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Tarefa paper_repro.cheng_hoekstra_2013.tab5_pb_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

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 B, column (2): in the state panel for 2000-2010, estimate by unweighted ordinary least squares the effect of castle doctrine laws on the log homicide rate, with state fixed effects, year fixed effects and region-by-year fixed effects (the four census regions) 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.

Resposta

Estimativa (infer.ols): coef.cdl = 0,08109.

Valores esperados e de onde vêm

cheng_hoekstra_2013.tab5_pb_c2.software

Fonte
statsmodels 0.15.0: the reference specification of cheng_hoekstra_2013 tab5_pb_c2 (infer.ols, figure 'coef.cdl') on an independent code path
Tipo
software
Localização
cheng_hoekstra_2013: NBER Working Paper 18134, Table 5, Panel B 'Log Homicide Rate (OLS - Unweighted)', column (2) adding region-by-year fixed effects, row 'Castle Doctrine Law'
Valor
0,08109020.08109024141547702 (se 0,08087020.08087017349667114)
Conferido por
execução de software · sm.OLS(l_homicide, add_constant(['cdl', 'r20001', 'r20002', 'r20003', 'r20004', 'r20011', 'r20012', 'r20013', 'r20014', 'r20021', 'r20022', 'r20023', 'r20024', 'r20031', 'r20032', 'r20033', 'r20034', 'r20041', 'r20042', 'r20043', 'r20044', 'r20051', 'r20052', 'r20053', 'r20054', 'r20061', 'r20062', 'r20063', 'r20064', 'r20071', 'r20072', 'r20073', 'r20074', 'r20081', 'r20082', 'r20083', 'r20084', 'r20091', 'r20092', 'r20093', 'r20094', 'r20101', 'r20102', 'r20103', 'r20104']), fe dummies ['sid', 'year']; 91 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: 0,08109020.08109024141547702 · relativa 0.000001 · absoluta 8.087017349667114e-8
  • #8 figura da resposta próxima: 0,08087020.08087017349667114 · relativa 0.000001 · absoluta 8.087017349667114e-8

cheng_hoekstra_2013.tab5_pb_c2.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 B 'Log Homicide Rate (OLS - Unweighted)', column (2), row 'Castle Doctrine Law'
Página
38
Trecho
Panel B: Castle Doctrine Law 0.0811 (0.0769)
Valor como publicado
0.0811
Erro-padrão como publicado
0.0769
Valor
0,08110.0811 (se 0,07690.0769) 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-padrão
Found in the software-reference run (scripts/paper_repro_reference.py, statsmodels 0.15.0, 2026-10-15), before the MAGI tool ran on this target: the declared specification (state, year and region-by-year effects as dummies, unweighted, CR1 clustered by state counting the dummies in K) gives an SE of 0.0809 against the printed 0.0769, the same 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) as Panel A, while the coefficient matches the printed one: the authors' clustered errors do not count the state effects, nested in the clusters, in K (Stata xtreg, fe). The reference keeps the declared convention; the printed cell is recorded and non-required on T1.
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: 0,08110.0811 · published_cell d=4 · absoluta 0.000050001000000000005
  • #10 figura da resposta próxima: 0,07690.0769 · published_cell d=4 (the SE as printed) · absoluta 0.000050001000000000005

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_c9d7c889:coef.cdl (infer.ols)
obrigatóriapassou—"call_c9d7c889:coef.cdl"——call_c9d7c889
2ferramenta chamada
answer call is infer.ols
obrigatóriapassou—"infer.ols"——call_c9d7c889
3argumento igual
infer.ols.y = 'l_homicide'
obrigatóriapassou"l_homicide""l_homicide"——call_c9d7c889
4argumento igual
infer.ols.x = ['cdl']
obrigatóriapassou["cdl"]["cdl"]——call_c9d7c889
5argumento igual
infer.ols.weights = None
obrigatóriapassou————call_c9d7c889
6diagnóstico
550 vs 550
obrigatóriapassou550550550550default rel 1e-6 (abs floor 1e-6)—call_c9d7c889
7figura da resposta próxima
0.08109024142 vs 0.08109024141547702
obrigatóriapassou0,08109020.081090241415477020,08109020.08109024141548848relativa 0.000001 · absoluta 8.087017349667114e-8cheng_hoekstra_2013.tab5_pb_c2.softwarecall_c9d7c889
8figura da resposta próxima
0.0808701735 vs 0.08087017349667114
registradapassou0,08087020.080870173496671140,08087020.080870173496671relativa 0.000001 · absoluta 8.087017349667114e-8cheng_hoekstra_2013.tab5_pb_c2.softwarecall_c9d7c889
9figura da resposta próxima
0.08109024142 vs 0.0811
registradapassou0,08110.08110,08109020.08109024141548848published_cell d=4 · absoluta 0.000050001000000000005cheng_hoekstra_2013.tab5_pb_c2.papercall_c9d7c889
10figura da resposta próxima
0.0808701735 vs 0.0769
registradafalhou0,07690.07690,08087020.080870173496671published_cell d=4 (the SE as printed) · absoluta 0.000050001000000000005cheng_hoekstra_2013.tab5_pb_c2.papercall_c9d7c889

