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Tarefa paper_repro.stock_watson_fatalities.tab10_1_c2.design

paper_reproagentT3designépoca 1, tentativa 1Execução: 20261015T165744Z-suite-paper_repro-scripted-def9df

Veredito

passouPassou em todas as verificações obrigatórias.

Família do método (das chamadas de ferramentas): Regressão linear por mínimos quadrados ordinários (MQO) · infer.ols (ols · regressão linear)

Calculado pelo agente
coef.beertax =-0,655874-0.6558737221504309
Coefficient on beertax in the regression of frate
via infer.ols (call_d05e466b)
Valor esperado (gabarito)
coef.beertax =-0,655874-0.6558737221504346
statsmodels 0.15.0: the reference specification of stock_watson_fatalities tab10_1_c2 (infer.ols, figure 'coef.beertax') on an independent code path
Como foi validado
7 de 7 verificações obrigatórias aprovadas
relativa 0.000001 · absoluta 3.1484759309473804e-7

Pergunta feita ao analista

Do higher beer taxes reduce traffic deaths? Using the panel of 48 U.S. states over 1982-1988, estimate the effect of the real beer tax on the traffic fatality rate (deaths per 10,000 residents), controlling for the states' unobserved characteristics that do not change over time.

Resposta

Estimativa (infer.ols): coef.beertax = -0,6559.

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
    fatalities— Stock and Watson's traffic fatalities panel (R package AER, Fatalities; Ruhm 1996): one row per U.S. state (the 48 contiguous states) and year, 1982-1988 (336 rows). Columns: rownames, a row number; state, two-letter state code; year; spirits, spirits consumption; unemp, unemployment rate; income, per capita personal income (1987 dollars); emppop, employment to population ratio; beertax, tax on a case of beer (1988 dollars); baptist and mormon, percent of residents Southern Baptist and Mormon; drinkage, minimum legal drinking age; dry, percent of residents in dry counties; youngdrivers, percent of drivers aged 15-24; miles, average miles per driver; breath, jail, service, 'yes' for a preliminary breath test law, mandatory jail sentence and mandatory community service; fatal, number of vehicle fatalities; nfatal and sfatal, night-time and single-vehicle fatalities; fatal1517, fatal1820, fatal2124 and their night-time counterparts, fatalities by age group; afatal, alcohol-involved fatalities; pop, population; pop1517, pop1820, pop2124, population by age group; milestot, total vehicle miles (millions); unempus and emppopus, national unemployment rate and employment ratio; gsp, growth of gross state product. Columns built from the book's definition: fatal_per_pop, fatal divided by pop; frate, the traffic fatality rate, deaths per 10,000 residents (fatal_per_pop times 10,000).
    Transformação "derive" aplicada à base "fatalities" (336 → 336 linhas). {"args":{"left":"fatal","right":"pop"},"name":"fatal_per_pop","op":"ratio"}
    Transformação "derive" aplicada à base "fatalities" (336 → 336 linhas). {"args":{"column":"fatal_per_pop","factor":10000},"name":"frate","op":"scale"}
  2. 2Chamada que gerou a resposta finalinfer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na respostacoincide com a referência
    Ver chamada call_d05e466b nos detalhes

    Stock and Watson Table 10.1, column (2): the traffic fatality rate on the real beer tax with state fixed effects, 1982-1988, errors clustered by state.

