industry, Rademacher weights:
tenure
t(11) = 0.2803
Prob>|t| = 0.7402
(1978 automobile data)
Re-running regression with null imposed.
Initial: Log likelihood = -51.292891
Alternative: Log likelihood = -45.055272
Rescale: Log likelihood = -45.055272
Iteration 0: Log likelihood = -45.055272
Iteration 1: Log likelihood = -29.50801
Iteration 2: Log likelihood = -28.91221
Iteration 3: Log likelihood = -28.908406
Iteration 4: Log likelihood = -28.908406
Score bootstrap-t, null imposed, 999 replications, Wald test, Rademacher weights:
mpg
z = -2.0409
Prob>|z| = 0.0350
note: 12.mpg != 0 predicts failure perfectly;
12.mpg omitted and 2 obs not used.
note: 15.mpg != 0 predicts failure perfectly;
15.mpg omitted and 2 obs not used.
note: 16.mpg != 0 predicts failure perfectly;
16.mpg omitted and 4 obs not used.
note: 19.mpg != 0 predicts failure perfectly;
19.mpg omitted and 8 obs not used.
note: 20.mpg != 0 predicts failure perfectly;
20.mpg omitted and 3 obs not used.
note: 22.mpg != 0 predicts failure perfectly;
22.mpg omitted and 5 obs not used.
note: 23.mpg != 0 predicts success perfectly;
23.mpg omitted and 3 obs not used.
note: 29.mpg != 0 predicts failure perfectly;
29.mpg omitted and 1 obs not used.
note: 31.mpg != 0 predicts success perfectly;
31.mpg omitted and 1 obs not used.
note: 34.mpg != 0 predicts failure perfectly;
34.mpg omitted and 1 obs not used.
note: 35.mpg != 0 predicts success perfectly;
35.mpg omitted and 2 obs not used.
note: 41.mpg != 0 predicts success perfectly;
41.mpg omitted and 1 obs not used.
note: 30.mpg omitted because of collinearity.
Iteration 0: Log likelihood = -26.925192
Iteration 1: Log likelihood = -23.571543
Iteration 2: Log likelihood = -23.565102
Iteration 3: Log likelihood = -23.565102
Probit regression Number of obs = 41
LR chi2(8) = 6.72
Prob > chi2 = 0.5671
Log likelihood = -23.565102 Pseudo R2 = 0.1248
------------------------------------------------------------------------------
foreign | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
mpg |
12 | 0 (empty)
14 | -.9674216 1.075272 -0.90 0.368 -3.074917 1.140073
15 | 0 (empty)
16 | 0 (empty)
17 | 8.35e-16 1.085402 0.00 1.000 -2.127349 2.127349
18 | -.7647097 1.000971 -0.76 0.445 -2.726577 1.197157
19 | 0 (empty)
20 | 0 (empty)
21 | -.2533471 1.052133 -0.24 0.810 -2.31549 1.808795
22 | 0 (empty)
23 | 0 (empty)
24 | -.6744897 1.117851 -0.60 0.546 -2.865437 1.516457
25 | .8416212 1.092553 0.77 0.441 -1.299744 2.982986
26 | -.4307273 1.160041 -0.37 0.710 -2.704367 1.842912
28 | -.4307273 1.160041 -0.37 0.710 -2.704367 1.842912
29 | 0 (empty)
30 | 0 (omitted)
31 | 0 (empty)
34 | 0 (empty)
35 | 0 (empty)
41 | 0 (empty)
|
_cons | -7.14e-16 .8862269 -0.00 1.000 -1.736973 1.736973
------------------------------------------------------------------------------
Re-running regression with null imposed.
note: 12.mpg != 0 predicts failure perfectly;
12.mpg omitted and 2 obs not used.
note: 15.mpg != 0 predicts failure perfectly;
15.mpg omitted and 2 obs not used.
note: 16.mpg != 0 predicts failure perfectly;
16.mpg omitted and 4 obs not used.
