Tarefa paper_repro.card_1995.ex15_4_iv.agent

Veredito

Pergunta feita ao analista

Wooldridge, Introductory Econometrics (7th edition), Example 15.4, Table 15.1, IV column, using Card's (1995) data on men in 1976: estimate by instrumental variables the return to education, the effect of years of schooling on the log hourly wage, using as the instrument whether the man grew up near a four-year college in 1966. Include as exogenous controls years of experience and its square, a race indicator, indicators for living in a standard metropolitan statistical area and in a southern state in 1976, an indicator for living in a metropolitan area in 1966, and the 1966 region-of-residence dummies with the first region omitted. 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
   card— Wooldridge's CARD data set (the R package wooldridge, served by Rdatasets): David Card's (1995) sample of men from the National Longitudinal Survey of Young Men, observed in 1976; one row per man (3,010 rows). Columns used here: lwage, log hourly wage in 1976; educ, years of schooling in 1976; nearc4, 1 if the man grew up near a four-year college in 1966; nearc2, 1 if near a two-year college in 1966; exper, years of experience (age - educ - 6); expersq, exper squared; black, 1 if black; smsa, 1 if living in an SMSA in 1976; south, 1 if living in the South in 1976; smsa66, 1 if living in an SMSA in 1966; reg661 to reg669, indicators of the region of residence in 1966; also age, fatheduc, motheduc, IQ, KWW, married, wage (cents per hour) and the family-background indicators momdad14, sinmom14, step14, south66, libcrd14, enroll, weight, id 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_a8e1f89f nos detalhes

   Example 15.4, Table 15.1, IV column: log wage on education instrumented by college proximity, with experience, its square, race, SMSA, South, SMSA in 1966 and the 1966 region dummies (classical errors with the degrees-of-freedom adjustment, as the example states).

   Por que este é o caminho de referência: Example 15.4, Table 15.1, IV column: log wage on education instrumented by college proximity, with experience, its square, race, SMSA, South, SMSA in 1966 and the 1966 region dummies (classical errors with the degrees-of-freedom adjustment, as the example states).
   Argumentos principais

   dataset:

   "card"

   endog:

   ["educ"]

   exog:

   ["exper","expersq","black","smsa","south","smsa66","reg662","reg663","reg664","reg665","reg666","reg667","reg668","reg669"]

   instruments:

   ["nearc4"]

   level:

   0,950.95

   se:

   "classical"

   y:

   "lwage"

   Resultado calculado

   • coef.educ = 0,131504 0.13150383624470474
     2SLS effect of educ on lwage
   Diagnósticos: first_stage_F.educ = 13,255813.255785330576108 (Wald F of the excluded instruments in the first stage for educ (classical (homoskedastic) standard errors)) · partial_r2.educ = 0,004407930.004407934102325983 (share of the variation the instruments explain beyond the controls) · weak_instrument = false (every first-stage F is at least 10) · n_obs = 3.0103010 · 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: 0,1315
   Figuras citadas: call_a8e1f89f: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, 1976
   • ✓
     #4argumento igualendog

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

     the instrument: near a four-year college in 1966
   • ✓
     #6argumento igualexog

     the example's exogenous controls
   • ✓
     #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 15.4, Table 15.1, IV column, using Card's (1995) data on men in 1976: estimate by instrumental variables the return to education, the effect of years of schooling on the log hourly wage, using as the instrument whether the man grew up near a four-year college in 1966. Include as exogenous controls years of experience and its square, a race indicator, indicators for living in a standard metropolitan statistical area and in a southern state in 1976, an indicator for living in a metropolitan area in 1966, and the 1966 region-of-residence dummies with the first region omitted. Report the coefficient on years of schooling.

   card (agent)

   Wooldridge's CARD data set (the R package wooldridge, served by Rdatasets): David Card's (1995) sample of men from the National Longitudinal Survey of Young Men, observed in 1976; one row per man (3,010 rows). Columns used here: lwage, log hourly wage in 1976; educ, years of schooling in 1976; nearc4, 1 if the man grew up near a four-year college in 1966; nearc2, 1 if near a two-year college in 1966; exper, years of experience (age - educ - 6); expersq, exper squared; black, 1 if black; smsa, 1 if living in an SMSA in 1976; south, 1 if living in the South in 1976; smsa66, 1 if living in an SMSA in 1966; reg661 to reg669, indicators of the region of residence in 1966; also age, fatheduc, motheduc, IQ, KWW, married, wage (cents per hour) and the family-background indicators momdad14, sinmom14, step14, south66, libcrd14, enroll, weight, id and rownames.

   file 326dad817467b20d51bdfd015033e3e3e313931f51f812c38c0776249e7fd711 · frame —
2. 1Turno do modelo
   call_78ec641ereferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   Example 15.4, Table 15.1, IV column: log wage on education instrumented by college proximity, with experience, its square, race, SMSA, South, SMSA in 1966 and the 1966 region dummies (classical errors with the degrees-of-freedom adjustment, as the example states).

