Tarefa paper_repro.mroz_1987.ex15_5.agent

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Pergunta feita ao analista

Wooldridge, Introductory Econometrics (7th edition), Example 15.5, using Mroz's (1987) data on married women: among the women who worked in 1975 (those with an observed wage), estimate by two-stage least squares the log hourly wage on years of schooling, years of experience and experience squared, using both the mother's and the father's years of schooling as instruments for schooling. Report the coefficient on years of schooling.

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1. 0Início

   Wooldridge, Introductory Econometrics (7th edition), Example 15.5, using Mroz's (1987) data on married women: among the women who worked in 1975 (those with an observed wage), estimate by two-stage least squares the log hourly wage on years of schooling, years of experience and experience squared, using both the mother's and the father's years of schooling as instruments for schooling. Report the coefficient on years of schooling.

   mroz (agent)

   Wooldridge's MROZ data set (the R package wooldridge, served by Rdatasets): Mroz's (1987) sample of married women from the Panel Study of Income Dynamics, for 1975; one row per woman (753 rows). Columns used here: inlf, 1 if the woman was in the labour force in 1975; lwage, log hourly wage (missing for women who did not work); wage, hourly wage; educ, years of schooling; exper, years of labour-market experience; expersq, exper squared; fatheduc, father's years of schooling; motheduc, mother's years of schooling; also hours, kidslt6, kidsge6, age, repwage, hushrs, husage, huseduc, huswage, faminc, mtr, unem, city, nwifeinc and rownames.

   file bcfc04b6a87746b185ed972f1149c459cc1af5a6ff44443ce5dd004eb8204a78 · frame —
2. 1Turno do modelo
   call_45194e0creferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   Example 15.5: 2SLS of the log wage on education, experience and its square, with mother's and father's education as instruments, for the working women.

   Chamadas pedidas

   • infer.iv_2sls

     {
       "dataset": "mroz",
       "endog": [
         "educ"
       ],
       "exog": [
         "exper",
         "expersq"
       ],
       "instruments": [
         "motheduc",
         "fatheduc"
       ],
       "se": "classical",
       "subset": "inlf == 1",
       "y": "lwage"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.iv_2slsv0.1.0call_685d19b4decidido pelo modeloreexecutável

   Motivo: Example 15.5: 2SLS of the log wage on education, experience and its square, with mother's and father's education as instruments, for the working women.

   Argumentos

   {
     "cluster": null,
     "dataset": "mroz",
     "endog": [
       "educ"
     ],
     "exog": [
       "exper",
       "expersq"
     ],
     "instruments": [
       "motheduc",
       "fatheduc"
     ],
     "level": 0.95,
     "se": "classical",
     "subset": "inlf == 1",
     "y": "lwage"
   }

   Figuras call_685d19b4

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.educ 2SLS effect of educ on lwage	0,0613966	0.06139662866015408	0,03143670.031436695644695214	-0,000394545 – 0,123188

   Diagnósticos

   • first_stage_F.educ = 55,400355.40030042777673
   • partial_r2.educ = 0,2075690.20756926964482014
   • weak_instrument = false
   • overid_p = 0,5386370.5386372330714904
   • sargan = 0,3780710.3780713419638187
   • sargan_df = 11
   • n_obs = 428428
   • se_type = "classical"
   • n_clusters = —

   Tabelas (3)

