Tarefa paper_repro.stock_watson_fatalities.tab10_1_c2.tool
paper_reprotoolT1publicépoca 1, tentativa 1Execução: 20261015T165911Z-suite-paper_repro-baseline-8b4f85Veredito
coef.beertax =-0,655874-0.6558737221504309coef.beertax =-0,655874-0.6558737221504346Pergunta feita ao analista
Tarefa de ferramenta: nenhuma pergunta; a especificação de referência é chamada direto.
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.
- 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"} - 2Chamada que gerou a resposta finalVer chamada call_262ee951 nos detalhes
infer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na respostacoincide com a referênciaArgumentos principais- cluster:
"state"- dataset:
"fatalities"- fe:
["state"]- intercept:
true- level:
- 0,95
0.95 - se:
"HC1"- x:
["beertax"]- y:
"frate"
Resultado calculadocoef.beertax= -0,655874-0.6558737221504309Coefficient 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) - 3Conferência de lastro e resposta final
- #0figura próxima
coef.beertaxsoftware reference - #1figura próxima
coef.beertaxsoftware reference, SE - #2figura próxima
coef.beertaxpublished cell - #3figura próxima
coef.beertaxpublished cell, SE (known deviation: se_convention)
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/
- #0 figura próxima: -0,655874
-0.6558737221504346· relativa 0.000001 · absoluta 3.1484759309473804e-7 - #1 figura próxima: 0,314848
0.31484759309473803· 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-padrãoRecorded 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.
- #2 figura próxima: -0,66
-0.66· published_cell d=2 · absoluta 0.0050000010000000004 - #3 figura próxima: 0,29
0.29· 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
| # | Verificação | Obrigatória | Resultado | Esperado | Observado | Tolerância | Fonte | Chamada |
|---|---|---|---|---|---|---|---|---|
| 0 | figura próxima coef.beertaxsoftware reference -0.6558737222 vs -0.6558737221504346 | obrigatória | passou | -0,655874-0.6558737221504346 | -0,655874-0.6558737221504309 | relativa 0.000001 · absoluta 3.1484759309473804e-7 | stock_watson_fatalities.tab10_1_c2.software | — |
| 1 | figura próxima coef.beertaxsoftware reference, SE 0.3148475931 vs 0.31484759309473803 | obrigatória | passou | 0,3148480.31484759309473803 | 0,3148480.3148475930947355 | relativa 0.000001 · absoluta 3.1484759309473804e-7 | stock_watson_fatalities.tab10_1_c2.software | — |
| 2 | figura próxima coef.beertaxpublished cell -0.6558737222 vs -0.66 | obrigatória | passou | -0,66-0.66 | -0,655874-0.6558737221504309 | published_cell d=2 · absoluta 0.0050000010000000004 | stock_watson_fatalities.tab10_1_c2.paper | — |
| 3 | figura próxima coef.beertaxpublished cell, SE (known deviation: se_convention) 0.3148475931 vs 0.29 | registrada | falhou | 0,290.29 | 0,3148480.3148475930947355 | published_cell d=2 (the SE as printed) · absoluta 0.0050000010000000004 | stock_watson_fatalities.tab10_1_c2.paper | — |
Caminho de referência e caminho do agente
A chamada de referência ao lado da chamada que deu a resposta, argumento por argumento.
a própria especificação da tarefa
| Argumento | Referência | Agente | |
|---|---|---|---|
| cluster | "state" | "state" | igual |
| controls | [] | [] | igual |
| dataset | "fatalities" | "fatalities" | igual |
| fe | ["state"] | ["state"] | igual |
| intercept | — | true | não fixado pela referência |
| level | — | 0,950.95 | não 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.
- 0Início
Tarefa de ferramenta: nenhuma pergunta; a especificação de referência é chamada direto.