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_pb_c2.tool)

Referência: infer.olsChamada do agente: infer.ols (call_c9d7c889)igual
Argumentos da referência e do agente
ArgumentoReferênciaAgente
cluster"sid""sid"igual
controls["r20001","r20002","r20003","r20004","r20011","r20012","r20013","r20014","r20021","r20022","r20023","r20024","r20031","r20032","r20033","r20034","r20041","r20042","r20043","r20044","r20051","r20052","r20053","r20054","r20061","r20062","r20063","r20064","r20071","r20072","r20073","r20074","r20081","r20082","r20083","r20084","r20091","r20092","r20093","r20094","r20101","r20102","r20103","r20104"]["r20001","r20002","r20003","r20004","r20011","r20012","r20013","r20014","r20021","r20022","r20023","r20024","r20031","r20032","r20033","r20034","r20041","r20042","r20043","r20044","r20051","r20052","r20053","r20054","r20061","r20062","r20063","r20064","r20071","r20072","r20073","r20074","r20081","r20082","r20083","r20084","r20091","r20092","r20093","r20094","r20101","r20102","r20103","r20104"]igual
dataset"castle""castle"igual
fe["sid","year"]["sid","year"]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——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.

  1. 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 B, column (2): in the state panel for 2000-2010, estimate by unweighted ordinary least squares the effect of castle doctrine laws on the log homicide rate, with state fixed effects, year fixed effects and region-by-year fixed effects (the four census regions) 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).

    file 804633c161827b6c0824f86f239046386d1a8266a866f83bf5ddb2aa762a5f29 · frame —
  2. 1Turno do modelo
    call_bbb8faa1referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache
    Texto visível

    Table 5, Panel B, column (2): the Panel B column (1) regression plus the 44 region-by-year indicators (those collinear with the year effects are dropped), unweighted, errors clustered by state.

    Chamadas pedidas
    • infer.ols
      {
        "cluster": "sid",
        "controls": [
          "r20001",
          "r20002",
          "r20003",
          "r20004",
          "r20011",
          "r20012",
          "r20013",
          "r20014",
          "r20021",
          "r20022",
          "r20023",
          "r20024",
          "r20031",
          "r20032",
          "r20033",
          "r20034",
          "r20041",
          "r20042",
          "r20043",
          "r20044",
          "r20051",
          "r20052",
          "r20053",
          "r20054",
          "r20061",
          "r20062",
          "r20063",
          "r20064",
          "r20071",
          "r20072",
          "r20073",
          "r20074",
          "r20081",
          "r20082",
          "r20083",
          "r20084",
          "r20091",
          "r20092",
          "r20093",
          "r20094",
          "r20101",
          "r20102",
          "r20103",
          "r20104"
        ],
        "dataset": "castle",
        "fe": [
          "sid",
          "year"
        ],
        "weights": null,
        "x": [
          "cdl"
        ],
        "y": "l_homicide"
      }
  3. 2Ferramenta
    infer.olsv0.1.0call_c9d7c889decidido pelo modeloreexecutável

    Motivo: Table 5, Panel B, column (2): the Panel B column (1) regression plus the 44 region-by-year indicators (those collinear with the year effects are dropped), unweighted, errors clustered by state.

