--------------------------------------------------------------------------------
name: <unnamed>
log: /Users/baum/Documents/Chuck/ ChuckLitGov4/litgov4-10.smcl
log type: smcl
opened on: 30 Nov 2009, 14:10:18
.
. * litgov4-10 9812
. * cfb calculate change in board size for estimation sample
. * based on dirten5_2.do in litgov2
.
. local ts 9408
. local extdir "/Users/baum/Documents/chuck/ ChuckLitGov4/Paper_datasets9408/
> IRRC_matched_input_revised9408"
. local in1 litgov4_est3`ts'
. local in2 dirinfo_full_long`ts'
. tempname temp
.
. // create flag for desired case_codes
.
. use `in1'
. drop if ~t56s
(28462 observations deleted)
. egen comp=tag(case_code)
. keep if comp
(2831 observations deleted)
. keep case_code dismissed topqssettle
. desc
Contains data from litgov4_est39408.dta
obs: 333
vars: 3 30 Nov 2009 14:08
size: 6,660 (99.9% of memory free)
--------------------------------------------------------------------------------
storage display value
variable name type format label variable label
--------------------------------------------------------------------------------
case_code str7 %9s
dismissed byte %8.0g Suit dismissed
topqssettle float %9.0g Scaled settlement in Q4 (0/1)
--------------------------------------------------------------------------------
Sorted by: case_code
Note: dataset has changed since last saved
. sort case_code
. save `temp',replace
file __000000.dta saved
.
. // read in full director dataset (before pruning late arrivals)
. use "`extdir'/`in2'"
. merge n:1 case_code using `temp'
Result # of obs.
-----------------------------------------
not matched 12,156
from master 12,156 (_merge==1)
from using 0 (_merge==2)
matched 30,558 (_merge==3)
-----------------------------------------
. drop if _merge < 3
(12156 observations deleted)
. drop _merge
.
. // calc board size in each year
. egen tag = tag(case_code suit_year)
. g nmid = !mi(yr)
. // calc number of outsiders
. g outsider = (status_yr == "IND" & !mi(status_yr))
. g nout = nmid & outsider
. forv i=0/4 {
2. qui {
3. bys case_code: egen size`i' = sum(nmid) if suit_year==`i'
4. bys case_code: egen sizeout`i' = sum(nout) if suit_year==`i'
5. replace size`i' = cond(tag, size`i', .)
6. replace sizeout`i' = cond(tag, sizeout`i', .)
7. }
8. }
.
. // calc number of directors not present in yr0
. g seat0 = (caseyr == "_0") if !mi(yr)
(12434 missing values generated)
. g seat4 = (caseyr == "_4") if !mi(yr)
(12434 missing values generated)
. egen sseat0 = sum(seat0), by(dircase)
. egen sseat4 = sum(seat4), by(dircase)
. g newby = max(0, sseat4 - sseat0) if caseyr == "_4"
(25465 missing values generated)
.
. collapse dismissed topqssettle size* (sum) newby, by(case_code)
. su size*
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
size0 | 333 9.501502 3.14281 4 27
sizeout0 | 333 7.099099 3.079142 2 25
size1 | 333 9 3.32841 0 23
sizeout1 | 333 6.846847 3.109057 0 21
size2 | 333 8.792793 3.375453 0 23
-------------+--------------------------------------------------------
sizeout2 | 333 6.855856 3.098329 0 21
size3 | 333 8.984985 2.93654 0 18
sizeout3 | 333 7.063063 2.870538 0 16
size4 | 333 9.243243 2.518185 3 18
sizeout4 | 333 7.36036 2.540489 1 15
. su newby
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
newby | 333 4.117117 2.326143 0 14
. l case_code newby
+------------------+
| case_c~e newby |
|------------------|
1. | AAM__01 7 |
2. | AAPL_01 2 |
3. | ACF__01 0 |
4. | ACF__02 1 |
5. | ACTN_01 5 |
|------------------|
6. | ACXM_01 3 |
7. | ADBE_01 1 |
8. | ADCT_01 6 |
9. | ADPT_01 4 |
10. | ADRX_01 3 |
|------------------|
11. | ADSK_01 3 |
12. | ADVNA01 2 |
13. | AEP__01 1 |
14. | AES__01 8 |
15. | AET__01 2 |