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).
fileae265c1b14b4a023179b3af5db33b6a659ccf804987f26e8349826d4083ee149· frame —derive{"args":{"left":"fatal","right":"pop"},"name":"fatal_per_pop","op":"ratio"}derive{"args":{"column":"fatal_per_pop","factor":10000},"name":"frate","op":"scale"}
- 1Transformação
fatalities·derive{"args":{"left":"fatal","right":"pop"},"name":"fatal_per_pop","op":"ratio"}336 → 336 linhasFrame antes → depois:63bcb8c60f68→4658e9b711d9 - 2Transformação
fatalities·derive{"args":{"column":"fatal_per_pop","factor":10000},"name":"frate","op":"scale"}336 → 336 linhasFrame antes → depois:4658e9b711d9→fbec03347d8a - 3Ferramenta
infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_262ee951decidido pelo códigoreexecutávelArgumentos
{ "cluster": "state", "controls": [], "dataset": "fatalities", "fe": [ "state" ], "intercept": true, "level": 0.95, "se": "HC1", "subset": null, "weights": null, "x": [ "beertax" ], "y": "frate" }Figuras call_262ee951 Chave Valor Valor bruto Erro-padrão IC coef.beertax Coefficient on beertax in the regression of frate-0,655874 -0.65587372215043090,314848 0.3148475930947355-1,28927 – -0,0224821 Diagnósticosn_obs= 336336(observations used)k= 4949(parameters estimated, fixed-effect dummies included)r2= 0,9050150.905014685169608df_resid= 287287se_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- Coeficiente de beertax na regressão por mínimos quadrados ordinários de frate, efeitos fixos de state.→ -0,655874 (-0.6558737221504309)
- Erro-padrão robusto por conglomerado (CR1): a raiz da variância estimada do coeficiente.
ep = √V_jj→ 0,314848 (0.3148475930947355) - 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
fatalitiesfbec03347d8ac47966e488772a52693f33319d956e744370f990665ce749914e- sha256 do resultado
b55d361a058c125734c1ef66cc7c8af6c256add171f4834b4341d2c8af30108d
- 4Pontuação#0figura próximapassouobrigatória
-0.6558737222 vs -0.6558737221504346 - 5Pontuação#1figura próximapassouobrigatória
0.3148475931 vs 0.31484759309473803 - 6Pontuação#2figura próximapassouobrigatória
-0.6558737222 vs -0.66 - 7Pontuação#3figura próximafalhouregistrada
0.3148475931 vs 0.29
Épocas × verificações
| # | 0 | 1 | 2 | 3 |
|---|---|---|---|---|
| Época 1 | passou | passou | passou | falhou |
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
9765e2a40c44cf2f147c17b419d3e4adf8250b8a1d5a1a62128d0dc75d538c7bO hash de um zip é só de transporte: o que ancora a integridade é o RUN_SHA256SUMS que vai dentro dele, cópia exata do SHA256SUMS da execução.
| Arquivo | Tamanho | sha256 |
|---|---|---|
| sources/stock_watson_fatalities.tab10_1_c2.paper.json | 2,8 KB | 3916f74ed2d41b56ebbd3b560d86c61cdf2c1250bcabea353b4337ac72db4b70 |
| sources/stock_watson_fatalities.tab10_1_c2.software.json | 1,4 KB | 02029cb3ca03fede940432b021201deb6d05816b437bb39cf1bc49c8505eb36e |
| specs/paper_repro.stock_watson_fatalities.tab10_1_c2.tool.json | 6,6 KB | 3a5e3b1a4f63415f31637de80ed0312aaf238c00e116f9fbdba8a07afae31101 |
| traces/paper_repro.stock_watson_fatalities.tab10_1_c2.tool.e1.a1.jsonl | 5,4 KB | fad979ecf04a797c0445c961d633a2408208d442da0e4b0242193c0e263c7068 |
| trajectories/paper_repro.stock_watson_fatalities.tab10_1_c2.tool.e1.a1.jsonl | 9,5 KB | 0f32145f0b8bd2cc72e78132c7f0d64c248a77ccd7da96c227df930e05b7f41e |