Tarefa paper_repro.cheng_hoekstra_2013.tab5_pa_c1.agent
paper_reproagentT2publicépoca 1, tentativa 1Execução: 20261015T165744Z-suite-paper_repro-scripted-def9dfVeredito
Família do método (das chamadas de ferramentas): ols (regression)
Pergunta feita ao analista
Cheng and Hoekstra (NBER Working Paper 18134, 2012; published in the Journal of Human Resources, 2013), 'Does Strengthening Self-Defense Law Deter Crime or Escalate Violence? Evidence from Expansions to Castle Doctrine', Table 5, Panel A, column (1): in the state panel for 2000-2010, estimate by ordinary least squares, weighted by the state's average population over the period (the paper's population weight), the effect of castle doctrine laws on the log homicide rate, with state fixed effects and year fixed effects and no other controls. As in the paper, the law is measured as the fraction of each year in which it was in effect. Report the coefficient on the castle doctrine law.
Estimativa (infer.ols): coef.cdl = 0,08006.
Valores esperados e de onde vêm
cheng_hoekstra_2013.tab5_pa_c1.software
- Fonte
- statsmodels 0.15.0: the reference specification of cheng_hoekstra_2013 tab5_pa_c1 (infer.ols, figure 'coef.cdl') on an independent code path
- Tipo
- software
- Localização
- cheng_hoekstra_2013: NBER Working Paper 18134, Table 5, Panel A 'Log Homicide Rate (OLS - Weighted)', column (1) state and year fixed effects, row 'Castle Doctrine Law'
- Valor
- 0,0800635
0.08006351033824417(se 0,03589850.03589849411715782) - Conferido por
- execução de software · sm.WLS(l_homicide, add_constant(['cdl']), fe dummies ['sid', 'year'], weights=popwt; 61 independent columns).fit(cov_type='cluster', groups=sid) on castle (the file pinned at sha256 804633c161827b6c0824f86f239046386d1a8266a866f83bf5ddb2aa762a5f29); n_obs = 550. 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/
- #7 figura da resposta próxima: 0,0800635
0.08006351033824417· relativa 0.000001 · absoluta 3.589849411715782e-8 - #8 figura da resposta próxima: 0,0358985
0.03589849411715782· relativa 0.000001 · absoluta 3.589849411715782e-8
cheng_hoekstra_2013.tab5_pa_c1.paper
- Fonte
- Cheng, C. and Hoekstra, M. (2013). Does Strengthening Self-Defense Law Deter Crime or Escalate Violence? Evidence from Expansions to Castle Doctrine. Journal of Human Resources 48(3): 821-854. Cells read from the NBER Working Paper 18134 (June 2012).
- Tipo
- paper
- Localização
- NBER Working Paper 18134, Table 5 (the effect of castle doctrine on homicide), Panel A 'Log Homicide Rate (OLS - Weighted)', column (1), row 'Castle Doctrine Law'
- Página
- 38
- Trecho
Panel A: Castle Doctrine Law 0.0801 (0.0342)
- Valor como publicado
0.0801- Erro-padrão como publicado
0.0342- Valor
- 0,0801
0.0801(se 0,03420.0342) log points of the homicide rate per unit of the castle doctrine law variable - Conferido por
- fonte primária · Read on 2026-10-15 from the NBER Working Paper 18134 PDF (43 pages, created 2012-06-28) with pdftotext -layout and from the page image rendered at 80 dpi: Table 5 'The Effect of Castle Doctrine on Homicide' is on PDF page 40 (printed page 38). The published JHR version (48(3), 2013) could not be read without a subscription, so the cells are the authors' circulated working-paper version. The notes say 'Robust standard errors are clustered at the state level'; the unit is the state-year; 550 observations in every column. Panel A is weighted (by state population, 'OLS - Weighted'), Panel B unweighted. Column (1) has state and year fixed effects; column (2) adds region-by-year fixed effects.
- sha256 da cópia lida
1283a9c1e37ea2114e36b6c48ffc7c3fc0f26b2334f08c1a60ffbe82f3005682(identifica a cópia lida, não um arquivo canônico)- Desvio conhecido
- convenção de erro-padrãoRecorded before the software-reference run and before any dry run of this track, from the phase-B research probe (research-papers.md sections 2.4 and 3.9, 2026-10-14): infer.ols with state and year fixed effects as dummies, population weights and errors clustered by state gives the printed coefficients but SEs of 0.0359 and 0.0293 against the printed 0.0342 and 0.0279, a ratio of about sqrt((N-k)/(N-K)) (k: the regressors left after absorbing the state effects, as Stata xtreg, fe counts them; K: the full dummy count): the authors' clustered errors do not count the state effects, which are nested in the clusters, in K (Stata xtreg, fe), while the dummy-variable convention does.
- Endereço
https://www.nber.org/system/files/working_papers/w18134/w18134.pdf- Licença
- NBER working paper, copyright the authors; cited, not reproduced.
