-------------------------------------------------------------------------------- log: C:\Documents and Settings\Patricia_Anderson\My Documents\ECON 20\I > nClass\nov18.log log type: text opened on: 18 Nov 2002, 11:12:15 . use card . reg educ nearc4 reg662-reg669 black smsa south exper married smsa66 Source | SS df MS Number of obs = 3003 -------------+------------------------------ F( 15, 2987) = 183.94 Model | 10331.7303 15 688.782017 Prob > F = 0.0000 Residual | 11185.1495 2987 3.74460982 R-squared = 0.4802 -------------+------------------------------ Adj R-squared = 0.4776 Total | 21516.8798 3002 7.16751492 Root MSE = 1.9351 ------------------------------------------------------------------------------ educ | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- nearc4 | .3037125 .0877701 3.46 0.001 .1316167 .4758084 reg662 | -.0973336 .186641 -0.52 0.602 -.4632916 .2686244 reg663 | -.0598969 .1830091 -0.33 0.743 -.4187335 .2989397 reg664 | .0768867 .2170234 0.35 0.723 -.3486438 .5024172 reg665 | -.2877592 .2178428 -1.32 0.187 -.7148964 .139378 reg666 | -.3430954 .2365777 -1.45 0.147 -.8069671 .1207763 reg667 | -.2653777 .2339345 -1.13 0.257 -.7240667 .1933112 reg668 | .487833 .2678474 1.82 0.069 -.0373512 1.013017 reg669 | .2225615 .202145 1.10 0.271 -.173796 .618919 black | -.8742627 .0946159 -9.24 0.000 -1.059782 -.6887438 smsa | .4179904 .1046608 3.99 0.000 .2127758 .623205 south | -.0501305 .1350098 -0.37 0.710 -.3148522 .2145912 exper | -.4045099 .0089727 -45.08 0.000 -.4221032 -.3869167 married | -.0729057 .0177821 -4.10 0.000 -.1077721 -.0380393 smsa66 | .0283534 .105545 0.27 0.788 -.1785947 .2353016 _cons | 16.82568 .210118 80.08 0.000 16.41369 17.23767 ------------------------------------------------------------------------------ . predict edhat (option xb assumed; fitted values) (7 missing values generated) . predict uhat, resid (7 missing values generated) . reg lwage edhat reg662-reg669 black smsa south exper married smsa66 Source | SS df MS Number of obs = 3003 -------------+------------------------------ F( 15, 2987) = 55.14 Model | 128.144391 15 8.54295941 Prob > F = 0.0000 Residual | 462.816726 2987 .154943664 R-squared = 0.2168 -------------+------------------------------ Adj R-squared = 0.2129 Total | 590.961117 3002 .196855802 Root MSE = .39363 ------------------------------------------------------------------------------ lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- edhat | .1200672 .0587851 2.04 0.041 .0048038 .2353306 reg662 | .0880477 .0383884 2.29 0.022 .0127773 .1633181 reg663 | .1333378 .0375933 3.55 0.000 .0596264 .2070492 reg664 | .0370077 .0442273 0.84 0.403 -.0497114 .1237267 reg665 | .1372811 .0483907 2.84 0.005 .0423985 .2321636 reg666 | .1429272 .0541323 2.64 0.008 .0367868 .2490675 reg667 | .1201503 .0513853 2.34 0.019 .0193962 .2209044 reg668 | -.0886022 .060181 -1.47 0.141 -.2066026 .0293981 reg669 | .1026233 .0428007 2.40 0.017 .0187015 .1865451 black | -.1334458 .0539606 -2.47 0.013 -.2392495 -.0276421 smsa | .129439 .033785 3.83 0.000 .0631948 .1956833 south | -.1524687 .0276577 -5.51 0.000 -.2066987 -.0982387 exper | .0539451 .0238921 2.26 0.024 .0070984 .1007917 married | -.0325035 .0056742 -5.73 0.000 -.0436293 -.0213777 smsa66 | .0195626 .0220035 0.89 0.374 -.023581 .0627062 _cons | 4.147924 .9996627 4.15 0.000 2.187826 6.108021 ------------------------------------------------------------------------------ . reg lwage educ reg662-reg669 black smsa south exper married smsa66 uhat Source | SS df MS Number of obs = 3003 -------------+------------------------------ F( 16, 2986) = 85.38 Model | 185.4956 16 11.593475 Prob > F = 0.0000 Residual | 405.465517 2986 .135788854 R-squared = 0.3139 -------------+------------------------------ Adj R-squared = 0.3102 Total | 590.961117 3002 .196855802 Root MSE = .3685 ------------------------------------------------------------------------------ lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- educ | .1200673 .0550316 2.18 0.029 .0121635 .2279711 reg662 | .0880477 .0359373 2.45 0.014 .0175834 .158512 reg663 | .1333378 .035193 3.79 0.000 .0643329 .2023427 reg664 | .0370077 .0414034 0.89 0.371 -.0441744 .1181897 reg665 | .1372811 .045301 3.03 0.002 .0484569 .2261053 reg666 | .1429272 .0506759 2.82 0.005 .043564 .2422905 reg667 | .1201503 .0481043 2.50 0.013 .0258295 .2144712 reg668 | -.0886023 .0563384 -1.57 0.116 -.1990683 .0218637 reg669 | .1026232 .0400678 2.56 0.010 .0240599 .1811866 black | -.1334457 .0505152 -2.64 0.008 -.2324938 -.0343976 smsa | .129439 .0316278 4.09 0.000 .0674245 .1914535 south | -.1524687 .0258917 -5.89 0.000 -.2032361 -.1017013 exper | .0539451 .0223666 2.41 0.016 .0100897 .0978006 married | -.0325035 .0053119 -6.12 0.000 -.0429189 -.0220881 smsa66 | .0195626 .0205986 0.95 0.342 -.0208263 .0599515 uhat | -.0484611 .0551418 -0.88 0.380 -.156581 .0596587 _cons | 4.147921 .9358337 4.43 0.000 2.312977 5.982865 ------------------------------------------------------------------------------ . reg lwage educ reg662-reg669 black smsa south exper married smsa66, robust Regression with robust standard errors Number of obs = 3003 F( 15, 2987) = 95.54 Prob > F = 0.0000 R-squared = 0.3137 Root MSE = .36848 ------------------------------------------------------------------------------ | Robust lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- educ | .0717996 .0036143 19.87 0.000 .0647129 .0788864 reg662 | .0833827 .0343628 2.43 0.015 .0160056 .1507598 reg663 | .1286953 .0332709 3.87 0.000 .0634591 .1939314 reg664 | .0395169 .0408815 0.97 0.334 -.0406418 .1196755 reg665 | .121161 .0425842 2.85 0.004 .0376637 .2046583 reg666 | .1221401 .0447342 2.73 0.006 .0344271 .209853 reg667 | .10385 .0452879 2.29 0.022 .0150514 .1926486 reg668 | -.0674776 .0494382 -1.36 0.172 -.1644139 .0294587 reg669 | .1124219 .0381303 2.95 0.003 .0376576 .1871863 black | -.1748457 .0181541 -9.63 0.000 -.2104415 -.1392498 smsa | .1510248 .0192725 7.84 0.000 .1132361 .1888136 south | -.1551729 .0281401 -5.51 0.000 -.2103489 -.0999969 exper | .0343849 .0022622 15.20 0.000 .0299493 .0388206 married | -.0360939 .0035725 -10.10 0.000 -.0430988 -.029089 smsa66 | .0259658 .0185838 1.40 0.162 -.0104726 .0624041 _cons | 4.968019 .0706578 70.31 0.000 4.829476 5.106562 ------------------------------------------------------------------------------ . use mroz,clear . desc Contains data from mroz.dta obs: 753 vars: 22 13 Sep 2000 15:33 size: 39,909 (100.0% of memory free) ------------------------------------------------------------------------------- storage display value variable name type format label variable label ------------------------------------------------------------------------------- inlf byte %9.0g =1 if in labor force, 1975 hours int %9.0g hours worked, 1975 kidslt6 byte %9.0g # kids < 6 years kidsge6 byte %9.0g # kids 6-18 age byte %9.0g woman's age in yrs educ byte %9.0g years of schooling wage float %9.0g estimated wage from earns., hours repwage float %9.0g reported wage at interview in 1976 hushrs int %9.0g hours worked by husband, 1975 husage byte %9.0g husband's age huseduc byte %9.0g husband's years of schooling huswage float %9.0g husband's hourly wage, 1975 faminc float %9.0g family income, 1975 mtr float %9.0g fed. marginal tax rate facing woman motheduc byte %9.0g mother's years of schooling fatheduc byte %9.0g father's years of schooling unem float %9.0g unem. rate in county of resid. city byte %9.0g =1 if live in SMSA exper byte %9.0g actual