|------------------|
16. | AFFX_01 2 |
17. | AHC__01 4 |
18. | AHG__01 4 |
19. | AHG__02 6 |
20. | AHP__01 3 |
|------------------|
21. | AIF__01 1 |
22. | AKS__01 4 |
23. | ALD__01 5 |
24. | ALO__01 5 |
25. | AMCC_01 2 |
|------------------|
26. | AMGN_01 5 |
27. | AMZN_01 3 |
28. | ANF__01 2 |
29. | ANN__01 3 |
30. | AOC__01 5 |
|------------------|
31. | AOL__01 3 |
32. | AOR__01 7 |
33. | APD__01 6 |
34. | APSO_01 7 |
35. | ARTG_01 8 |
|------------------|
36. | ASHW_01 4 |
37. | ATIS_01 1 |
38. | ATML_01 3 |
39. | ATX__01 3 |
40. | AVA__01 7 |
|------------------|
41. | AVS__01 4 |
42. | AVY__01 3 |
43. | AWA__01 4 |
44. | AXP__01 4 |
45. | AYE__01 5 |
|------------------|
46. | BA___01 6 |
47. | BBI__02 6 |
48. | BBOX_01 3 |
49. | BBY__01 5 |
50. | BE___01 5 |
|------------------|
51. | BHE__01 1 |
52. | BHI__01 6 |
53. | BID__01 6 |
54. | BLS__01 1 |
55. | BMCS_01 2 |
|------------------|
56. | BMY__01 1 |
57. | BOL__01 3 |
58. | BRCM_01 3 |
59. | BRW__01 5 |
60. | BSX__01 7 |
|------------------|
61. | BTGC_01 5 |
62. | BVSN_01 2 |
63. | CACC_01 1 |
64. | CAG__01 8 |
65. | CA___01 4 |
|------------------|
66. | CBIZ_01 2 |
67. | CBM__01 1 |
68. | CB___01 5 |
69. | CCL__01 5 |
70. | CC___01 6 |
|------------------|
71. | CDE__01 5 |
72. | CDN__01 2 |
73. | CECO_01 4 |
74. | CELL_01 5 |
75. | CEN__01 5 |
|------------------|
76. | CERN_01 1 |
77. | CGO__01 9 |
78. | CHB__01 2 |
79. | CHK__01 0 |
80. | CHTR_01 8 |
|------------------|
81. | CITU_01 2 |
82. | CI___01 5 |
83. | CLHB_01 3 |
84. | CLST_01 1 |
85. | CLX__01 6 |
|------------------|
86. | CMA__01 1 |
87. | CMLS_01 1 |
88. | CMM__01 5 |
89. | CMO__01 5 |
90. | CMS__01 7 |
|------------------|
91. | CMVT_01 1 |
92. | CNX__01 7 |
93. | COF__01 5 |
94. | COL__01 9 |
95. | COMS_01 5 |
|------------------|
96. | COVD_01 4 |
97. | CPB__01 5 |
98. | CPN__01 2 |
99. | CPQ__01 4 |
100. | CPRO_01 3 |
|------------------|
101. | CPWR_01 3 |
102. | CREE_01 2 |
103. | CRY__01 3 |
104. | CSCO_01 3 |
105. | CS___01 5 |
|------------------|
106. | CTXS_01 2 |
107. | CU___01 14 |
108. | CVS__01 1 |
109. | CYTC_01 4 |
110. | C____01 5 |
|------------------|
111. | DFC__01 1 |
112. | DFS__01 5 |
113. | DG___01 6 |
114. | DIS__01 6 |
115. | DOW__01 3 |
|------------------|
116. | DPL__01 7 |
117. | DQE__01 5 |
118. | DUK__01 10 |
119. | DYN__01 11 |
120. | EDS__01 7 |
|------------------|
121. | EEX__01 1 |
122. | EE___01 1 |
123. | EFDS_01 5 |
124. | EFII_01 3 |
125. | EIX__01 4 |
|------------------|
126. | EMLX_01 0 |
127. | ENTU_01 4 |
128. | EP___01 9 |
129. | ESST_01 1 |
130. | ETS__01 6 |
|------------------|
131. | EXC__01 3 |
132. | EX___01 7 |
133. | FCAA_01 2 |
134. | FD___01 0 |
135. | FE___01 3 |
|------------------|
136. | FLEX_01 2 |
137. | FLM__02 5 |
138. | FLR__01 7 |
139. | FLS__01 3 |
140. | FMXI_01 4 |
|------------------|
141. | FNV__01 5 |
142. | FON__01 7 |
143. | FTU__01 9 |
144. | GAP__01 1 |
145. | GAS__01 5 |
|------------------|
146. | GEMS_01 7 |
147. | GEN__01 1 |
148. | GILD_01 4 |
149. | GLW__01 2 |
150. | GMST_01 5 |
|------------------|
151. | GRB__01 4 |
152. | GTIS_01 7 |
153. | GTK__01 4 |
154. | GTW__01 1 |
155. | GT___01 6 |
|------------------|
156. | HAL__01 3 |
157. | HAZ__01 3 |
158. | HC___01 6 |
159. | HGR__01 3 |
160. | HOMS_01 3 |
|------------------|
161. | HON__01 3 |
162. | HPH__01 6 |
163. | HRB__01 3 |
164. | HRC__01 2 |
165. | HRC__02 9 |
|------------------|
166. | HUM__01 1 |
167. | HWP__01 4 |
168. | ICN__01 5 |
169. | IDXX_01 3 |
170. | IFMX_01 5 |
|------------------|
171. | IKN__01 7 |
172. | IMCL_01 6 |
173. | IMNR_01 5 |
174. | INSP_01 5 |
175. | INTC_01 3 |
|------------------|
176. | IOM__01 5 |