- #9 figura da resposta próxima: 0,0801
0.0801· published_cell d=4 · absoluta 0.000050001000000000005 - #10 figura da resposta próxima: 0,0342
0.0342· published_cell d=4 (the SE as printed) · absoluta 0.000050001000000000005
Verificações
| # | Verificação | Obrigatória | Resultado | Esperado | Observado | Tolerância | Fonte | Chamada |
|---|---|---|---|---|---|---|---|---|
| 0 | resposta com lastro every number backed | obrigatória | passou | — | [] | — | — | — |
| 1 | figura da resposta escolhida answer figure call_6e86f3fa:coef.cdl (infer.ols) | obrigatória | passou | — | "call_6e86f3fa:coef.cdl" | — | — | call_6e86f3fa |
| 2 | ferramenta chamada answer call is infer.ols | obrigatória | passou | — | "infer.ols" | — | — | call_6e86f3fa |
| 3 | argumento igual infer.ols.y = 'l_homicide' | obrigatória | passou | "l_homicide" | "l_homicide" | — | — | call_6e86f3fa |
| 4 | argumento igual infer.ols.x = ['cdl'] | obrigatória | passou | ["cdl"] | ["cdl"] | — | — | call_6e86f3fa |
| 5 | argumento igual infer.ols.weights = 'popwt' | obrigatória | passou | "popwt" | "popwt" | — | — | call_6e86f3fa |
| 6 | diagnóstico 550 vs 550 | obrigatória | passou | 550550 | 550550 | default rel 1e-6 (abs floor 1e-6) | — | call_6e86f3fa |
| 7 | figura da resposta próxima 0.08006351034 vs 0.08006351033824417 | obrigatória | passou | 0,08006350.08006351033824417 | 0,08006350.08006351033824458 | relativa 0.000001 · absoluta 3.589849411715782e-8 | cheng_hoekstra_2013.tab5_pa_c1.software | call_6e86f3fa |
| 8 | figura da resposta próxima 0.03589849412 vs 0.03589849411715782 | registrada | passou | 0,03589850.03589849411715782 | 0,03589850.0358984941171576 | relativa 0.000001 · absoluta 3.589849411715782e-8 | cheng_hoekstra_2013.tab5_pa_c1.software | call_6e86f3fa |
| 9 | figura da resposta próxima 0.08006351034 vs 0.0801 | registrada | passou | 0,08010.0801 | 0,08006350.08006351033824458 | published_cell d=4 · absoluta 0.000050001000000000005 | cheng_hoekstra_2013.tab5_pa_c1.paper | call_6e86f3fa |
| 10 | figura da resposta próxima 0.03589849412 vs 0.0342 | registrada | falhou | 0,03420.0342 | 0,03589850.0358984941171576 | published_cell d=4 (the SE as printed) · absoluta 0.000050001000000000005 | cheng_hoekstra_2013.tab5_pa_c1.paper | call_6e86f3fa |
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.cheng_hoekstra_2013.tab5_pa_c1.tool)
| Argumento | Referência | Agente | |
|---|---|---|---|
| cluster | "sid" | "sid" | igual |
| controls | [] | [] | igual |
| dataset | "castle" | "castle" | igual |
| fe | ["sid","year"] | ["sid","year"] | 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 | "popwt" | "popwt" | igual |
| x | ["cdl"] | ["cdl"] | igual |
| y | "l_homicide" | "l_homicide" | igual |
Trajetória
Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.
- 0Início
Cheng and Hoekstra (NBER Working Paper 18134, 2012; published in the Journal of Human Resources, 2013), 'Does Strengthening Self-Defense Law Deter Crime or Escalate Violence? Evidence from Expansions to Castle Doctrine', Table 5, Panel A, column (1): in the state panel for 2000-2010, estimate by ordinary least squares, weighted by the state's average population over the period (the paper's population weight), the effect of castle doctrine laws on the log homicide rate, with state fixed effects and year fixed effects and no other controls. As in the paper, the law is measured as the fraction of each year in which it was in effect. Report the coefficient on the castle doctrine law.
castle(agent)The state-year panel of Cheng and Hoekstra (2013), 'Does Strengthening Self-Defense Law Deter Crime or Escalate Violence? Evidence from Expansions to Castle Doctrine', as distributed with Cunningham's Causal Inference: The Mixtape: one row per U.S. state and year, 2000-2010 (550 rows: 50 states, 11 years). Columns used most: state, the state's name; sid, a numeric state id; year; cdl, the castle doctrine law variable as the paper codes it: the fraction of the year in which the state's castle doctrine (stand-your-ground) law was in effect, 0 before adoption and 1 in full years after it; post, 1 in every year from the adoption year on (any part of the year); effyear and treatment_date, the year the law took effect (missing for states that never adopted one); homicide, the homicide rate per 100,000 residents (FBI Uniform Crime Reports), and l_homicide its natural log; murder and l_murder, the murder rate and its log; population; popwt, the state's average population over 2000-2010 (constant within a state), the weight the paper's weighted regressions use; northeast, midwest, south, west, census region indicators; r20001 to r20104, region-by-year indicators named r20<two-digit year><region 1-4> (for example r20063 is region 3 in 2006); trend_1 to trend_51, state-specific linear trends; lead1 to lead9 and lag0 to lag5, event-time indicators relative to adoption; other crime rates (robbery, assault, burglary, larceny, motor) and their logs (l_robbery and so on); and the time-varying controls of the paper (police, unemployrt, income, poverty, prisoner, lagprisoner, exp_subsidy, exp_pubwelfare, and the shares of black and white men aged 15-24 and 25-44).