    Argumentos
    {
      "cluster": "sid",
      "controls": [
        "r20001",
        "r20002",
        "r20003",
        "r20004",
        "r20011",
        "r20012",
        "r20013",
        "r20014",
        "r20021",
        "r20022",
        "r20023",
        "r20024",
        "r20031",
        "r20032",
        "r20033",
        "r20034",
        "r20041",
        "r20042",
        "r20043",
        "r20044",
        "r20051",
        "r20052",
        "r20053",
        "r20054",
        "r20061",
        "r20062",
        "r20063",
        "r20064",
        "r20071",
        "r20072",
        "r20073",
        "r20074",
        "r20081",
        "r20082",
        "r20083",
        "r20084",
        "r20091",
        "r20092",
        "r20093",
        "r20094",
        "r20101",
        "r20102",
        "r20103",
        "r20104"
      ],
      "dataset": "castle",
      "fe": [
        "sid",
        "year"
      ],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "cdl"
      ],
      "y": "l_homicide"
    }
    Figuras call_c9d7c889
    ChaveValorValor brutoErro-padrãoIC
    coef.cdl
    Coefficient on cdl in the regression of l_homicide
    0,0810902 0.081090241415488480,08087020.080870173496671-0,0814245 – 0,243605
    Diagnósticos
    • n_obs = 550550
    • k = 9191
    • r2 = 0,9152740.9152744967959459
    • df_resid = 459459
    • se_type = "cluster"
    • n_clusters = 5050
    • fixed_effects = "sid (50 levels), year (11 levels)"
    Tabelas (1)
    {
      "coefficients": [
        {
          "ci_high": 0.7741797803945534,
          "ci_low": 0.5797218136571007,
          "coef": 0.6769507970258271,
          "p": 9.694977976155178e-19,
          "se": 0.04838285303894466,
          "t": 13.99154358427212,
          "term": "Intercept"
        },
        {
          "ci_high": 0.24360493949674383,
          "ci_low": -0.08142445666576686,
          "coef": 0.08109024141548848,
          "p": 0.32092157860812803,
          "se": 0.080870173496671,
          "t": 1.00272124949536,
          "term": "cdl"
        },
        {
          "ci_high": 0.118011145029562,
          "ci_low": -0.30855797276126806,
          "coef": -0.09527341386585303,
          "p": 0.3737513155994151,
          "se": 0.10613414962263408,
          "t": -0.8976697340545243,
          "term": "r20001"
        },
        {
          "ci_high": 0.6670816915611157,
          "ci_low": 0.2010345409295885,
          "coef": 0.4340581162453521,
          "p": 0.0004775421302794401,
          "se": 0.11595663153604907,
          "t": 3.743279797761376,
          "term": "r20002"
        },
        {
          "ci_high": 1.4213196870434257,
          "ci_low": 1.190184556986409,
          "coef": 1.3057521220149173,
          "p": 1.1665835632193587e-27,
          "se": 0.05750845397239334,
          "t": 22.705394282408243,
          "term": "r20003"
        },
        {
          "ci_high": 0.30637527120200536,
          "ci_low": 0.0322601228866608,
          "coef": 0.16931769704433308,
          "p": 0.016517066877957055,
          "se": 0.06820226066950578,
          "t": 2.482581887788325,
          "term": "r20004"
        },
        {
          "ci_high": 0.06765009653479996,
          "ci_low": -0.26785042299557604,
          "coef": -0.10010016323038805,
          "p": 0.2362339851075746,
          "se": 0.08347548111949575,
          "t": -1.1991564695158083,
          "term": "r20011"
        },
        {
          "ci_high": 0.6070166406365023,
          "ci_low": 0.2459818522165092,
          "coef": 0.4264992464265057,
          "p": 0.00001825722150722625,
          "se": 0.08982863187937816,
          "t": 4.747920985807829,
          "term": "r20012"
        },
        {
          "ci_high": 1.4070434067325377,
          "ci_low": 1.1420847255514626,
          "coef": 1.2745640661420001,
          "p": 1.412026122565892e-24,
          "se": 0.065924051084442,
          "t": 19.33382498762725,
          "term": "r20013"
        },
        {
          "ci_high": 0.43897843151012084,
          "ci_low": 0.177128119799239,
          "coef": 0.30805327565467994,
          "p": 0.00001950725156523586,
          "se": 0.06515066141164881,
          "t": 4.7283215393359095,
          "term": "r20014"
        },
        {
          "ci_high": 0.05174083367412126,
          "ci_low": -0.29247248746198984,
          "coef": -0.12036582689393428,
          "p": 0.1662018533972377,
          "se": 0.08564330281752326,
          "t": -1.405431866054873,
          "term": "r20021"
        },
        {
          "ci_high": 0.5336806775097321,
          "ci_low": 0.2299203476704544,
          "coef": 0.3818005125900933,
          "p": 0.000006476895685771953,
          "se": 0.07557824266216871,
          "t": 5.051725194203365,
          "term": "r20022"
        },
        {
          "ci_high": 1.399785456828165,
          "ci_low": 1.1831573441112309,
          "coef": 1.2914714004696979,
          "p": 1.0336015332715826e-28,