note: 19.mpg != 0 predicts failure perfectly;
19.mpg omitted and 8 obs not used.
note: 20.mpg != 0 predicts failure perfectly;
20.mpg omitted and 3 obs not used.
note: 22.mpg != 0 predicts failure perfectly;
22.mpg omitted and 5 obs not used.
note: 23.mpg != 0 predicts success perfectly;
23.mpg omitted and 3 obs not used.
note: 29.mpg != 0 predicts failure perfectly;
29.mpg omitted and 1 obs not used.
note: 31.mpg != 0 predicts success perfectly;
31.mpg omitted and 1 obs not used.
note: 34.mpg != 0 predicts failure perfectly;
34.mpg omitted and 1 obs not used.
note: 35.mpg != 0 predicts success perfectly;
35.mpg omitted and 2 obs not used.
note: 41.mpg != 0 predicts success perfectly;
41.mpg omitted and 1 obs not used.
note: 30.mpg omitted because of collinearity.
Iteration 0: Log likelihood = -26.925192
Iteration 1: Log likelihood = -23.980576
Iteration 2: Log likelihood = -23.974122
Iteration 3: Log likelihood = -23.974122
Probit regression Number of obs = 41
Wald chi2(7) = 5.32
Log likelihood = -23.974122 Prob > chi2 = 0.6215
( 1) [foreign]14.mpg = 0
------------------------------------------------------------------------------
foreign | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
mpg |
12 | 0 (empty)
14 | 0 (omitted)
15 | 0 (empty)
16 | 0 (empty)
17 | .6744897 .7904398 0.85 0.393 -.8747437 2.223723
18 | -.0902199 .6698064 -0.13 0.893 -1.403016 1.222576
19 | 0 (empty)
20 | 0 (empty)
21 | .4211426 .7440976 0.57 0.571 -1.037262 1.879547
22 | 0 (empty)
23 | 0 (empty)
24 | 6.84e-17 .8344386 0.00 1.000 -1.63547 1.63547
25 | 1.516111 .8002314 1.89 0.058 -.0523137 3.084536
26 | .2437624 .8901651 0.27 0.784 -1.500929 1.988454
28 | .2437624 .8901651 0.27 0.784 -1.500929 1.988454
29 | 0 (empty)
30 | 0 (omitted)
31 | 0 (empty)
34 | 0 (empty)
35 | 0 (empty)
41 | 0 (empty)
|
_cons | -.6744897 .4817634 -1.40 0.162 -1.618729 .2697491
------------------------------------------------------------------------------
Rao score (Lagrange multiplier) test:
14.mpg
z = -0.9053
Prob>|z| = 0.3653
Wild bootstrap-t, null imposed, 999 replications, Wald test, bootstrap clustering by year, Webb weights:
post_self=.04
t(7) = 2.0194
Prob>|t| = 0.0791
95% confidence set for null hypothesis expression: [.03874, .07098]
Wild bootstrap-t, null imposed, 9999999 replications, Wald test, bootstrap clustering by year, Webb weights:
post_self=.04
t(7) = 2.0194
Prob>|t| = 0.0757
Wild bootstrap-t, null imposed, 9999 replications, Wald test, bootstrap clustering by year, Normal weights:
post_self=.04
t(7) = 2.0194
Prob>|t| = 0.0665
Wild bootstrap-t, null imposed, 9999 replications, Wald test, bootstrap clustering by year, Gamma weights:
post_self=.04
t(7) = 2.0194
Prob>|t| = 0.0638
Warning: with 8 bootstrap clusters, the number of replications, 9999, exceeds the universe of Mammen draws, 2^8 = 256.
Consider Webb weights instead, using weight(webb).
Wild bootstrap-t, null imposed, 9999 replications, Wald test, bootstrap clustering by year, Mammen weights:
post_self=.04
t(7) = 2.0194
Prob>|t| = 0.1022