   Chamadas pedidas

   • infer.iv_2sls

     {
       "dataset": "card",
       "endog": [
         "educ"
       ],
       "exog": [
         "exper",
         "expersq",
         "black",
         "smsa",
         "south",
         "smsa66",
         "reg662",
         "reg663",
         "reg664",
         "reg665",
         "reg666",
         "reg667",
         "reg668",
         "reg669"
       ],
       "instruments": [
         "nearc4"
       ],
       "se": "classical",
       "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_a8e1f89fdecidido pelo modeloreexecutável

   Motivo: Example 15.4, Table 15.1, IV column: log wage on education instrumented by college proximity, with experience, its square, race, SMSA, South, SMSA in 1966 and the 1966 region dummies (classical errors with the degrees-of-freedom adjustment, as the example states).

   Argumentos

   {
     "cluster": null,
     "dataset": "card",
     "endog": [
       "educ"
     ],
     "exog": [
       "exper",
       "expersq",
       "black",
       "smsa",
       "south",
       "smsa66",
       "reg662",
       "reg663",
       "reg664",
       "reg665",
       "reg666",
       "reg667",
       "reg668",
       "reg669"
     ],
     "instruments": [
       "nearc4"
     ],
     "level": 0.95,
     "se": "classical",
     "subset": null,
     "y": "lwage"
   }

   Figuras call_a8e1f89f

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.educ 2SLS effect of educ on lwage	0,131504	0.13150383624470474	0,05496370.054963672601175226	0,0237335 – 0,239274

   Diagnósticos

   • first_stage_F.educ = 13,255813.255785330576108 (Wald F of the excluded instruments in the first stage for educ (classical (homoskedastic) standard errors))
   • partial_r2.educ = 0,004407930.004407934102325983 (share of the variation the instruments explain beyond the controls)
   • weak_instrument = false (every first-stage F is at least 10)
   • n_obs = 3.0103010
   • se_type = "classical" (classical (homoskedastic) standard errors)
   • n_clusters = — (errors not clustered)

