Tarefa paper_repro.mroz_1987.ex15_5.agent

Veredito

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.

Como o agente resolveu e como foi validado

Resumo passo a passo do que o agente executou, por que o caminho está correto e como cada verificação conferiu o resultado.

1. 1Preparação da base de dados
   mroz— 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.
2. 2Chamada que gerou a resposta finalinfer.iv_2sls(Variáveis instrumentais por mínimos quadrados em dois estágios (MQ2E))citada na respostacoincide com a referência

   Ver chamada call_685d19b4 nos detalhes

   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.

   Por que este é o caminho de referência: 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 principais

   dataset:

   "mroz"

   endog:

   ["educ"]

   exog:

   ["exper","expersq"]

   instruments:

   ["motheduc","fatheduc"]

   level:

   0,950.95

   se:

   "classical"

   subset:

   "inlf == 1"

   y:

   "lwage"

   Resultado calculado

   • coef.educ = 0,0613966 0.06139662866015408
     2SLS effect of educ on lwage
   Diagnósticos: first_stage_F.educ = 55,400355.40030042777673 (Wald F of the excluded instruments in the first stage for educ (classical (homoskedastic) standard errors)) · partial_r2.educ = 0,2075690.20756926964482014 (share of the variation the instruments explain beyond the controls) · weak_instrument = false (every first-stage F is at least 10) · overid_p = 0,5386370.5386372330714904 (Sargan test of the overidentifying restrictions (assumes homoskedastic errors)) · sargan = 0,3780710.3780713419638187 (n·R² of the structural residuals on the instruments) · sargan_df = 11 · n_obs = 428428 · se_type = "classical" (classical (homoskedastic) standard errors)
3. 3Conferência de lastro e resposta final

   Todos os 1 números escritos na resposta foram conferidos contra as ferramentas e o enunciado (nenhum número sem lastro).

   Números encontrados: 0,06140
   Figuras citadas: call_685d19b4:coef.educ
   • ✓
     #0resposta com lastro

     Garante que todo número citado no texto veio de uma ferramenta ou do enunciado (sem cálculo mental do modelo).
   • ✓
     #1figura da resposta escolhida

     an answer figure: the effect of an estimator call the answer cites
   • ✓
     #2ferramenta chamadainfer.iv_2sls

     Exige que o resultado venha da ferramenta infer.iv_2sls.
   • ✓
     #3argumento igualy

     outcome: log hourly wage
   • ✓
     #4argumento igualendog

     the endogenous regressor: years of schooling
   • ✓
     #5argumento igualinstruments

     the instruments: both parents' schooling
   • ✓
     #6argumento igualexog

     experience and its square
   • ✓
     #7diagnósticon_obs

     Confere o diagnóstico "n_obs" reportado pela ferramenta.
   • ✓
     #8figura da resposta próxima

     software reference
   • ✓
     #9figura da resposta próxima

     software reference, SE: recorded, not required (the question does not state the convention)
   • ✓
     #10figura da resposta próxima

     published cell: recorded, never required on T2/T3
   • ✓
     #11figura da resposta próxima

     published cell, SE: recorded, never required on T2/T3

Valores esperados e de onde vêm

Verificações

Caminho de referência e caminho do agente

A chamada de referência ao lado da chamada que deu a resposta, argumento por argumento.

Trajetória

Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.

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_2sls— Variáveis instrumentais por mínimos quadrados em dois estágios (MQ2E)v0.1.0call_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 (Wald F of the excluded instruments in the first stage for educ (classical (homoskedastic) standard errors))
   • partial_r2.educ = 0,2075690.20756926964482014 (share of the variation the instruments explain beyond the controls)
   • weak_instrument = false (every first-stage F is at least 10)
   • overid_p = 0,5386370.5386372330714904 (Sargan test of the overidentifying restrictions (assumes homoskedastic errors))
   • sargan = 0,3780710.3780713419638187 (n·R² of the structural residuals on the instruments)
   • sargan_df = 11
   • n_obs = 428428
   • se_type = "classical" (classical (homoskedastic) standard errors)
   • n_clusters = — (errors not clustered)

   Derivação matemática e pressupostos

   coef.educ

   1. Coeficiente de educ por mínimos quadrados em dois estágios: lwage sobre as projeções dos regressores endógenos nos instrumentos ['motheduc', 'fatheduc'] e os controles; resíduos com os valores observados de educ.→ 0,0613966 (0.06139662866015408)
   2. Erro-padrão clássico (homocedástico): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,0314367 (0.031436695644695214)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 424 graus de liberdade vezes o erro-padrão.IC = β ± c · ep

   Pressupostos do estimador

   • the instruments affect the outcome only through the endogenous regressors (exclusion restriction)
   • the instruments are as good as randomly assigned given the controls
   • errors are independent and have the same variance for every observation

   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
       }
     ]
   }

   Hashes SHA-256 e metadados de reprodução

   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

Épocas × verificações

Tentativas

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

• época 1, tentativa 1passoupontuada(exibindo)

Downloads desta tarefa