   {
     "coefficients": [
       {
         "ci_high": 0.8349750469784886,
         "ci_low": -0.7387744331141283,
         "coef": 0.048100306932180106,
         "p": 0.9044194793612478,
         "se": 0.4003280776041123,
         "t": 0.12015221919994053,
         "term": "Intercept"
       },
       {
         "ci_high": 0.07057292697452149,
         "ci_low": 0.017767858923003838,
         "coef": 0.04417039294876266,
         "p": 0.0010918384252699615,
         "se": 0.013432475529443424,
         "t": 3.2883285625157486,
         "term": "exper"
       },
       {
         "ci_high": -0.00010942651309300572,
         "ci_low": -0.001688512663218038,
         "coef": -0.0008989695881555219,
         "p": 0.025740027334257214,
         "se": 0.0004016856118761863,
         "t": -2.2379930014337086,
         "term": "expersq"
       },
       {
         "ci_high": 0.12318780219307032,
         "ci_low": -0.00039454487276216477,
         "coef": 0.06139662866015408,
         "p": 0.05147417391505394,
         "se": 0.031436695644695214,
         "t": 1.9530242412902723,
         "term": "educ"
       }
     ],
     "first_stage": [
       {
         "ci_high": 0.22815007550124894,
         "ci_low": 0.08704398999594125,
         "coef": 0.1575970327485951,
         "endog": "educ",
         "instrument": "motheduc",
         "p": 0.000014298403672048712,
         "se": 0.03589411554668934,
         "t": 4.390609166664114
       },
       {
         "ci_high": 0.25589971614954615,
         "ci_low": 0.12319710416036223,
         "coef": 0.1895484101549542,
         "endog": "educ",
         "instrument": "fatheduc",
         "p": 3.561512366875694e-8,
         "se": 0.03375646678192402,
         "t": 5.615173275671551
       }
     ],
     "reduced_form": [
       {
         "ci_high": 0.02756810644924526,
         "ci_low": -0.021429317804883524,
         "coef": 0.0030693943221808697,
         "instrument": "motheduc",
         "p": 0.8055968958254257,
         "se": 0.012463808356456057,
         "t": 0.24626456331791813
       },
       {
         "ci_high": 0.04045959911766131,
         "ci_low": -0.0056198180526244,
         "coef": 0.017419890532518457,
         "instrument": "fatheduc",
         "p": 0.1379859225819088,
         "se": 0.01172153503026715,
         "t": 1.48614413449579
       }
     ]
   }

   Digests dos dados

   mroz 909e3d99ac8070dc661ce7915c99f27631797719369832e227d4005096cbd479

   sha256 do resultado

   bd94b15a76cbcf37dfcd4fe4fc3d74d550edcb08d04b01f49309133e0d215e1e

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_ca4e37dcreferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Estimativa (infer.iv_2sls): coef.educ = 0,06140.",
       "caveats": [],
       "figure_refs": [
         "call_685d19b4:coef.educ"
       ]
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
5. 4Guarda
   resposta aceita

   Números encontrados

   0,06140

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
6. 5Resposta

   Estimativa (infer.iv_2sls): coef.educ = 0,06140.

   Figuras citadas: call_685d19b4:coef.educ · Parada: answered

   Figura da resposta: call_685d19b4:coef.educ = 0,06139660.06139662866015408 (se 0,03143670.031436695644695214)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta escolhidapassouobrigatóriaanswer figure call_685d19b4:coef.educ (infer.iv_2sls)
9. 8Pontuação
   #2ferramenta chamadapassouobrigatóriaanswer call is infer.iv_2sls
10. 9Pontuação
    #3argumento igualpassouobrigatóriainfer.iv_2sls.y = 'lwage'
11. 10Pontuação
    #4argumento igualpassouobrigatóriainfer.iv_2sls.endog = ['educ']
12. 11Pontuação
    #5argumento igualpassouobrigatóriainfer.iv_2sls.instruments = ['motheduc', 'fatheduc']
13. 12Pontuação
    #6argumento igualpassouobrigatóriainfer.iv_2sls.exog = ['exper', 'expersq']
14. 13Pontuação
    #7diagnósticopassouobrigatória428 vs 428
15. 14Pontuação
    #8figura da resposta próximapassouobrigatória0.06139662866 vs 0.061396628660153496
16. 15Pontuação
    #9figura da resposta próximapassouregistrada0.03143669564 vs 0.0314366956446951
17. 16Pontuação
    #10figura da resposta próximapassouregistrada0.06139662866 vs 0.061
18. 17Pontuação
    #11figura da resposta próximapassouregistrada0.03143669564 vs 0.031

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Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.

• época 1, tentativa 1passoupontuada(exibindo)

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