labor mkt exper nwifeinc float %9.0g (faminc - wage*hours)/1000 lwage float %9.0g log(wage) expersq int %9.0g exper^2 ------------------------------------------------------------------------------- Sorted by: . keep if wage~=. (325 observations deleted) . **so this is working married women . **use mom's, dad's and husband's education as instruments . **check the first stage . **original model . reg lwage educ exper expersq, robust Regression with robust standard errors Number of obs = 428 F( 3, 424) = 27.30 Prob > F = 0.0000 R-squared = 0.1568 Root MSE = .66642 ------------------------------------------------------------------------------ | Robust lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- educ | .1074896 .013219 8.13 0.000 .0815068 .1334725 exper | .0415665 .015273 2.72 0.007 .0115462 .0715868 expersq | -.0008112 .0004201 -1.93 0.054 -.0016369 .0000145 _cons | -.5220407 .2016505 -2.59 0.010 -.9183997 -.1256817 ------------------------------------------------------------------------------ . **now do the first stage . reg educ motheduc fatheduc huseduc exper expersq Source | SS df MS Number of obs = 428 -------------+------------------------------ F( 5, 422) = 63.30 Model | 955.830608 5 191.166122 Prob > F = 0.0000 Residual | 1274.36565 422 3.01982382 R-squared = 0.4286 -------------+------------------------------ Adj R-squared = 0.4218 Total | 2230.19626 427 5.22294206 Root MSE = 1.7378 ------------------------------------------------------------------------------ educ | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- motheduc | .1141532 .0307835 3.71 0.000 .0536452 .1746613 fatheduc | .1060801 .0295153 3.59 0.000 .0480648 .1640955 huseduc | .3752548 .0296347 12.66 0.000 .3170049 .4335048 exper | .0374977 .0343102 1.09 0.275 -.0299424 .1049379 expersq | -.0006002 .0010261 -0.58 0.559 -.0026171 .0014167 _cons | 5.538311 .4597824 12.05 0.000 4.634562 6.44206 ------------------------------------------------------------------------------ . predict edhat (option xb assumed; fitted values) . predict uhat, resid . **do Hausman test for endogeneity . reg lwage educ exper expersq uhat, robust Regression with robust standard errors Number of obs = 428 F( 4, 423) = 22.98 Prob > F = 0.0000 R-squared = 0.1622 Root MSE = .66506 ------------------------------------------------------------------------------ | Robust lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- educ | .0803918 .0213686 3.76 0.000 .03839 .1223936 exper | .0430973 .0151037 2.85 0.005 .0134097 .0727849 expersq | -.0008628 .0004156 -2.08 0.038 -.0016796 -.000046 uhat | .047189 .0263068 1.79 0.074 -.0045193 .0988973 _cons | -.1868574 .2977278 -0.63 0.531 -.7720674 .3983527 ------------------------------------------------------------------------------ . **signif at 7% level, let's continue with IV . reg lwage educ exper expersq (motheduc fatheduc huseduc exper expersq), robus > t IV (2SLS) regression with robust standard errors Number of obs = 428 F( 3, 424) = 9.19 Prob > F = 0.0000 R-squared = 0.1495 Root MSE = .6693 ------------------------------------------------------------------------------ | Robust lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- educ | .0803918 .0217033 3.70 0.000 .0377323 .1230512 exper | .0430973 .0153064 2.82 0.005 .0130114 .0731832 expersq | -.0008628 .0004217 -2.05 0.041 -.0016916 -.000034 _cons | -.1868574 .3012625 -0.62 0.535 -.7790113 .4052966 ------------------------------------------------------------------------------ . **get iv residual . predict uhativ, resid . **overID test checks whether instruments are exogenous . reg uhativ motheduc fatheduc huseduc exper expersq Source | SS df MS Number of obs = 428 -------------+------------------------------ F( 5, 422) = 