    Por que este é o caminho de referência: Stock and Watson Table 10.1, column (2): the traffic fatality rate on the real beer tax with state fixed effects, 1982-1988, errors clustered by state.
    Argumentos principais
    cluster:
    "state"
    dataset:
    "fatalities"
    fe:
    ["state"]
    intercept:
    true
    level:
    0,950.95
    se:
    "HC1"
    x:
    ["beertax"]
    y:
    "frate"
    Resultado calculado
    • coef.beertax = -0,655874 -0.6558737221504309
      Coefficient on beertax in the regression of frate
    Diagnósticos: n_obs = 336336 (observations used) · k = 4949 (parameters estimated, fixed-effect dummies included) · r2 = 0,9050150.905014685169608 · df_resid = 287287 · se_type = "cluster" (cluster-robust standard errors (CR1) by 'state') · n_clusters = 4848 (48 clusters of 'state') · fixed_effects = "state (48 levels)" (absorbed as dummies; their coefficients are not tabulated)
  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: -0,6559
    Figuras citadas: call_d05e466b:coef.beertax
    • #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
    • #2uma entre
      acceptable tools: the reference tool and its alternatives
    • #3argumento igualy
      T3: the outcome the design question implies
    • #4argumento igualx[0]
      T3: the treatment the design question implies
    • #5argumento igualy
      T3: the outcome the design question implies
    • #6argumento igualx[0]
      T3: the treatment the design question implies
    • #7argumento igualy
      outcome: traffic deaths per 10,000 residents (T3: recorded; a design question cannot state it)
    • #8argumento igualfe
      state fixed effects, no time effects (T3: recorded; a design question cannot state it)
    • #9argumento igualcluster
      errors clustered by state (T3: recorded; a design question cannot state it)
    • #10diagnósticon_obs
      (T3: recorded; a design question cannot state it)
    • #11figura da resposta próxima
      software reference (T3: recorded; T3 pontua o desenho; a estimativa é registrada)
    • #12figura da resposta próxima
      software reference, SE: recorded (T3)
    • #13figura da resposta próxima
      published cell: recorded, never required on T2/T3
    • #14figura da resposta próxima
      published cell, SE: recorded, never required on T2/T3

Aprofundamento técnico para especialistas

Acesso direto às fontes primárias, tabela formal de verificações, comparação de argumentos, trajetória completa com prompts brutos e somas SHA-256.

Valores esperados e de onde vêm

stock_watson_fatalities.tab10_1_c2.software

Fonte
statsmodels 0.15.0: the reference specification of stock_watson_fatalities tab10_1_c2 (infer.ols, figure 'coef.beertax') on an independent code path
Tipo
software
Localização
stock_watson_fatalities: Table 10.1 (Regression analysis of the effect of drunk driving laws on traffic deaths), column (2) (state effects, no time effects, clustered standard errors), row 'Beer tax'
Valor
-0,655874-0.6558737221504346 (se 0,3148480.31484759309473803)
Conferido por
execução de software · sm.OLS(frate, add_constant(['beertax']), fe dummies ['state']; 49 independent columns).fit(cov_type='cluster', groups=state) on fatalities (the file pinned at sha256 ae265c1b14b4a023179b3af5db33b6a659ccf804987f26e8349826d4083ee149, after its 2 declared transforms); n_obs = 336. The tool infer.ols (magi_infer, numpy) does not call statsmodels. Written by scripts/paper_repro_reference.py.
Implementação
independente da ferramenta
Endereço
https://pypi.org/project/statsmodels/0.15.0/
  • #11 figura da resposta próxima: -0,655874-0.6558737221504346 · relativa 0.000001 · absoluta 3.1484759309473804e-7
  • #12 figura da resposta próxima: 0,3148480.31484759309473803 · relativa 0.000001 · absoluta 3.1484759309473804e-7