177. | IO___01 6 |
178. | IPG__01 1 |
179. | ISSX_01 1 |
180. | ITRI_01 3 |
|------------------|
181. | ITWO_01 5 |
182. | IVX__01 2 |
183. | JDEC_01 2 |
184. | JDSU_01 3 |
185. | JNJ__01 7 |
|------------------|
186. | JNPR_01 4 |
187. | JPM__01 10 |
188. | KNE__01 3 |
189. | KO___01 5 |
190. | KR___01 4 |
|------------------|
191. | KSE__01 3 |
192. | LENS_01 0 |
193. | LFCO_01 5 |
194. | LGTO_01 5 |
195. | LH___01 6 |
|------------------|
196. | LMT__01 3 |
197. | LNT__01 5 |
198. | LNY__01 1 |
199. | LTRE_01 4 |
200. | LU___01 3 |
|------------------|
201. | LXK__01 0 |
202. | MAT__01 4 |
203. | MCK__01 7 |
204. | MDM__01 8 |
205. | MDR__01 2 |
|------------------|
206. | MER__01 5 |
207. | MFN__01 7 |
208. | MIK__01 1 |
209. | MOT__01 6 |
210. | MRK__01 4 |
|------------------|
211. | MSC__01 4 |
212. | MSM__01 0 |
213. | MTRS_01 7 |
214. | MWD__01 3 |
215. | NAFC_01 4 |
|------------------|
216. | NBTY_01 1 |
217. | NCS__01 5 |
218. | NETA_01 4 |
219. | NITE_01 3 |
220. | NKE__01 2 |
|------------------|
221. | NOVL_01 5 |
222. | NOVN_01 4 |
223. | NSIT_01 3 |
224. | NU___01 6 |
225. | NVDA_01 2 |
|------------------|
226. | NWL__01 3 |
227. | OCA__01 7 |
228. | OH___01 2 |
229. | OMC__01 2 |
230. | OMG__01 4 |
|------------------|
231. | ONE__01 4 |
232. | ORB__01 4 |
233. | ORCL_01 3 |
234. | OXHP_01 6 |
235. | PCG__01 3 |
|------------------|
236. | PCLN_01 8 |
237. | PCMS_01 3 |
238. | PCTL_01 2 |
239. | PDX__01 5 |
240. | PER__01 5 |
|------------------|
241. | PETM_01 5 |
242. | PG___01 9 |
243. | PHSY_01 5 |
244. | PKI__01 2 |
245. | PLX__01 3 |
|------------------|
246. | PMA__01 8 |
247. | PMTC_01 0 |
248. | PNC__01 5 |
249. | POI__01 7 |
250. | PPD__01 1 |
|------------------|
251. | PSFT_01 4 |
252. | PSSI_01 2 |
253. | PTA__01 5 |
254. | PTEK_01 6 |
255. | PVN__01 5 |
|------------------|
256. | PZN__01 10 |
257. | Q____01 3 |
258. | RAD__01 7 |
259. | RCCC_01 3 |
260. | RCII_01 3 |
|------------------|
261. | REV__01 1 |
262. | RHB__01 4 |
263. | RMBS_01 3 |
264. | ROV__01 5 |
265. | RTNB_01 6 |
|------------------|
266. | RURL_01 2 |
267. | RWY__01 1 |
268. | SAMC_01 2 |
269. | SBL__01 6 |
270. | SCHL_01 2 |
|------------------|
271. | SDTI_01 4 |
272. | SEI__01 8 |
273. | SEPR_01 1 |
274. | SERO_01 4 |
275. | SFE__01 6 |
|------------------|
276. | SGE__01 9 |
277. | SGI__01 1 |
278. | SHG__01 8 |
279. | SIII_01 2 |
280. | SIRI_01 5 |
|------------------|
281. | SLE__01 7 |
282. | SLR__01 4 |
283. | SONE_01 4 |
284. | SPC__01 4 |
285. | SRV__01 1 |
|------------------|
286. | STAF_01 3 |
287. | STEI_01 3 |
288. | SVU__01 4 |
289. | SWC__01 0 |
290. | SYBS_01 3 |
|------------------|
291. | SYKE_01 3 |
292. | SYMC_01 6 |
293. | S____01 4 |
294. | TCSI_01 3 |
295. | TERN_01 2 |
|------------------|
296. | TER__01 2 |
297. | THC__01 7 |
298. | TLAB_01 5 |
299. | TNB__01 1 |
300. | TRL__01 6 |
|------------------|
301. | TSAI_01 4 |
302. | TSO__01 2 |
303. | TXT__01 4 |
304. | TXU__01 7 |
305. | TYC__01 11 |
|------------------|
306. | T____01 7 |
307. | UCR__01 4 |
308. | UCU__01 5 |
309. | UHAL_01 1 |
310. | UICI_01 4 |
|------------------|
311. | UIS__01 1 |
312. | UK___01 9 |
313. | UNH__01 4 |
314. | UNM__02 7 |
315. | UNP__01 5 |
|------------------|
316. | USU__01 4 |
317. | VC___01 4 |
318. | VIAB_01 9 |
319. | VISX_01 2 |
320. | VNWK_01 4 |
|------------------|
321. | VRSN_01 5 |
322. | VRTX_01 3 |
323. | VTA__01 6 |
324. | WAC__01 7 |
325. | WDC__01 4 |
|------------------|
326. | WMB__01 3 |
327. | WMX__01 5 |
328. | WNC__01 3 |
329. | WND__01 4 |
330. | WPI__01 2 |
|------------------|
331. | WR___01 7 |
332. | XEL__01 4 |
333. | XL___01 3 |
+------------------+
.
