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by: Noah Scovill

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# Econometrics HW 10 solutions Econ 4400

Marketplace > Ohio State University > Economcs > Econ 4400 > Econometrics HW 10 solutions
Noah Scovill
OSU
GPA 3.25
Elementary Econometrics

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These are the solutions for the HW 10 problem set
COURSE
Elementary Econometrics
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Class Notes
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Econometrics
KARMA
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## Popular in Economcs

This page Class Notes was uploaded by Noah Scovill on Sunday March 20, 2016. The Class Notes belongs to Econ 4400 at Ohio State University taught by Anthony Bradfield in Fall 2015. Since its upload, it has received 31 views. For similar materials see Elementary Econometrics in Economcs at Ohio State University.

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Date Created: 03/20/16
Homework 10 Extra Credit 12032014 Econ 4400102 Due 12092014 Instructor Anthony J Brad eld Name Show all necessary work 1 2 Use the Homework 10 dataset on female workers You want to use the regression equation hoursi 30 Bllnwagei Bzeduci Bgagei B4kidslt6i Bsnwifeinci 81 to explain the wages of female workers However lnwage is an endogenous variable see the class notes You want to run 2SLS to remove the endogeneity problem What is the auxiliary or stage one regression equation The relevant excluded variables are exper and experz lnwagei no meduci nzagei 7t3kidslt6i n4nwifeinci n5experi n6expersqi vi 3 Estimate the auxiliary stage 1 regression equation and use the predicted value as an instrument for lnwage to estimate the full stage 2 model Report your results Source SS df MS Number of obs 428 F 6 421 1369 Model 364697152 6 607828587 Prob gt F 00000 Residual 186857726 421 443842579 R squared 01633 Adj R squared 01514 Total 223327441 427 523015084 Root MSE 66622 lwage Coef Std Err t Pgtt 95 Conf Interval educ 1011113 0149618 676 0000 0717023 1305204 age 0025561 005192 049 0623 0127615 0076492 kidslt6 0532185 0884411 060 0548 2270596 1206225 nwifeinc 00556 0033104 168 0094 0009469 0120669 exper 0418643 0132377 316 0002 015844 0678846 expersq 0007625 0004008 190 0058 0015503 0000253 cons 4471607 2852028 157 0118 100776 1134381 Optional Source SS df MS Number of obs 428 F 5 422 1180 Model 315515909 5 631031817 Prob gt F 00000 Residual 225759429 422 53497495 R squared 01226 Adj R squared 01122 Total 257311020 427 60260192 Root MSE 73142 hours Coef Std Err t Pgtt 95 Conf Interval lwagehat 1639556 2541628 645 0000 1139973 2139138 educ 1837513 3192041 576 0000 2464941 1210085 age 7806092 5065162 154 0124 1776218 2149997 kidslt6 1981543 9880191 201 0046 3923595 3949137 nwifeinc 1016959 3572691 285 0005 1719208 3147104 cons 2225662 310328 717 0000 1615681 2835643 2 Use Stataprogram to estimate 2SLS directly Report the results and compare them to the previous part ivregress 2sls hours educ age kidslt6 nwifeinc lwage exper expersq Instrumental variables 2SLS regression Number of obs 428 Wald chi25 1745 Prob gt chi2 00037 R squared Root MSE 13447 hours Coef Std Err z Pgtz 95 Conf Interval lwage 1639556 4672656 351 0000 7237318 2555379 educ 1837513 5868409 313 0002 29877 6873257 age 7806092 9312048 084 0402 2605737 1044519 kids1t6 1981543 1816424 109 0275 5541669 1578583 nwifeinc 1016959 6568215 155 0122 2304306 2703873 cons 2225662 5705226 390 0000 1107458 3343866 Instrumented lwage Instruments educ age kidslt6 nwifeinc exper expersq The coefficients are all the same The standard errors are different and correct while the standard errors in the previous part