Tarefa paper_repro.lmb_2004.tab1_c3.agent

Veredito

Pergunta feita ao analista

Lee, Moretti and Butler (2004), 'Do Voters Affect or Elect Policies? Evidence from the U.S. House', Table I, column (3): in the paper's close-elections sample, the House elections at time t in which the Democratic candidate's share of the two-party vote was strictly between 48 and 52 percent, estimate the difference in the probability that the Democratic candidate wins the seat's next election (t+1) between districts where the Democratic candidate's share at t was strictly between 50 and 52 percent and districts where it was strictly between 48 and 50 percent. Estimate it as the coefficient of a regression of an indicator of a Democratic win at t+1 on an indicator of a Democratic win at t, with no other regressors. Report that difference.

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
   lmb— U.S. House elections and roll-call voting, the extract of the data of Lee, Moretti and Butler (2004), 'Do Voters Affect or Elect Policies? Evidence from the U.S. House', distributed with the causaldata R package: one row per district and congressional session (13,588 rows). Each row pairs a district's election with the one before it for the same seat. Columns: rownames, the row number; state, ICPSR state code; district, district code; id, an election identifier; score, the adjusted ADA voting score of the district's representative in the session that follows the row's election (0 to 100, higher means a more liberal roll-call record); year, year of the election; demvoteshare, the Democratic candidate's share of the two-party vote in the row's election; democrat, 1 if the Democratic candidate won the row's election; lagdemvoteshare, the Democratic share of the two-party vote in the previous election for the same seat; lagdemocrat, 1 if the Democratic candidate won that previous election. In the paper's notation the previous election is time t and the row's election, and the session after it, are time t+1.
2. 2Chamada que gerou a resposta finalinfer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na respostacoincide com a referência

   Ver chamada call_1d90f670 nos detalhes

   Table I, column (3): the difference in the probability of a Democratic win at t+1 between districts the Democrat won and lost at t, among elections with the Democratic share at t strictly between 48 and 52 percent (915 observations), as the coefficient of a regression on the win indicator; heteroskedasticity-robust errors.

   Por que este é o caminho de referência: Table I, column (3): the difference in the probability of a Democratic win at t+1 between districts the Democrat won and lost at t, among elections with the Democratic share at t strictly between 48 and 52 percent (915 observations), as the coefficient of a regression on the win indicator; heteroskedasticity-robust errors.
   Argumentos principais

   dataset:

   "lmb"

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   subset:

   "lagdemvoteshare > 0.48 and lagdemvoteshare < 0.52"

   x:

   ["lagdemocrat"]

   y:

   "democrat"

   Resultado calculado

   • coef.lagdemocrat = 0,484329 0.48432871476349737
     Coefficient on lagdemocrat in the regression of democrat
   Diagnósticos: n_obs = 915915 (observations used) · k = 22 (parameters estimated, fixed-effect dummies included) · r2 = 0,2347720.2347717230713633 · df_resid = 913913 · se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
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: 0,4843
   Figuras citadas: call_1d90f670:coef.lagdemocrat
   • ✓
     #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.ols

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

     outcome: a Democratic win at t+1
   • ✓
     #4argumento igualx

     the regressor: a Democratic win at t
   • ✓
     #5argumento igualcontrols

     column (3) is a difference in means: no other regressors
   • ✓
     #6argumento igualfe

     no fixed effects
   • ✓
     #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

   Lee, Moretti and Butler (2004), 'Do Voters Affect or Elect Policies? Evidence from the U.S. House', Table I, column (3): in the paper's close-elections sample, the House elections at time t in which the Democratic candidate's share of the two-party vote was strictly between 48 and 52 percent, estimate the difference in the probability that the Democratic candidate wins the seat's next election (t+1) between districts where the Democratic candidate's share at t was strictly between 50 and 52 percent and districts where it was strictly between 48 and 50 percent. Estimate it as the coefficient of a regression of an indicator of a Democratic win at t+1 on an indicator of a Democratic win at t, with no other regressors. Report that difference.

   lmb (agent)

   U.S. House elections and roll-call voting, the extract of the data of Lee, Moretti and Butler (2004), 'Do Voters Affect or Elect Policies? Evidence from the U.S. House', distributed with the causaldata R package: one row per district and congressional session (13,588 rows). Each row pairs a district's election with the one before it for the same seat. Columns: rownames, the row number; state, ICPSR state code; district, district code; id, an election identifier; score, the adjusted ADA voting score of the district's representative in the session that follows the row's election (0 to 100, higher means a more liberal roll-call record); year, year of the election; demvoteshare, the Democratic candidate's share of the two-party vote in the row's election; democrat, 1 if the Democratic candidate won the row's election; lagdemvoteshare, the Democratic share of the two-party vote in the previous election for the same seat; lagdemocrat, 1 if the Democratic candidate won that previous election. In the paper's notation the previous election is time t and the row's election, and the session after it, are time t+1.

   file 58cf9d8ef80b2871a96c00844fae975051a5aac443dff3d317fe92ffd6a516b7 · frame —
2. 1Turno do modelo
   call_4c1d3383referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   Table I, column (3): the difference in the probability of a Democratic win at t+1 between districts the Democrat won and lost at t, among elections with the Democratic share at t strictly between 48 and 52 percent (915 observations), as the coefficient of a regression on the win indicator; heteroskedasticity-robust errors.