stock_watson_fatalities.tab10_1_c2.paper

Fonte
Stock, J. H. and Watson, M. W. (2015). Introduction to Econometrics, 3rd edition (update) or later; the edition of the reproduced table is not confirmed. Boston: Pearson.
Tipo
textbook
Localização
Table 10.1 (Regression analysis of the effect of drunk driving laws on traffic deaths), column (2) (state effects, no time effects, clustered standard errors), row 'Beer tax'
Página
slide 66 of the NTU reproduction (book page not read)
Trecho
Table 10.1, Beer tax, column (2): −0.66 (0.29)
Valor como publicado
−0.66
Erro-padrão como publicado
0.29
Valor
-0,66-0.66 (se 0,290.29) deaths per 10,000 residents per dollar of real beer tax per case
Conferido por
fonte secundária · The book itself was not read (not public). Secondary reproduction read on 2026-10-15: Ming-Ching Luoh's course slides 'Regression with Panel Data' (National Taiwan University, 2022; panel.pdf, sha256 72404054...), slide 66, an image of the book's Table 10.1 'Regression Analysis of the Effect of Drunk Driving Laws on Traffic Deaths' (dependent variable: traffic fatality rate, deaths per 10,000), read from the page rendered at 220 dpi. The table's layout (95% intervals in brackets, a 'Clustered standard errors?' row) suggests the 3rd edition; neither slide names an edition. The same deck's slide 35 prints an equation with a non-clustered standard error, -.66 (.20), edition not stated; the cells here are the table's clustered ones.
sha256 da cópia lida
724040543bc392d0119d830d39ae5aabbbb08e847c66f33e9bfe56c64734b344 (identifica a cópia lida, não um arquivo canônico)
Desvio conhecido
convenção de erro-padrão
Recorded before this track's software-reference run and before any dry run on this target; from the stated conventions. Table 10.1 reports errors clustered by state with the convention of Stata's xtreg/areg-style panel output, where the absorbed state effects (nested in the clusters) are not counted in K; infer.ols, by its card, puts fixed effects in as dummies and applies the CR1 correction G/(G-1)·(N-1)/(N-K) with the dummies counted in K, so its clustered SE is larger by about sqrt((N-1)/(N-K)). The research probe of 2026-10-14 (research-papers.md sections 2.4 and 3.21) found 0.3148 against the printed 0.29 for column (2). The printed SE (and, for safety, the printed cell) is therefore a se_convention deviation: recorded, counted in replication fidelity, non-required on T1.
Endereço
https://homepage.ntu.edu.tw/~luohm/metrics2022s/panel.pdf
Licença
Copyright Pearson Education; cited, not reproduced.
  • #13 figura da resposta próxima: -0,66-0.66 · published_cell d=2 · absoluta 0.0050000010000000004
  • #14 figura da resposta próxima: 0,290.29 · published_cell d=2 (the SE as printed) · absoluta 0.0050000010000000004
Especificação técnica do gabarito

Stock, J. H. and Watson, M. W. (2015). Introduction to Econometrics, 3rd edition (update) or later; the edition of the reproduced table is not confirmed. Boston: Pearson. Table 10.1 (drunk driving laws and traffic deaths). Table 10.1 (Regression analysis of the effect of drunk driving laws on traffic deaths), column (2) (state effects, no time effects, clustered standard errors), row 'Beer tax' (source stock_watson_fatalities.tab10_1_c2.paper); software reference stock_watson_fatalities.tab10_1_c2.software: statsmodels 0.15.0: the reference specification of stock_watson_fatalities tab10_1_c2 (infer.ols, figure 'coef.beertax') on an independent code path