are incorrect 2 2 Use the Homework 10 dataset on female workers You want to use the regression equation lwagei 30 Bleduci Bzagei Bgsmsai B4experi Bsexpersqi B6kidslt6i 81 to explain the wages of female workers However you are concerned with an omitted variable ability causing bias in 31 What sign would you expect for the bias in 31 You want to run 2SLS to remove the endogeneity caused by omitting ability What is the auxiliary or stage one regression equation The relevant excluded variables are huseduc motheduc and fath educ bias31 370 positive bias educi no magei nzsmsai ngexperi n4expersqi nskidslt6i n6huseduci n7motheduci ngfatheduci vi 3 Estimate the auxiliary stage 1 regression equation and use the predicted value as an instrument for educ to estimate the full stage 2 model Report your results Source SS df MS Number of obs 428 F 8 419 4027 Model 969459882 8 121182485 Prob gt F 00000 Residual 126073638 419 300891737 R squared 04347 Adj R squared 04239 Total 223019626 427 522294206 Root MSE 17346 educ Coef Std Err t Pgtt 95 Conf Interval age 0124688 0135842 092 0359 0142328 0391704 smsa 0024736 1836444 001 0989 3634527 3585056 exper 043406 0344141 126 0208 0242397 1110518 expersq 0008313 0010444 080 0426 0028842 0012216 kidslt6 4834706 2295894 211 0036 0321801 9347611 huseduc 3696702 0302983 1220 0000 3101147 4292258 motheduc 1191135 0314729 378 0000 057249 180978 fatheduc 1039704 0298648 348 0001 0452669 162674 cons 4968189 7312814 679 0000 3530752 6405627 Source SS df MS Number of obs 428 F 6 421 543 Model 16029941 6 267165684 Prob gt F 00000 Residual 2072975 421 492393111 R squared 00718 Adj R squared 00585 Total 223327441 427 523015084 Root MSE 70171 lwage Coef Std Err t Pgtt 95 Conf Interval educhat 0744483 0238341 312 0002 0275996 121297 age 0014725 0053886 027 0785 0120645 0091195 smsa 0801077 0735301 109 0277 0644241 2246395 exper 0420956 0139837 301 0003 014609 0695821 expersq 000824 0004237 194 0052 0016569 880e 06 kidslt6 0370878 0940764 039 0694 2220057 1478301 cons 0919381 3719766 025 0805 8231008 6392247 Optional 2 Use Stataprogram to estimate 2SLS directly Report the results and compare them to the previous part ivregress 2sls lwage age smsa exper expersq kidslt6 educ huseduc motheduc fatheduc Instrumental variables 2SLS regression Number of obs 428 Wald chi26 3610 Prob gt chi2 00000 R squared 01490 Root MSE 66636 lwage Coef Std Err z Pgtz 95 Conf Interval educ 0744483 0226335 329 0001 0300874 1188092 age 0014725 0051172 029 0774 0115021 008557 smsa 0801077 0698262 115 0251 0567491 2169645 exper 0420956 0132793 317 0002 0160686 0681225 expersq 000824 0004024 205 0041 0016126 0000354 kidslt6 0370878 0893375 042 0678 212186 1380105 cons 0919381 3532391 026 0795 784274 6003979 Instrumented educ Instruments age smsa exper expersq kidslt6 huseduc motheduc fatheduc 2 You want to see if this removed or at least helped the bias that you hypothesized Run the standard OLS regression report your estimate of B1 and if the bias was reducedcorrected reg lwage educ age smsa exper expersq kidslt6 Source SS df MS Number of obs 428 F 6 421 1327 Model 355127956 6 591879926 Prob gt F 00000 Residual 187814645 421 446115547 R squared 01590 Adj R squared 01470 Total 223327441 427 523015084 Root MSE 66792 lwage Coef Std Err t Pgtt 95 Conf Interval educ 106844 0144806 738 0000 0783808 1353073 age 0012308 0051275 024 0810 0113095 0088479 smsa 0559309 0687652 081 0416 0792351 1910969 exper 0402504 0132731 303 0003 0141605 0663402 expersq 0007712 0004023 192 0056 0015619 0000196 kidslt6 0605211 0886509 068 0495 2347747 1137324 cons 4817604 2849578 169 0092 1041878 0783568 31W 2 00744483 lt lms 2 0106844 so the bias is reduced

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