   Chamadas pedidas

   • infer.ols

     {
       "controls": [],
       "dataset": "lmb",
       "fe": [],
       "se": "HC1",
       "subset": "lagdemvoteshare > 0.48 and lagdemvoteshare < 0.52",
       "x": [
         "lagdemocrat"
       ],
       "y": "democrat"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_1d90f670decidido pelo modeloreexecutável

   Motivo: Table I, column (3): the difference in the probability of a Democratic win at t+1 between districts the Democrat won and lost at t, among elections with the Democratic share at t strictly between 48 and 52 percent (915 observations), as the coefficient of a regression on the win indicator; heteroskedasticity-robust errors.

   Argumentos

   {
     "cluster": null,
     "controls": [],
     "dataset": "lmb",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": "lagdemvoteshare > 0.48 and lagdemvoteshare < 0.52",
     "weights": null,
     "x": [
       "lagdemocrat"
     ],
     "y": "democrat"
   }

   Figuras call_1d90f670

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.lagdemocrat Coefficient on lagdemocrat in the regression of democrat	0,484329	0.48432871476349737	0,02893220.02893215206729027	0,427547 – 0,54111

   Diagnósticos

   • n_obs = 915915 (observations used)
   • k = 22 (parameters estimated, fixed-effect dummies included)
   • r2 = 0,2347720.2347717230713633
   • df_resid = 913913
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
   • n_clusters = — (errors not clustered)

   Derivação matemática e pressupostos

   coef.lagdemocrat

   1. Coeficiente de lagdemocrat na regressão por mínimos quadrados ordinários de democrat.→ 0,484329 (0.48432871476349737)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,0289322 (0.02893215206729027)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 913 graus de liberdade vezes o erro-padrão.IC = β ± c · ep

   Pressupostos do estimador

   • the conditional mean of the outcome is linear in the regressors
   • errors are independent across observations; their variance may differ

   Tabelas (1)

   {
     "coefficients": [
       {
         "ci_high": 0.2811938149509847,
         "ci_low": 0.2023226685654988,
         "coef": 0.24175824175824176,
         "p": 4.715920282942519e-31,
         "se": 0.02009388689657253,
         "t": 12.031432395465465,
         "term": "Intercept"
       },
       {
         "ci_high": 0.5411099640233312,
         "ci_low": 0.42754746550366346,
         "coef": 0.48432871476349737,
         "p": 4.618711084163874e-55,
         "se": 0.02893215206729027,
         "t": 16.740155161532673,
         "term": "lagdemocrat"
       }
     ]
   }

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   lmb 6f2fbc93d91c4156cab244ba7581cc8eae45210626f97f85ac8b2d366e4cbc20

   sha256 do resultado

   76ed0f6ef69f3b30cdf11d85c7bf8f52f40de0e8e80b3853a9eefed10bbaafcb

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_f482756ereferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Estimativa (infer.ols): coef.lagdemocrat = 0,4843.",
       "caveats": [],
       "figure_refs": [
         "call_1d90f670:coef.lagdemocrat"
       ]
     }

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

   Números encontrados

   0,4843

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.ols): coef.lagdemocrat = 0,4843.

   Figuras citadas: call_1d90f670:coef.lagdemocrat · Parada: answered

   Figura da resposta: call_1d90f670:coef.lagdemocrat = 0,4843290.48432871476349737 (se 0,02893220.02893215206729027)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta escolhidapassouobrigatóriaanswer figure call_1d90f670:coef.lagdemocrat (infer.ols)
9. 8Pontuação
   #2ferramenta chamadapassouobrigatóriaanswer call is infer.ols
10. 9Pontuação
    #3argumento igualpassouobrigatóriainfer.ols.y = 'democrat'
11. 10Pontuação
    #4argumento igualpassouobrigatóriainfer.ols.x = ['lagdemocrat']
12. 11Pontuação
    #5argumento igualpassouobrigatóriainfer.ols.controls = []
13. 12Pontuação
    #6argumento igualpassouobrigatóriainfer.ols.fe = []
14. 13Pontuação
    #7diagnósticopassouobrigatória915 vs 915
15. 14Pontuação
    #8figura da resposta próximapassouobrigatória0.4843287148 vs 0.4843287147634966
16. 15Pontuação
    #9figura da resposta próximapassouregistrada0.02893215207 vs 0.02893215206729024
17. 16Pontuação
    #10figura da resposta próximapassouregistrada0.4843287148 vs 0.48
18. 17Pontuação
    #11figura da resposta próximafalhouregistrada0.02893215207 vs 0.02

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)

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