   Derivação matemática e pressupostos

   coef.educ

   1. Com um instrumento e um regressor endógeno, o MQ2E é a razão entre o coeficiente de nearc4 na forma reduzida (lwage sobre nearc4 e os controles) e no primeiro estágio (educ sobre nearc4 e os controles).β = ρ / π→ 0,131504 (0.13150383624470474)
   2. Erro-padrão clássico (homocedástico): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,0549637 (0.054963672601175226)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 2994 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": 5.479516553400695,
         "ci_low": 1.8527852634542328,
         "coef": 3.666150908427464,
         "p": 0.00007538415974785308,
         "se": 0.9248295310138733,
         "t": 3.9641369414407985,
         "term": "Intercept"
       },
       {
         "ci_high": 0.1546598064790368,
         "ci_low": 0.06188240572195053,
         "coef": 0.10827110610049366,
         "p": 0.000004922903208547169,
         "se": 0.023658571085405363,
         "t": 4.576400903911081,
         "term": "exper"
       },
       {
         "ci_high": -0.001681030950206941,
         "ci_low": -0.0029888444048044374,
         "coef": -0.002334937677505689,
         "p": 3.1161246944085553e-12,
         "se": 0.00033349713755244625,
         "t": -7.001372469466829,
         "term": "expersq"
       },
       {
         "ci_high": -0.04109124105905192,
         "ci_low": -0.25246025330930105,
         "coef": -0.1467757471841765,
         "p": 0.006504376545512747,
         "se": 0.05389985881009377,
         "t": -2.7231193258096242,
         "term": "black"
       },
       {
         "ci_high": 0.1738897625009303,
         "ci_low": 0.04972685470035713,
         "coef": 0.11180830860064371,
         "p": 0.0004197459454284399,
         "se": 0.03166198833336174,
         "t": 3.5313103972953286,
         "term": "smsa"
       },
       {
         "ci_high": -0.09117299500874089,
         "ci_low": -0.19817000636948712,
         "coef": -0.144671500689114,
         "p": 1.2266258857565786e-7,
         "se": 0.027284622963645526,
         "t": -5.302308955556272,
         "term": "south"
       },
       {
         "ci_high": 0.06090028882943441,
         "ci_low": -0.023838079835042367,
         "coef": 0.01853110449719602,
         "p": 0.39119278809555685,
         "se": 0.021608588970484666,
         "t": 0.8575804983151744,
         "term": "smsa66"
       },
       {
         "ci_high": 0.17466030000544155,
         "ci_low": 0.026875261834297726,
         "coef": 0.10076778091986964,
         "p": 0.007538241795889867,
         "se": 0.0376857165904629,
         "t": 2.6738984962109194,
         "term": "reg662"
       },
       {
         "ci_high": 0.2204423366673024,
         "ci_low": 0.07607522011011421,
         "coef": 0.1482587783887083,
         "p": 0.000057840797772050855,
         "se": 0.03681413427830229,
         "t": 4.027224360837132,
         "term": "reg663"
       },
       {
         "ci_high": 0.13566022759527543,
         "ci_low": -0.035866069886474596,
         "coef": 0.049897078854400416,
         "p": 0.25405688043284264,
         "se": 0.04373982315600082,
         "t": 1.1407700181237441,
         "term": "reg664"
       },
       {
         "ci_high": 0.23855286385627722,
         "ci_low": 0.05399096224877149,
         "coef": 0.14627191305252435,
         "p": 0.001901645058086012,
         "se": 0.04706394912130858,
         "t": 3.107939639223742,
         "term": "reg665"
       },
       {
         "ci_high": 0.2646849853540353,
         "ci_low": 0.06112089839772622,
         "coef": 0.16290294187588075,
         "p": 0.001716385181435308,
         "se": 0.05190957477135003,
         "t": 3.1382060553073585,
         "term": "reg666"
       },
       {
         "ci_high": 0.2314381083788585,
         "ci_low": 0.03770631068276323,
         "coef": 0.13457220953081087,
         "p": 0.006486970827583493,
         "se": 0.04940230562501896,
         "t": 2.72400665977539,
         "term": "reg667"
       },
       {
         "ci_high": 0.03325735213331668,
         "ci_low": -0.1994113382810437,
         "coef": -0.08307699307386351,
         "p": 0.161550835975611,
         "se": 0.05933135339638065,
         "t": -1.4002207655511092,
         "term": "reg668"
       },
       {
         "ci_high": 0.1898006809461838,
         "ci_low": 0.0258277843270978,
         "coef": 0.10781423263664079,
         "p": 0.009971984091442317,
         "se": 0.041813678752432255,
         "t": 2.578444084649437,
         "term": "reg669"
       },
       {
         "ci_high": 0.23927422232576134,
         "ci_low": 0.023733450163648154,
         "coef": 0.13150383624470474,
         "p": 0.016792621890736437,
         "se": 0.054963672601175226,
         "t": 2.392559121711473,
         "term": "educ"
       }
     ],
     "first_stage": [
       {
         "ci_high": 0.4921785043603216,
         "ci_low": 0.147619375822603,
         "coef": 0.3198989400914623,
         "endog": "educ",
         "instrument": "nearc4",
         "p": 0.00027634008572957033,
         "se": 0.08786381779522222,
         "t": 3.640849534185134
       }
     ],
     "reduced_form": [
       {
         "ci_high": 0.07751371396063084,
         "ci_low": 0.006622161704650145,
         "coef": 0.04206793783264049,
         "instrument": "nearc4",
         "p": 0.020027629759561513,
         "se": 0.01807760095364329,
         "t": 2.3270752540957202
       }
     ]
   }

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   card 4ec4da8d7939f11a46d67bd248416cc7330bd0dddaddcd6c49c9872c5a427413

   sha256 do resultado

   b802de3a11811db9bcfea8a78ae95e51fb522bd2f6317a6de14b55029f51e0f7

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_2309ed95referenceFim: 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 = 0,1315.",
       "caveats": [],
       "figure_refs": [
         "call_a8e1f89f:coef.educ"
       ]
     }

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

   Números encontrados

   0,1315

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.iv_2sls): coef.educ = 0,1315.

   Figuras citadas: call_a8e1f89f:coef.educ · Parada: answered

   Figura da resposta: call_a8e1f89f:coef.educ = 0,1315040.13150383624470474 (se 0,05496370.054963672601175226)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta escolhidapassouobrigatóriaanswer figure call_a8e1f89f: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 = ['nearc4']
13. 12Pontuação
    #6argumento igualpassouobrigatóriainfer.iv_2sls.exog = ['exper', 'expersq', 'black', 'smsa', 'south', 'smsa66', 'reg662', 'reg663', 'reg664', 'reg665', 'reg666', 'reg667', 'reg668', 'reg669']
14. 13Pontuação
    #7diagnósticopassouobrigatória3010 vs 3010
15. 14Pontuação
    #8figura da resposta próximapassouobrigatória0.1315038362 vs 0.13150383624451933
16. 15Pontuação
    #9figura da resposta próximapassouregistrada0.0549636726 vs 0.054963672601153514
17. 16Pontuação
    #10figura da resposta próximapassouregistrada0.1315038362 vs 0.132
18. 17Pontuação
    #11figura da resposta próximapassouregistrada0.0549636726 vs 0.055

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• época 1, tentativa 1passoupontuada(exibindo)

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