Verificações

Cada verificação, o que esperava, o que observou e a chamada que leu
#VerificaçãoObrigatóriaResultadoEsperadoObservadoTolerânciaFonteChamada
0
resposta com lastro
Garante que todo número citado no texto veio de uma ferramenta ou do enunciado (sem cálculo mental do modelo).
every number backed
obrigatóriapassou0 sem lastro0 sem lastro[]———
1
figura da resposta escolhida
an answer figure: the effect of an estimator call the answer cites
answer figure call_d05e466b:coef.beertax (infer.ols)
obrigatóriapassou—"call_d05e466b:coef.beertax"——call_d05e466b
2
uma entre
acceptable tools: the reference tool and its alternatives
tool_called(infer.ols): ok | tool_called(infer.fe): answer call is infer.ols
obrigatóriapassou[{"abs_tol":null,"bind":"answer_call","column":null,"failure_class":null,"field":"value","interval":false,"key":null,"kind":"tool_called","min_lre":null,"note":"","open_interval":false,"rel_tol":null,"required":true,"source_id":null,"table":null,"tool":"infer.ols","value":null,"weight":1,"where":null},{"abs_tol":null,"bind":"answer_call","column":null,"failure_class":null,"field":"value","interval":false,"key":null,"kind":"tool_called","min_lre":null,"note":"","open_interval":false,"rel_tol":null,"required":true,"source_id":null,"table":null,"tool":"infer.fe","value":null,"weight":1,"where":null}]"infer.ols"——call_d05e466b
3
argumento igualy
T3: the outcome the design question implies
infer.ols.y = 'frate'
obrigatóriapassou"frate""frate"——call_d05e466b
4
argumento igualx[0]
T3: the treatment the design question implies
infer.ols.x[0] = 'beertax'
obrigatóriapassou"beertax""beertax"——call_d05e466b
5
argumento igualy
T3: the outcome the design question implies
not applicable: the answer call is infer.ols, not infer.fe
obrigatóriapassou"frate"———call_d05e466b
6
argumento igualx[0]
T3: the treatment the design question implies
not applicable: the answer call is infer.ols, not infer.fe
obrigatóriapassou"beertax"———call_d05e466b
7
argumento igualy
outcome: traffic deaths per 10,000 residents (T3: recorded; a design question cannot state it)
infer.ols.y = 'frate'
registradapassou"frate""frate"——call_d05e466b
8
argumento igualfe
state fixed effects, no time effects (T3: recorded; a design question cannot state it)
infer.ols.fe = ['state']
registradapassou["state"]["state"]——call_d05e466b
9
argumento igualcluster
errors clustered by state (T3: recorded; a design question cannot state it)
infer.ols.cluster = 'state'
registradapassou"state""state"——call_d05e466b
10
diagnósticon_obs
(T3: recorded; a design question cannot state it)
336 vs 336
registradapassou336336336336default rel 1e-6 (abs floor 1e-6)—call_d05e466b
11
figura da resposta próxima
software reference (T3: recorded; T3 pontua o desenho; a estimativa é registrada)
-0.6558737222 vs -0.6558737221504346
registradapassou-0,655874-0.6558737221504346-0,655874-0.6558737221504309relativa 0.000001 · absoluta 3.1484759309473804e-7stock_watson_fatalities.tab10_1_c2.softwarecall_d05e466b
12
figura da resposta próxima
software reference, SE: recorded (T3)
0.3148475931 vs 0.31484759309473803
registradapassou0,3148480.314847593094738030,3148480.3148475930947355relativa 0.000001 · absoluta 3.1484759309473804e-7stock_watson_fatalities.tab10_1_c2.softwarecall_d05e466b
13
figura da resposta próxima
published cell: recorded, never required on T2/T3
-0.6558737222 vs -0.66
registradapassou-0,66-0.66-0,655874-0.6558737221504309published_cell d=2 · absoluta 0.0050000010000000004stock_watson_fatalities.tab10_1_c2.papercall_d05e466b
14
figura da resposta próxima
published cell, SE: recorded, never required on T2/T3
0.3148475931 vs 0.29
registradafalhou0,290.290,3148480.3148475930947355published_cell d=2 (the SE as printed) · absoluta 0.0050000010000000004stock_watson_fatalities.tab10_1_c2.papercall_d05e466b

Caminho de referência e caminho do agente

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

a tarefa T1 do mesmo alvo (paper_repro.stock_watson_fatalities.tab10_1_c2.tool)

Referência: infer.olsChamada do agente: infer.ols (call_d05e466b)igual
Argumentos da referência e do agente
ArgumentoReferênciaAgente
cluster"state""state"igual
controls[][]igual
dataset"fatalities""fatalities"igual
fe["state"]["state"]igual
intercept—truenão fixado pela referência
level—0,950.95não fixado pela referência
se—"HC1"não fixado pela referência
subset——não fixado pela referência
weights——não fixado pela referência
x["beertax"]["beertax"]igual
y"frate""frate"igual

Trajetória

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

  1. 0Início

    Do higher beer taxes reduce traffic deaths? Using the panel of 48 U.S. states over 1982-1988, estimate the effect of the real beer tax on the traffic fatality rate (deaths per 10,000 residents), controlling for the states' unobserved characteristics that do not change over time.

