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Tarefa paper_repro.stock_watson_fatalities.tab10_1_c2.agent

paper_reproagentT2publicépoca 1, tentativa 1Execução: 20261015T165744Z-suite-paper_repro-scripted-def9df

Veredito

passouPassou em todas as verificações obrigatórias.

Família do método (das chamadas de ferramentas): ols (regression)

Pergunta feita ao analista

Stock and Watson, Introduction to Econometrics, Table 10.1, column (2): using the panel of 48 U.S. states over 1982-1988, regress the traffic fatality rate (deaths per 10,000 residents) on the real tax on a case of beer with fixed effects for the states and no time effects, with standard errors clustered by states. Report the coefficient on the beer tax.

Resposta

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

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/
  • #10 figura da resposta próxima: -0,655874-0.6558737221504346 · relativa 0.000001 · absoluta 3.1484759309473804e-7
  • #11 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.
  • #12 figura da resposta próxima: -0,66-0.66 · published_cell d=2 · absoluta 0.0050000010000000004
  • #13 figura da resposta próxima: 0,290.29 · published_cell d=2 (the SE as printed) · absoluta 0.0050000010000000004

Verificações

Cada verificação, o que esperava, o que observou e a chamada que leu
#VerificaçãoObrigatóriaResultadoEsperadoObservadoTolerânciaFonteChamada
0resposta com lastro
every number backed
obrigatóriapassou—[]———
1figura da resposta escolhida
answer figure call_bbe7c6af:coef.beertax (infer.ols)
obrigatóriapassou—"call_bbe7c6af:coef.beertax"——call_bbe7c6af
2uma entre
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_bbe7c6af
3argumento igual
infer.ols.y = 'frate'
obrigatóriapassou"frate""frate"——call_bbe7c6af
4argumento igual
infer.ols.fe = ['state']
obrigatóriapassou["state"]["state"]——call_bbe7c6af
5argumento igual
infer.ols.cluster = 'state'
obrigatóriapassou"state""state"——call_bbe7c6af
6argumento igual
not applicable: the answer call is infer.ols, not infer.fe
obrigatóriapassou"frate"———call_bbe7c6af
7argumento igual
not applicable: the answer call is infer.ols, not infer.fe
obrigatóriapassou"state"———call_bbe7c6af
8diagnóstico
336 vs 336
obrigatóriapassou336336336336default rel 1e-6 (abs floor 1e-6)—call_bbe7c6af
9diagnóstico
not applicable: the answer call is infer.ols, not infer.fe
obrigatóriapassou336336—default rel 1e-6 (abs floor 1e-6)—call_bbe7c6af
10figura da resposta próxima
-0.6558737222 vs -0.6558737221504346
obrigatóriapassou-0,655874-0.6558737221504346-0,655874-0.6558737221504309relativa 0.000001 · absoluta 3.1484759309473804e-7stock_watson_fatalities.tab10_1_c2.softwarecall_bbe7c6af
11figura da resposta próxima
0.3148475931 vs 0.31484759309473803
obrigatóriapassou0,3148480.314847593094738030,3148480.3148475930947355relativa 0.000001 · absoluta 3.1484759309473804e-7stock_watson_fatalities.tab10_1_c2.softwarecall_bbe7c6af
12figura da resposta próxima
-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_bbe7c6af
13figura da resposta próxima
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_bbe7c6af

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_bbe7c6af)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

    Stock and Watson, Introduction to Econometrics, Table 10.1, column (2): using the panel of 48 U.S. states over 1982-1988, regress the traffic fatality rate (deaths per 10,000 residents) on the real tax on a case of beer with fixed effects for the states and no time effects, with standard errors clustered by states. Report the coefficient on the beer tax.

    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_54c4272ereferenceFim: 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.olsv0.1.0call_bbe7c6afdecidido 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_bbe7c6af
    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
    • k = 4949
    • r2 = 0,9050150.905014685169608
    • df_resid = 287287
    • se_type = "cluster"
    • n_clusters = 4848
    • fixed_effects = "state (48 levels)"
    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"
        }
      ]
    }
    Digests dos dados
    fatalities fbec03347d8ac47966e488772a52693f33319d956e744370f990665ce749914e
    sha256 do resultado
    8e0fea7007ce107ea4f36257630d4d4bc6facee59e9c52e2f9ed8968a651384e
  6. 5Turno do modelo
    call_690aa9d4referenceFim: 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_bbe7c6af: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_bbe7c6af:coef.beertax · Parada: answered
    Figura da resposta: call_bbe7c6af: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_bbe7c6af: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.fe = ['state']
  14. 13Pontuação
    #5argumento igualpassouobrigatóriainfer.ols.cluster = 'state'
  15. 14Pontuação
    #6argumento igualpassouobrigatórianot applicable: the answer call is infer.ols, not infer.fe
  16. 15Pontuação
    #7argumento igualpassouobrigatórianot applicable: the answer call is infer.ols, not infer.fe
  17. 16Pontuação
    #8diagnósticopassouobrigatória336 vs 336
  18. 17Pontuação
    #9diagnósticopassouobrigatórianot applicable: the answer call is infer.ols, not infer.fe
  19. 18Pontuação
    #10figura da resposta próximapassouobrigatória-0.6558737222 vs -0.6558737221504346
  20. 19Pontuação
    #11figura da resposta próximapassouobrigatória0.3148475931 vs 0.31484759309473803
  21. 20Pontuação
    #12figura da resposta próximapassouregistrada-0.6558737222 vs -0.66
  22. 21Pontuação
    #13figura da resposta próximafalhouregistrada0.3148475931 vs 0.29

Épocas × verificações

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#012345678910111213
Época 1passoupassoupassoupassoupassoupassoupassoupassoupassoupassoupassoupassoupassoufalhou

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