. forv i=0/4 {
2. di _n "Year `i'"
3. list case_code size`i' sizeout`i' if size`i'==0 | sizeout`i' == 0,
> sep(999)
4. }
Year 0
Year 1
+-----------------------------+
| case_c~e size1 sizeout1 |
|-----------------------------|
77. | CGO__01 0 0 |
88. | CMM__01 0 0 |
113. | DG___01 0 0 |
140. | FMXI_01 0 0 |
150. | GMST_01 0 0 |
157. | HAZ__01 0 0 |
165. | HRC__02 0 0 |
207. | MFN__01 0 0 |
237. | PCMS_01 0 0 |
272. | SEI__01 0 0 |
278. | SHG__01 0 0 |
+-----------------------------+
Year 2
+-----------------------------+
| case_c~e size2 sizeout2 |
|-----------------------------|
41. | AVS__01 0 0 |
77. | CGO__01 0 0 |
88. | CMM__01 0 0 |
100. | CPRO_01 0 0 |
111. | DFC__01 0 0 |
139. | FLS__01 0 0 |
141. | FNV__01 0 0 |
152. | GTIS_01 0 0 |
157. | HAZ__01 0 0 |
162. | HPH__01 0 0 |
165. | HRC__02 0 0 |
207. | MFN__01 0 0 |
230. | OMG__01 0 0 |
269. | SBL__01 0 0 |
272. | SEI__01 0 0 |
276. | SGE__01 0 0 |
278. | SHG__01 0 0 |
324. | WAC__01 0 0 |
+-----------------------------+
Year 3
+-----------------------------+
| case_c~e size3 sizeout3 |
|-----------------------------|
50. | BE___01 0 0 |
162. | HPH__01 0 0 |
207. | MFN__01 0 0 |
272. | SEI__01 0 0 |
276. | SGE__01 0 0 |
278. | SHG__01 0 0 |
+-----------------------------+
Year 4
.
. // changes in overall board size, t=0 to t=4
. // ttest size0 = size4
. g dsize = size0 - size4
. g outrat0 = 100 * sizeout0 / size0
. g outrat4 = 100 * sizeout4 / size4
. su outrat*
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
outrat0 | 333 73.30797 14.61281 25 100
outrat4 | 333 78.68524 12.17671 12.5 100
. g dout = outrat0 - outrat4
. g newdir4 = 100 * newby / size4
. su newdir4
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
newdir4 | 333 44.93131 23.62911 0 100
.
. ttest size0 = size4 if !dismissed
Paired t test
------------------------------------------------------------------------------
Variable | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
size0 | 193 9.398964 .2272864 3.157563 8.950665 9.847263
size4 | 193 9.129534 .1929624 2.68072 8.748935 9.510132
---------+--------------------------------------------------------------------
diff | 193 .2694301 .1601143 2.224379 -.0463789 .585239
------------------------------------------------------------------------------
mean(diff) = mean(size0 - size4) t = 1.6827
Ho: mean(diff) = 0 degrees of freedom = 192
Ha: mean(diff) < 0 Ha: mean(diff) != 0 Ha: mean(diff) > 0
Pr(T < t) = 0.9530 Pr(|T| > |t|) = 0.0941 Pr(T > t) = 0.0470
. local ab = r(mu_2) - r(mu_1)
. mat a = `r(mu_1)',`r(mu_2)',`ab',`r(p)'
. reg dsize dismissed, robust
Linear regression Number of obs = 333
F( 1, 331) = 0.01
Prob > F = 0.9165
R-squared = 0.0000
Root MSE = 2.2661
------------------------------------------------------------------------------
| Robust
dsize | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dismissed | -.0265729 .2532611 -0.10 0.917 -.5247772 .4716314
_cons | .2694301 .1601807 1.68 0.094 -.0456705 .5845306
------------------------------------------------------------------------------
. local fpr = fprob(e(df_m), e(df_r), e(F))
. mat a = a, `fpr'
. mat list a
a[1,5]
c1 c2 c3 c4 c5
r1 9.3989637 9.1295337 -.26943005 .09405173 .91650047
.