    fatalities (agent)

    Stock and Watson's traffic fatalities panel (R package AER, Fatalities; Ruhm 1996): one row per U.S. state (the 48 contiguous states) and year, 1982-1988 (336 rows). Columns: rownames, a row number; state, two-letter state code; year; spirits, spirits consumption; unemp, unemployment rate; income, per capita personal income (1987 dollars); emppop, employment to population ratio; beertax, tax on a case of beer (1988 dollars); baptist and mormon, percent of residents Southern Baptist and Mormon; drinkage, minimum legal drinking age; dry, percent of residents in dry counties; youngdrivers, percent of drivers aged 15-24; miles, average miles per driver; breath, jail, service, 'yes' for a preliminary breath test law, mandatory jail sentence and mandatory community service; fatal, number of vehicle fatalities; nfatal and sfatal, night-time and single-vehicle fatalities; fatal1517, fatal1820, fatal2124 and their night-time counterparts, fatalities by age group; afatal, alcohol-involved fatalities; pop, population; pop1517, pop1820, pop2124, population by age group; milestot, total vehicle miles (millions); unempus and emppopus, national unemployment rate and employment ratio; gsp, growth of gross state product. Columns built from the book's definition: fatal_per_pop, fatal divided by pop; frate, the traffic fatality rate, deaths per 10,000 residents (fatal_per_pop times 10,000).

    file ae265c1b14b4a023179b3af5db33b6a659ccf804987f26e8349826d4083ee149 · frame —
    1. derive {"args":{"left":"fatal","right":"pop"},"name":"fatal_per_pop","op":"ratio"}
    2. derive {"args":{"column":"fatal_per_pop","factor":10000},"name":"frate","op":"scale"}
  2. 1Transformação
    fatalities · derive {"args":{"left":"fatal","right":"pop"},"name":"fatal_per_pop","op":"ratio"}
    336 → 336 linhas
    Frame antes → depois: 63bcb8c60f68 → 4658e9b711d9
  3. 2Transformação
    fatalities · derive {"args":{"column":"fatal_per_pop","factor":10000},"name":"frate","op":"scale"}
    336 → 336 linhas
    Frame antes → depois: 4658e9b711d9 → fbec03347d8a
  4. 3Turno do modelo
    call_26e293efreferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache
    Texto visível

    Stock and Watson Table 10.1, column (2): the traffic fatality rate on the real beer tax with state fixed effects, 1982-1988, errors clustered by state.

    Chamadas pedidas
    • infer.ols
      {
        "cluster": "state",
        "controls": [],
        "dataset": "fatalities",
        "fe": [
          "state"
        ],
        "x": [
          "beertax"
        ],
        "y": "frate"
      }
  5. 4Ferramenta
    infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_d05e466bdecidido pelo modeloreexecutável

    Motivo: Stock and Watson Table 10.1, column (2): the traffic fatality rate on the real beer tax with state fixed effects, 1982-1988, errors clustered by state.