file804633c161827b6c0824f86f239046386d1a8266a866f83bf5ddb2aa762a5f29· frame — - 1Turno do modelo
call_ce8ec9cereferenceFim:tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cacheTexto visívelTable 5, Panel A, column (1): log homicide rate on the castle doctrine law variable (the fraction of the year in effect) with state and year fixed effects, weighted by population, errors clustered by state.
Chamadas pedidasinfer.ols{ "cluster": "sid", "controls": [], "dataset": "castle", "fe": [ "sid", "year" ], "weights": "popwt", "x": [ "cdl" ], "y": "l_homicide" }
- 2Ferramenta
infer.olsv0.1.0call_6e86f3fadecidido pelo modeloreexecutávelMotivo: Table 5, Panel A, column (1): log homicide rate on the castle doctrine law variable (the fraction of the year in effect) with state and year fixed effects, weighted by population, errors clustered by state.
Argumentos
{ "cluster": "sid", "controls": [], "dataset": "castle", "fe": [ "sid", "year" ], "intercept": true, "level": 0.95, "se": "HC1", "subset": null, "weights": "popwt", "x": [ "cdl" ], "y": "l_homicide" }Figuras call_6e86f3fa Chave Valor Valor bruto Erro-padrão IC coef.cdl Coefficient on cdl in the regression of l_homicide0,0800635 0.080063510338244580,0358985 0.03589849411715760,00792279 – 0,152204 Diagnósticosn_obs= 550550k= 6161r2= 0,9341670.9341667643844439df_resid= 489489se_type="cluster"n_clusters= 5050fixed_effects="sid (50 levels), year (11 levels)"
Tabelas (1)
{ "coefficients": [ { "ci_high": 2.0246397884543303, "ci_low": 1.9368211431033613, "coef": 1.9807304657788458, "p": 3.0984228479904365e-56, "se": 0.021850051624944748, "t": 90.65106571728086, "term": "Intercept" }, { "ci_high": 0.15220423516631415, "ci_low": 0.007922785510174987, "coef": 0.08006351033824458, "p": 0.030341540853612167, "se": 0.0358984941171576, "t": 2.2302748989122194, "term": "cdl" } ] }- Digests dos dados
castle6a9af2b4a14b985d82e0f3aea93c535c24cfc2497c4d9f07242bac5f84ae11c1- sha256 do resultado
ded38c2380f9b375e6b2aed585e79881ac0dca015e577e8c24addfcbfbe5a607
- 3Turno do modelo
call_44a79161referenceFim:tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cacheChamadas pedidassubmit_answer(meta-ferramenta){ "answer": "Estimativa (infer.ols): coef.cdl = 0,08006.", "caveats": [], "figure_refs": [ "call_6e86f3fa:coef.cdl" ] }
- 4Guardaresposta aceita
- Números encontrados
- 0,08006
- Sem lastro
- —
- Não citados
- —
1 nova(s) tentativa(s) restante(s) - 5Resposta
Estimativa (infer.ols): coef.cdl = 0,08006.
Figuras citadas:call_6e86f3fa:coef.cdl· Parada:answeredFigura da resposta:call_6e86f3fa:coef.cdl= 0,08006350.08006351033824458(se 0,03589850.0358984941171576) - 6Pontuação#0resposta com lastropassouobrigatória
every number backed - 7Pontuação#1figura da resposta escolhidapassouobrigatória
answer figure call_6e86f3fa:coef.cdl (infer.ols) - 8Pontuação#2ferramenta chamadapassouobrigatória
answer call is infer.ols - 9Pontuação#3argumento igualpassouobrigatória
infer.ols.y = 'l_homicide' - 10Pontuação#4argumento igualpassouobrigatória
infer.ols.x = ['cdl'] - 11Pontuação#5argumento igualpassouobrigatória
infer.ols.weights = 'popwt' - 12Pontuação#6diagnósticopassouobrigatória
550 vs 550 - 13Pontuação#7figura da resposta próximapassouobrigatória
0.08006351034 vs 0.08006351033824417 - 14Pontuação#8figura da resposta próximapassouregistrada
0.03589849412 vs 0.03589849411715782 - 15Pontuação#9figura da resposta próximapassouregistrada
0.08006351034 vs 0.0801 - 16Pontuação#10figura da resposta próximafalhouregistrada
0.03589849412 vs 0.0342
Épocas × verificações
| # | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Época 1 | passou | passou | passou | passou | passou | passou | passou | 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
2b89ca2e6579b900cf892d74d0ca78642fe9cf56279d4d9dbb7bf0dd056eb743O 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.
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