. ttest size0 = size4 if dismissed
Paired t test
------------------------------------------------------------------------------
Variable | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
size0 | 140 9.642857 .2643753 3.12813 9.12014 10.16557
size4 | 140 9.4 .1922977 2.275297 9.019793 9.780207
---------+--------------------------------------------------------------------
diff | 140 .2428571 .1962839 2.322463 -.1452311 .6309454
------------------------------------------------------------------------------
mean(diff) = mean(size0 - size4) t = 1.2373
Ho: mean(diff) = 0 degrees of freedom = 139
Ha: mean(diff) < 0 Ha: mean(diff) != 0 Ha: mean(diff) > 0
Pr(T < t) = 0.8910 Pr(|T| > |t|) = 0.2181 Pr(T > t) = 0.1090
. local ab = r(mu_2) - r(mu_1)
. mat b = `r(mu_1)',`r(mu_2)',`ab',`r(p)'
. reg dsize dismissed, robust
Linear regression Number of obs = 333
F( 1, 331) = 0.01
Prob > F = 0.9165
R-squared = 0.0000
Root MSE = 2.2661
------------------------------------------------------------------------------
| Robust
dsize | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dismissed | -.0265729 .2532611 -0.10 0.917 -.5247772 .4716314
_cons | .2694301 .1601807 1.68 0.094 -.0456705 .5845306
------------------------------------------------------------------------------
. local fpr = fprob(e(df_m), e(df_r), e(F))
. mat b = b, .
. mat c = a \ b
. mat list c
c[2,5]
c1 c2 c3 c4 c5
r1 9.3989637 9.1295337 -.26943005 .09405173 .91650047
r1 9.6428571 9.4 -.24285714 .21807123 .
.
. ttest size0 = size4 if topqssettle
Paired t test
------------------------------------------------------------------------------
Variable | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
size0 | 49 8.020408 .3431668 2.402167 7.330425 8.710391
size4 | 49 8.102041 .3047873 2.133511 7.489225 8.714857
---------+--------------------------------------------------------------------
diff | 49 -.0816327 .2809676 1.966773 -.6465559 .4832906
------------------------------------------------------------------------------
mean(diff) = mean(size0 - size4) t = -0.2905
Ho: mean(diff) = 0 degrees of freedom = 48
Ha: mean(diff) < 0 Ha: mean(diff) != 0 Ha: mean(diff) > 0
Pr(T < t) = 0.3863 Pr(|T| > |t|) = 0.7727 Pr(T > t) = 0.6137
. local ab = r(mu_2) - r(mu_1)
. mat aa = `r(mu_1)',`r(mu_2)',`ab',`r(p)'
. reg dsize topqssettle, robust
Linear regression Number of obs = 333
F( 1, 331) = 1.64
Prob > F = 0.2007
R-squared = 0.0039
Root MSE = 2.2617
------------------------------------------------------------------------------
| Robust
dsize | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
topqssettle | -.3985341 .3108089 -1.28 0.201 -1.009944 .2128757
_cons | .3169014 .1371246 2.31 0.021 .0471558 .586647
------------------------------------------------------------------------------
. local fpr = fprob(e(df_m), e(df_r), e(F))
. mat aa = aa, `fpr'
. mat list aa
aa[1,5]
c1 c2 c3 c4 c5
r1 8.0204082 8.1020408 .08163265 .77265391 .20065293
.
. ttest size0 = size4 if !topqssettle
Paired t test
------------------------------------------------------------------------------
Variable | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
size0 | 284 9.757042 .1891733 3.188005 9.384677 10.12941
size4 | 284 9.440141 .1501497 2.530368 9.144589 9.735693
---------+--------------------------------------------------------------------
diff | 284 .3169014 .1369535 2.307982 .0473246 .5864783
------------------------------------------------------------------------------
mean(diff) = mean(size0 - size4) t = 2.3139
Ho: mean(diff) = 0 degrees of freedom = 283
Ha: mean(diff) < 0 Ha: mean(diff) != 0 Ha: mean(diff) > 0
Pr(T < t) = 0.9893 Pr(|T| > |t|) = 0.0214 Pr(T > t) = 0.0107
. local ab = r(mu_2) - r(mu_1)
. mat bb = `r(mu_1)',`r(mu_2)',`ab',`r(p)'
. reg dsize dismissed, robust
Linear regression Number of obs = 333
F( 1, 331) = 0.01
Prob > F = 0.9165
R-squared = 0.0000
Root MSE = 2.2661
------------------------------------------------------------------------------
| Robust
dsize | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dismissed | -.0265729 .2532611 -0.10 0.917 -.5247772 .4716314
_cons | .2694301 .1601807 1.68 0.094 -.0456705 .5845306
------------------------------------------------------------------------------
. local fpr = fprob(e(df_m), e(df_r), e(F))
. mat bb = bb, .