0.22 Model | .494825844 5 .098965169 Prob > F = 0.9537 Residual | 189.439884 422 .448909678 R-squared = 0.0026 -------------+------------------------------ Adj R-squared = -0.0092 Total | 189.93471 427 .444811967 Root MSE = .67001 ------------------------------------------------------------------------------ uhativ | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- motheduc | -.0103852 .0118688 -0.87 0.382 -.0337145 .0129442 fatheduc | .0006734 .0113798 0.06 0.953 -.0216948 .0230417 huseduc | .0067811 .0114259 0.59 0.553 -.0156776 .0292398 exper | .000056 .0132285 0.00 0.997 -.025946 .026058 expersq | -8.88e-06 .0003956 -0.02 0.982 -.0007865 .0007687 _cons | .0086063 .1772724 0.05 0.961 -.3398405 .3570531 ------------------------------------------------------------------------------ . **look at nR2 . display 428*.0026 1.1128 . display chi2tail(2,1.1128) .57326912 . **test for heteroskedasticity . gen uhativsq=uhativ^2 . **B-P test . reg uhativsq motheduc fatheduc huseduc exper expersq, robust Regression with robust standard errors Number of obs = 428 F( 5, 422) = 1.94 Prob > F = 0.0860 R-squared = 0.0291 Root MSE = 1.0483 ------------------------------------------------------------------------------ | Robust uhativsq | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- motheduc | .0119529 .0179702 0.67 0.506 -.0233695 .0472752 fatheduc | .008793 .013905 0.63 0.527 -.0185387 .0361247 huseduc | -.0033767 .015188 -0.22 0.824 -.0332303 .0264769 exper | -.0552427 .0268004 -2.06 0.040 -.1079216 -.0025637 expersq | .0012101 .0006786 1.78 0.075 -.0001238 .002544 _cons | .7297637 .3267098 2.23 0.026 .0875825 1.371945 ------------------------------------------------------------------------------ . use prison, clear . desc Contains data from prison.dta obs: 714 vars: 45 13 Sep 2000 15:34 size: 92,820 (99.9% of memory free) ------------------------------------------------------------------------------- storage display value variable name type format label variable label ------------------------------------------------------------------------------- state byte %9.0g alphabetical; DC = 9 year byte %9.0g 80 to 93 govelec byte %9.0g =1 if gubernatorial election black float %9.0g proportion black metro float %9.0g proportion in metro areas unem float %9.0g proportion unemployed criv float %9.0g violent crimes per 100,000 crip float %9.0g property crimes per 100,000 lcriv float %9.0g log(criv) lcrip float %9.0g log(crip) gcriv float %9.0g lcriv - lcriv_1 gcrip float %9.0g lcrip - lcrip_1 y81 byte %9.0g =1 if year == 81 y82 byte %9.0g y83 byte %9.0g y84 byte %9.0g y85 byte %9.0g y86 byte %9.0g y87 byte %9.0g y88 byte %9.0g y89 byte %9.0g y90 byte %9.0g y91 byte %9.0g y92 byte %9.0g y93 byte %9.0g ag0_14 float %9.0g prop pop 0 to 14 yrs ag15_17 float %9.0g prop pop 15 to 17 yrs ag18_24 float %9.0g prop pop 18 to 24 yrs ag25_34 float %9.0g prop pop 25 to 34 yrs incpc float %9.0g per capita income, nominal polpc float %9.0g police per 100,000 residents gincpc float %9.0g log(incpc) - log(incpc_1) gpolpc float %9.0g lpolpc - lpolpc_1 cag0_14 float %9.0g change in ag0_14 cag15_17 float %9.0g change in ag15_17 cag18_24 float %9.0g change in ag18_24 cag25_34 float %9.0g change in ag25_34 cunem float %9.0g change in unem cblack float %9.0g change in black cmetro float %9.0g change in metro pris float %9.0g prison pop per 100,000 lpris float %9.0g log(pris) gpris float %9.0g lpris - lpris[t-1] final1 byte %9.0g =1 if fnl dec on litig, curr yr final2 byte %9.0g =1 if dec on litig, prev 2 yrs ------------------------------------------------------------------------------- Sorted by: . **look at growth in crime as function of growth in prison population . reg gcriv gpris gincpc y81-y93 Source | SS df MS Number of obs = 714 -------------+------------------------------ F( 15, 698) = 13.51 Model | 1.25815437 15 .083876958 Prob > F = 0.0000 Residual | 4.33335238 698 .006208241 R-squared = 0.2250 -------------+------------------------------ Adj R-squared = 0.2084 Total | 5.59150675 713 .007842225 Root MSE = .07879 ------------------------------------------------------------------------------ gcriv | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- gpris | -.1835158 .047277 -3.88 0.000 -.276338 -.0906936 gincpc | .7460694 .1420064 5.25 0.000 .4672585 1.02488 y81 | -.0755268 .0156378 -4.83 0.000 -.1062295 -.0448241 y82 | -.0544962 .0172838 -3.15 0.002 -.0884306 -.0205617 y83 | -.0602035 .0171329 -3.51 0.000 -.0938416 -.0265654 y84 | -.0357688 .0157648 -2.27 0.024 -.066721 -.0048166 y85 | -.0020018 .016568 -0.12 0.904 -.0345308 .0305273 y86 | .0346097 .0170402 2.03 0.043 .0011535 .0680659 y87 | -.0437328 .0171856 -2.54 0.011 -.0774745 -.009991 y88 | .0100445 .0164809 0.61 0.542 -.0223137 .0424026 y89 | -.0008878 .0161113 -0.06 0.956 -.0325202 .0307447 y90 | .0679607 .0168165 4.04 0.000 .0349436 .1009777 y91 | .0246232 .0178573 1.38 0.168 -.0104374 .0596837 y92 | -.0072702 .0168621 -0.43 0.666 -.0403767 .0258362 y93 | -.0093005 .0177784 -0.52 0.601 -.0442061 .0256051 _cons | -.0022811 .017498 -0.13 0.896 -.0366361 .0320739 ------------------------------------------------------------------------------ . **do a first stage . reg gpris final1 final2 govelec gincpc y81-y93, robust Regression with robust standard errors Number of obs = 714 F( 17, 696) = 7.31 Prob > F = 0.0000 R-squared = 0.1445 Root MSE = .06234 ------------------------------------------------------------------------------ | Robust gpris | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- final1 | -.0751839 .0148643 -5.06 0.000 -.1043681 -.0459997 final2 | -.05291 .0194206 -2.72 0.007 -.09104 -.01478 govelec | -.0095086 .0071415 -1.33 0.183 -.02353 .0045129 gincpc | .1014419 .1645537 0.62 0.538 -.2216393 .4245231 y81 | .0058938 .0149331 0.39 0.693 -.0234254 .0352131 y82 | .0800731 .0162673 4.92 0.000 .0481343 .1120119 y83 | .0695779 .0151347 4.60 0.000 .0398628 .0992931 y84 | .0192226 .0146438 1.31 0.190 -.0095286 .0479739 y85 | .0148375 .0143586 1.03 0.302 -.0133539 .0430288 y86 | .0432926 .0170281 2.54 0.011 .00986 .0767251 y87 | .0211683 .0149641 1.41 0.158 -.0082118 .0505484 y88 | .0179523 .0141484 1.27 0.205 -.0098263 .0457309 y89 | .0210666 .0138081 1.53 0.128 -.006044 .0481771 y90 | .0698559 .0152759 4.57 0.000 .0398635 .0998482 y91 | .0248187 .0154019 1.61 0.108 -.005421 .0550584 y92 | .0184905 .0133625 1.38 0.167 -.0077452 .0447261 y93 | .0082404 .0160753 0.51 0.608 -.0233216 .0398024 _cons | .0347193 .0189697 1.83 0.068 -.0025255 .071964 ------------------------------------------------------------------------------ . predict gprishat (option xb assumed; fitted values) . predict uhat, resid . reg gcriv gpris gincpc y81-y93 uhat, robust Regression with robust standard errors Number of obs = 714 F( 16, 697) = 14.92 Prob > F = 0.0000 R-squared = 0.2356 Root MSE = .07831 ------------------------------------------------------------------------------ | Robust gcriv | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- gpris | -1.071361 .2587703 -4.14 0.000 -1.579424 -.5632982 gincpc | .845796 .1916617 4.41 0.000 .4694925 1.222099 y81 | -.0656052 .0183785 -3.57 0.000 -.101689 -.0295214 y82 | .0161545 .0288811 0.56 0.576 -.0405498 .0728588 y83 | .0052655 .0276626 0.19 0.849 -.0490465 .0595774 y84 | -.0167231 .0159739 -1.05 0.296 -.0480857 .0146396 y85 | .0153742 .0170265 0.90 0.367 -.0180551 .0488035 y86 | .0713064 .022354 3.19 0.001 .0274173 .1151956 y87 | -.0207922 .0187074 -1.11 0.267 -.0575217 .0159373 y88 | .0283462 .0177752 1.59 0.111 -.0065531 .0632455 y89 | .0215179 .0186798 1.15 0.250 -.0151576 .0581933 y90 | .1285485 .0244917 5.25 0.000 .0804621 .176635 y91 | .0482887 .0209074 2.31 0.021 .0072396 .0893379 y92 | .0102759 .0193443 0.53 0.595 -.0277042 .048256 y93 | .0015021 .0195047 0.08 0.939 -.036793 .0397972 uhat | .9118572 .2661202 3.43 0.001 .3893639 1.43435 _cons | .0225431 .0234016 0.96 0.336 -.0234029 .0684892 ------------------------------------------------------------------------------ . **clearly need iv . reg gcriv gpris gincpc y81-y93 (final1 final2 govelec gincpc y81-y93), robust IV (2SLS) regression with robust standard errors Number of obs = 714 F( 15, 698) = 7.98 Prob > F = 0.0000 R-squared = . Root MSE = .09667 ------------------------------------------------------------------------------ | Robust gcriv | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- gpris | -1.071361 .3229889 -3.32 0.001 -1.705507 -.4372147 gincpc | .845796 .2753649 3.07 0.002 .3051533 1.386439 y81 | -.0656052 .0235507 -2.79 0.005 -.1118439 -.0193665 y82 | .0161545 .0366801 0.44 0.660 -.055862 .088171 y83 | .0052655 .0349868 0.15 0.880 -.0634265 .0739574 y84 | -.0167231 .022666 -0.74 0.461 -.0612247 .0277786 y85 | .0153742 .0235045 0.65 0.513 -.0307738 .0615222 y86 | .0713064 .0283275 2.52 0.012 .0156891 .1269238 y87 | -.0207922 .0260134 -0.80 0.424 -.0718662 .0302817 y88 | .0283462 .0239975 1.18 0.238 -.0187697 .0754621 y89 | .0215179 .0244478 0.88 0.379 -.0264822 .069518 y90 | .1285485 .0325573 3.95 0.000 .0646265 .1924706 y91 | .0482887 .0278522 1.73 0.083 -.0063953 .1029728 y92 | .0102759 .0243087 0.42 0.673 -.037451 .0580028 y93 | .0015021 .0272114 0.06 0.956 -.051924 .0549282 _cons | .0225431 .0324943 0.69 0.488 -.0412551 .0863414 ------------------------------------------------------------------------------ . **try overID test . predict uhativ, resid . reg uhativ final1 final2 govelec gincpc y81-y93 Source | SS df MS Number of obs = 714 -------------+------------------------------ F( 17, 696) = 0.01 Model | .002101263 17 .000123604 Prob > F = 1.0000 Residual | 6.5207414 696 .009368881 R-squared = 0.0003 -------------+------------------------------ Adj R-squared = -0.0241 Total | 6.52284267 713 .009148447 Root MSE = .09679 ------------------------------------------------------------------------------ uhativ | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- final1 | -.0019176 .0400665 -0.05 0.962 -.0805833 .076748 final2 | -.0048914 .0284274 -0.17 0.863 -.0607051 .0509223 govelec | .00457 .010364 0.44 0.659 -.0157784 .0249183 gincpc | .0055363 .1748612 0.03 0.975 -.3377823 .3488549 y81 | .0007527 .0194166 0.04 0.969 -.0373695 .0388748 y82 | -.0020089 .021154 -0.09 0.924 -.0435423 .0395244 y83 | .0009758 .0208378 0.05 0.963 -.0399366 .0418882 y84 | -.0001134 .0193549 -0.01 0.995 -.0381144 .0378876 y85 | .0010978 .0205422 0.05 0.957 -.0392343 .0414299 y86 | -.0019654 .0212277 -0.09 0.926 -.0436434 .0397125 y87 | .0010708 .0212598 0.05 0.960 -.0406702 .0428119 y88 | .0001013 .0202415 0.01 0.996 -.0396404 .039843 y89 | .0009747 .0199906 0.05 0.961 -.0382745 .0402239 y90 | -.0019534 .020699 -0.09 0.925 -.0425934 .0386867 y91 | .0012043 .0221191 0.05 0.957 -.0422239 .0446325 y92 | .0001821 .0207188 0.01 0.993 -.0404967 .040861 y93 | .0013111 .0220518 0.06 0.953 -.041985 .0446072 _cons | -.0014941 .0218216 -0.07 0.945 -.0443382 .04135 ------------------------------------------------------------------------------ . display 714*.0003 .2142 . log close log: C:\Documents and Settings\Patricia_Anderson\My Documents\ECON 20\I > nClass\nov18.log log type: text closed on: 18 Nov 2002, 12:17:50 -------------------------------------------------------------------------------