    Argumentos
    {
      "cluster": "state",
      "controls": [],
      "dataset": "fatalities",
      "fe": [
        "state"
      ],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "beertax"
      ],
      "y": "frate"
    }
    Figuras call_d05e466b
    ChaveValorValor brutoErro-padrãoIC
    coef.beertax
    Coefficient on beertax in the regression of frate
    -0,655874-0.65587372215043090,3148480.3148475930947355-1,28927 – -0,0224821
    Diagnósticos
    • n_obs = 336336 (observations used)
    • k = 4949 (parameters estimated, fixed-effect dummies included)
    • r2 = 0,9050150.905014685169608
    • df_resid = 287287
    • se_type = "cluster" (cluster-robust standard errors (CR1) by 'state')
    • n_clusters = 4848 (48 clusters of 'state')
    • fixed_effects = "state (48 levels)" (absorbed as dummies; their coefficients are not tabulated)
    Derivação matemática e pressupostos
    coef.beertax
    1. Coeficiente de beertax na regressão por mínimos quadrados ordinários de frate, efeitos fixos de state.→ -0,655874 (-0.6558737221504309)
    2. Erro-padrão robusto por conglomerado (CR1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,314848 (0.3148475930947355)
    3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 47 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 clusters of 'state' and may be correlated within them
    Tabelas (1)
    {
      "coefficients": [
        {
          "ci_high": 4.506126847112231,
          "ci_low": 2.449133366231346,
          "coef": 3.4776301066717887,
          "p": 1.6311977618807354e-8,
          "se": 0.5112472177306856,
          "t": 6.802247496051376,
          "term": "Intercept"
        },
        {
          "ci_high": -0.022482063471447655,
          "ci_low": -1.289265380829414,
          "coef": -0.6558737221504309,
          "p": 0.04270329712149496,
          "se": 0.3148475930947355,
          "t": -2.083146692352458,
          "term": "beertax"
        }
      ]
    }
    Hashes SHA-256 e metadados de reprodução
    Digests dos dados
    fatalities fbec03347d8ac47966e488772a52693f33319d956e744370f990665ce749914e
    sha256 do resultado
    df4219c4870b05c864f5bec77339cc7ec2dfbe88d3f2be426e5c21dd4ed9ce25
  6. 5Turno do modelo
    call_fa42a134referenceFim: 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.beertax = -0,6559.",
        "caveats": [],
        "figure_refs": [
          "call_d05e466b:coef.beertax"
        ]
      }
  7. 6Guarda
    resposta aceita
    Números encontrados
    -0,6559
    Sem lastro
    —
    Não citados
    —
    1 nova(s) tentativa(s) restante(s)
  8. 7Resposta

    Estimativa (infer.ols): coef.beertax = -0,6559.

    Figuras citadas: call_d05e466b:coef.beertax · Parada: answered
    Figura da resposta: call_d05e466b:coef.beertax = -0,655874-0.6558737221504309 (se 0,3148480.3148475930947355)
  9. 8Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
  10. 9Pontuação
    #1figura da resposta escolhidapassouobrigatóriaanswer figure call_d05e466b:coef.beertax (infer.ols)
  11. 10Pontuação
    #2uma entrepassouobrigatóriatool_called(infer.ols): ok | tool_called(infer.fe): answer call is infer.ols
  12. 11Pontuação
    #3argumento igualpassouobrigatóriainfer.ols.y = 'frate'
  13. 12Pontuação
    #4argumento igualpassouobrigatóriainfer.ols.x[0] = 'beertax'
  14. 13Pontuação
    #5argumento igualpassouobrigatórianot applicable: the answer call is infer.ols, not infer.fe
  15. 14Pontuação
    #6argumento igualpassouobrigatórianot applicable: the answer call is infer.ols, not infer.fe
  16. 15Pontuação
    #7argumento igualpassouregistradainfer.ols.y = 'frate'
  17. 16Pontuação
    #8argumento igualpassouregistradainfer.ols.fe = ['state']
  18. 17Pontuação
    #9argumento igualpassouregistradainfer.ols.cluster = 'state'
  19. 18Pontuação
    #10diagnósticopassouregistrada336 vs 336
  20. 19Pontuação
    #11figura da resposta próximapassouregistrada-0.6558737222 vs -0.6558737221504346
  21. 20Pontuação
    #12figura da resposta próximapassouregistrada0.3148475931 vs 0.31484759309473803
  22. 21Pontuação
    #13figura da resposta próximapassouregistrada-0.6558737222 vs -0.66
  23. 22Pontuação
    #14figura da resposta próximafalhouregistrada0.3148475931 vs 0.29

Épocas × verificações

Resultado de cada verificação em cada época pontuada
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Época 1passoupassoupassoupassoupassoupassoupassoupassoupassoupassoupassoupassoupassoupassoufalhou

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Pacote da tarefa (.zip)
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