. mat cc = aa \ bb
. mat list cc
cc[2,5]
c1 c2 c3 c4 c5
r1 8.0204082 8.1020408 .08163265 .77265391 .20065293
r1 9.7570423 9.4401408 -.31690141 .02138771 .
. // g dif04 = size0 - size4
. // ttest dif04, by(dismissed)
.
. // changes in outsider board size, t=0 to t=4
. // ttest outrat0 = outrat4
.
. ttest outrat0 = outrat4 if !dismissed
Paired t test
------------------------------------------------------------------------------
Variable | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
outrat0 | 193 72.85908 1.102614 15.318 70.68429 75.03387
outrat4 | 193 78.41631 .9228431 12.82055 76.59609 80.23652
---------+--------------------------------------------------------------------
diff | 193 -5.557228 .8390128 11.65594 -7.212094 -3.902362
------------------------------------------------------------------------------
mean(diff) = mean(outrat0 - outrat4) t = -6.6235
Ho: mean(diff) = 0 degrees of freedom = 192
Ha: mean(diff) < 0 Ha: mean(diff) != 0 Ha: mean(diff) > 0
Pr(T < t) = 0.0000 Pr(|T| > |t|) = 0.0000 Pr(T > t) = 1.0000
. local ab = r(mu_2) - r(mu_1)
. mat d = `r(mu_1)',`r(mu_2)',`ab',`r(p)'
. reg dout dismissed, robust
Linear regression Number of obs = 333
F( 1, 331) = 0.11
Prob > F = 0.7355
R-squared = 0.0003
Root MSE = 11.474
------------------------------------------------------------------------------
| Robust
dout | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dismissed | .4280414 1.265836 0.34 0.735 -2.062056 2.918139
_cons | -5.557228 .8393608 -6.62 0.000 -7.208382 -3.906073
------------------------------------------------------------------------------
. local fpr = fprob(e(df_m), e(df_r), e(F))
. mat d = d, `fpr'
. mat list d
d[1,5]
c1 c2 c3 c4 c5
r1 72.859079 78.416307 5.5572277 3.434e-10 .73546513
.
. ttest outrat0 = outrat4 if dismissed
Paired t test
------------------------------------------------------------------------------
Variable | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
outrat0 | 140 73.92679 1.15031 13.61065 71.65242 76.20115
outrat4 | 140 79.05597 .9519495 11.26362 77.1738 80.93815
---------+--------------------------------------------------------------------
diff | 140 -5.129186 .9480728 11.21775 -7.003695 -3.254678
------------------------------------------------------------------------------
mean(diff) = mean(outrat0 - outrat4) t = -5.4101
Ho: mean(diff) = 0 degrees of freedom = 139
Ha: mean(diff) < 0 Ha: mean(diff) != 0 Ha: mean(diff) > 0
Pr(T < t) = 0.0000 Pr(|T| > |t|) = 0.0000 Pr(T > t) = 1.0000
. local ab = r(mu_2) - r(mu_1)
. mat e = `r(mu_1)',`r(mu_2)',`ab',`r(p)'
. reg dsize dismissed, robust
Linear regression Number of obs = 333
F( 1, 331) = 0.01
Prob > F = 0.9165
R-squared = 0.0000
Root MSE = 2.2661
------------------------------------------------------------------------------
| Robust
dsize | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dismissed | -.0265729 .2532611 -0.10 0.917 -.5247772 .4716314
_cons | .2694301 .1601807 1.68 0.094 -.0456705 .5845306
------------------------------------------------------------------------------
. local fpr = fprob(e(df_m), e(df_r), e(F))
. mat e = e, .
. mat f = d \ e
. mat list f
f[2,5]
c1 c2 c3 c4 c5
r1 72.859079 78.416307 5.5572277 3.434e-10 .73546513
r1 73.926786 79.055973 5.1291863 2.679e-07 .
.
. ttest outrat0 = outrat4 if topqssettle
Paired t test
------------------------------------------------------------------------------
Variable | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
outrat0 | 49 69.35311 2.200372 15.4026 64.92896 73.77725
outrat4 | 49 78.00996 1.627142 11.39 74.73837 81.28155
---------+--------------------------------------------------------------------
diff | 49 -8.656853 1.891804 13.24263 -12.46058 -4.853126
------------------------------------------------------------------------------
mean(diff) = mean(outrat0 - outrat4) t = -4.5760
Ho: mean(diff) = 0 degrees of freedom = 48
Ha: mean(diff) < 0 Ha: mean(diff) != 0 Ha: mean(diff) > 0
Pr(T < t) = 0.0000 Pr(|T| > |t|) = 0.0000 Pr(T > t) = 1.0000
. local ab = r(mu_2) - r(mu_1)
. mat dd = `r(mu_1)',`r(mu_2)',`ab',`r(p)'
. reg dout topqssettle, robust
Linear regression Number of obs = 333
F( 1, 331) = 3.74
Prob > F = 0.0541
R-squared = 0.0142
Root MSE = 11.394
------------------------------------------------------------------------------
| Robust
dout | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
topqssettle | -3.845426 1.989496 -1.93 0.054 -7.759077 .0682238
_cons | -4.811427 .6565258 -7.33 0.000 -6.102916 -3.519938
------------------------------------------------------------------------------
. local fpr = fprob(e(df_m), e(df_r), e(F))
. mat dd = dd, `fpr'
. mat list dd
dd[1,5]
c1 c2 c3 c4 c5
r1 69.353107 78.00996 8.6568533 .00003359 .05410562
.
. ttest outrat0 = outrat4 if !topqssettle
Paired t test
------------------------------------------------------------------------------
Variable | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
outrat0 | 284 73.99032 .8539313 14.39071 72.30945 75.67118
outrat4 | 284 78.80174 .7312158 12.32267 77.36243 80.24106
---------+--------------------------------------------------------------------
diff | 284 -4.811427 .6557067 11.05017 -6.102108 -3.520746
------------------------------------------------------------------------------
mean(diff) = mean(outrat0 - outrat4) t = -7.3378
Ho: mean(diff) = 0 degrees of freedom = 283
Ha: mean(diff) < 0 Ha: mean(diff) != 0 Ha: mean(diff) > 0
Pr(T < t) = 0.0000 Pr(|T| > |t|) = 0.0000 Pr(T > t) = 1.0000
. local ab = r(mu_2) - r(mu_1)
. mat ee = `r(mu_1)',`r(mu_2)',`ab',`r(p)'
. reg dsize dismissed, robust
Linear regression Number of obs = 333
F( 1, 331) = 0.01
Prob > F = 0.9165
R-squared = 0.0000
Root MSE = 2.2661
------------------------------------------------------------------------------
| Robust
dsize | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dismissed | -.0265729 .2532611 -0.10 0.917 -.5247772 .4716314
_cons | .2694301 .1601807 1.68 0.094 -.0456705 .5845306
------------------------------------------------------------------------------
. local fpr = fprob(e(df_m), e(df_r), e(F))
. mat ee = ee, .
. mat ff = dd \ ee
. mat list ff
ff[2,5]
c1 c2 c3 c4 c5
r1 69.353107 78.00996 8.6568533 .00003359 .05410562
r1 73.990317 78.801744 4.8114268 2.306e-12 .
.
. // g difout04 = outrat0 - outrat4
. // ttest difout04, by(dismissed)
.
. // percentage of board seats held by new directors, t=4
.
. ttest newdir4, by(dismissed)
Two-sample t test with equal variances
------------------------------------------------------------------------------
Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
0 | 193 48.90831 1.783461 24.77663 45.39062 52.426
1 | 140 39.44874 1.759444 20.81802 35.97 42.92747
---------+--------------------------------------------------------------------
combined | 333 44.93131 1.294867 23.62911 42.38413 47.47849
---------+--------------------------------------------------------------------
diff | 9.459575 2.575166 4.393819 14.52533
------------------------------------------------------------------------------
diff = mean(0) - mean(1) t = 3.6734
Ho: diff = 0 degrees of freedom = 331
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.9999 Pr(|T| > |t|) = 0.0003 Pr(T > t) = 0.0001
. local ab = r(mu_1) - r(mu_2)
. mat g = `r(mu_1)',`r(mu_2)',`ab',`r(p)'
. mat list g
g[1,4]
c1 c2 c3 c4
r1 48.908312 39.448737 9.459575 .00027905
.
. ttest newdir4, by(topqssettle)
Two-sample t test with equal variances
------------------------------------------------------------------------------
Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
0 | 284 42.47641 1.344421 22.65659 39.83008 45.12274
1 | 49 59.15973 3.478917 24.35242 52.1649 66.15457
---------+--------------------------------------------------------------------
combined | 333 44.93131 1.294867 23.62911 42.38413 47.47849
---------+--------------------------------------------------------------------
diff | -16.68333 3.544015 -23.65496 -9.711693
------------------------------------------------------------------------------
diff = mean(0) - mean(1) t = -4.7075
Ho: diff = 0 degrees of freedom = 331
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.0000 Pr(|T| > |t|) = 0.0000 Pr(T > t) = 1.0000
. local ab = r(mu_2) - r(mu_1)
. mat gg = `r(mu_2)',`r(mu_1)',`ab',`r(p)'
. mat list gg
gg[1,4]
c1 c2 c3 c4
r1 59.159735 42.476409 16.683325 3.693e-06
.
. tempname hh
. file open `hh' using table10.tex, write replace
. file write `hh' "\begin{table}[htbp]\caption{{\bf Change in Board Structure }}
> \bigskip" _n
. file write `hh' "\include{table10t}" _n
. file write `hh' "\begin{tabular}{lrrrrr}" _n "\hline\hline" _n
. // file write `hh' " & & & & \multicolumn{2}{c}{P-value: Difference} \\"
> _n
. // file write `hh' " & & & & \multicolumn{2}{c}{in Sample Means} \\" _n
. file write `hh' " & T=0 & T=4 & Change & P-value: & P-value: \\" _n
. file write `hh' "& & & & Change=0 & Equal change \\" _n
. file write `hh' "\hline" _n
. file write `hh' "\multicolumn{6}{c}{Panel A} \\" _n
. file write `hh' "\hline" _n
. file write `hh' "\multicolumn{6}{c}{Number of Directors} \\" _n
. file write `hh' "Settled & " %9.3f (c[1,1]) " & " %9.3f (c[1,2]) " & " %9.3f
> (c[1,3]) " & " %9.3f (c[1,4]) " & " %9.3f (c[1,5]) " \\" _n
. file write `hh' "Dismissed & " %9.3f (c[2,1]) " & " %9.3f (c[2,2]) " & " %9.3f
> (c[2,3]) " & " %9.3f (c[2,4]) " & \\" _n
. file write `hh' "\hline" _n
. file write `hh' "\multicolumn{6}{c}{Percentage of Outside Directors} \\" _n
. file write `hh' "Settled & " %9.3f (f[1,1]) " & " %9.3f (f[1,2]) " & " %9.3f
> (f[1,3]) " & " %9.3f (f[1,4]) " & " %9.3f (f[1,5]) " \\" _n
. file write `hh' "Dismissed & " %9.3f (f[2,1]) " & " %9.3f (f[2,2]) " & " %9.3f
> (f[2,3]) " & " %9.3f (f[2,4]) " & \\" _n
. file write `hh' "\hline" _n
. file write `hh' "\multicolumn{6}{c}{Percentage of New Directors} \\" _n
. file write `hh' "Settled & & " %9.3f (g[1,1]) " & " %9.3f (g[1,3]) " & " %
> 9.3f (g[1,4]) " \\" _n
. file write `hh' "Dismissed & & " %9.3f (g[1,2]) " \\" _n
.
. file write `hh' "\hline\hline" _n
. file write `hh' "\multicolumn{6}{c}{Panel B} \\" _n
. file write `hh' "\hline" _n
. file write `hh' "\multicolumn{6}{c}{Number of Directors} \\" _n
. file write `hh' "Q4 Scaled Settlement & " %9.3f (cc[1,1]) " & " %9.3f (cc[1,
> 2]) " & " %9.3f (cc[1,3]) " & " %9.3f (cc[1,4]) " & " %9.3f (cc[1,5]) " \\" _n
. file write `hh' "Other & " %9.3f (cc[2,1]) " & " %9.3f (cc[2,2]) " & " %9.3f (
> cc[2,3]) " & " %9.3f (cc[2,4]) " & \\" _n
. file write `hh' "\hline" _n
. file write `hh' "\multicolumn{6}{c}{Percentage of Outside Directors} \\" _n
. file write `hh' "Q4 Scaled Settlement & " %9.3f (ff[1,1]) " & " %9.3f (ff[1
> ,2]) " & " %9.3f (ff[1,3]) " & " %9.3f (ff[1,4]) " & " %9.3f (ff[1,5]) " \\" _
> n
. file write `hh' "Other & " %9.3f (ff[2,1]) " & " %9.3f (ff[2,2]) " & " %9.3f (
> ff[2,3]) " & " %9.3f (ff[2,4]) " & \\" _n
. file write `hh' "\hline" _n
. file write `hh' "\multicolumn{6}{c}{Percentage of New Directors} \\" _n
. file write `hh' "Q4 Scaled Settlement & & " %9.3f (gg[1,1]) " & " %9.3f (
> gg[1,3]) " & " %9.3f (gg[1,4]) " \\" _n
. file write `hh' "Other & & " %9.3f (gg[1,2]) " \\" _n
. file write `hh' "\hline\hline" _n "\end{tabular}" "\medskip" _n
. file write `hh' "\input{table10b}" _n "\end{table}" _n
. file close `hh'
.
.
. log close
name: <unnamed>
log: /Users/baum/Documents/Chuck/ ChuckLitGov4/litgov4-10.smcl
log type: smcl
closed on: 30 Nov 2009, 14:10:21
--------------------------------------------------------------------------------