          "se": 0.053898980419960864,
          "t": 23.960961606453996,
          "term": "r20023"
        },
        {
          "ci_high": 0.4429558712670536,
          "ci_low": 0.07928945777515542,
          "coef": 0.2611226645211045,
          "p": 0.005789786344785666,
          "se": 0.0904834033512999,
          "t": 2.8858625432920695,
          "term": "r20024"
        },
        {
          "ci_high": 0.08344780552353079,
          "ci_low": -0.23447674714641456,
          "coef": -0.07551447081144189,
          "p": 0.3444450108689908,
          "se": 0.07910242592462316,
          "t": -0.9546416551547958,
          "term": "r20031"
        },
        {
          "ci_high": 0.6111471986271204,
          "ci_low": 0.33902180114034774,
          "coef": 0.4750844998837341,
          "p": 6.2490647208954965e-9,
          "se": 0.06770719315677748,
          "t": 7.016750772457302,
          "term": "r20032"
        },
        {
          "ci_high": 1.4391957980628105,
          "ci_low": 1.199588314688986,
          "coef": 1.3193920563758983,
          "p": 3.667616970144512e-27,
          "se": 0.059616450020580816,
          "t": 22.13134220371084,
          "term": "r20033"
        },
        {
          "ci_high": 0.43777014470394027,
          "ci_low": 0.13057172016766602,
          "coef": 0.28417093243580316,
          "p": 0.0005164807718057423,
          "se": 0.07643367087243766,
          "t": 3.7178762866180293,
          "term": "r20034"
        },
        {
          "ci_high": 0.09609454540568925,
          "ci_low": -0.20363051086640432,
          "coef": -0.05376798273035753,
          "p": 0.4743359025099274,
          "se": 0.07457423109479173,
          "t": -0.7209994919292261,
          "term": "r20041"
        },
        {
          "ci_high": 0.5685816663812439,
          "ci_low": 0.31164652298398987,
          "coef": 0.44011409468261686,
          "p": 1.0013897728966103e-8,
          "se": 0.06392772428971019,
          "t": 6.884557515110195,
          "term": "r20042"
        },
        {
          "ci_high": 1.4006740268629754,
          "ci_low": 1.1847394760093275,
          "coef": 1.2927067514361514,
          "p": 8.561891917912833e-29,
          "se": 0.05372641612615587,
          "t": 24.060915367977007,
          "term": "r20043"
        },
        {
          "ci_high": 0.42160431421676065,
          "ci_low": 0.2081091797095755,
          "coef": 0.31485674696316807,
          "p": 3.0280344429585853e-7,
          "se": 0.053119467876249236,
          "t": 5.927332474351588,
          "term": "r20044"
        },
        {
          "ci_high": 0.0943593560277558,
          "ci_low": -0.1994759888252527,
          "coef": -0.05255831639874844,
          "p": 0.47561379983950036,
          "se": 0.0731088190738169,
          "t": -0.7189052848149698,
          "term": "r20051"
        },
        {
          "ci_high": 0.6719873551400278,
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        {
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          "p": 0.144537443780071,
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          "term": "r20101"
        },
        {
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          "coef": 0.34853940359541774,
          "p": 4.651664789532676e-7,
          "se": 0.060031105483262716,
          "t": 5.805980096311804,
          "term": "r20102"
        },
        {
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          "ci_low": 0.9343903303445598,
          "coef": 1.1124759124335803,
          "p": 6.314878573873831e-17,
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      ]
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    • dropped as collinear with the other regressors: ['r20104', 'sid[46]', 'sid[50]', 'sid[51]', 'year[2001]', 'year[2002]', 'year[2003]', 'year[2004]', '…']
    Digests dos dados
    castle 6a9af2b4a14b985d82e0f3aea93c535c24cfc2497c4d9f07242bac5f84ae11c1
    sha256 do resultado
    745b2c83ac11fda337287c1b04927c964a32c10e1db236c84e4cf35b22622592
  4. 3Turno do modelo
    call_0ef324aareferenceFim: 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.cdl = 0,08109.",
        "caveats": [],
        "figure_refs": [
          "call_c9d7c889:coef.cdl"
        ]
      }
  5. 4Guarda
    resposta aceita
    Números encontrados
    0,08109
    Sem lastro
    —
    Não citados
    —
    1 nova(s) tentativa(s) restante(s)
  6. 5Resposta

    Estimativa (infer.ols): coef.cdl = 0,08109.

    Figuras citadas: call_c9d7c889:coef.cdl · Parada: answered
    Figura da resposta: call_c9d7c889:coef.cdl = 0,08109020.08109024141548848 (se 0,08087020.080870173496671)
  7. 6Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
  8. 7Pontuação
    #1figura da resposta escolhidapassouobrigatóriaanswer figure call_c9d7c889:coef.cdl (infer.ols)
  9. 8Pontuação
    #2ferramenta chamadapassouobrigatóriaanswer call is infer.ols
  10. 9Pontuação
    #3argumento igualpassouobrigatóriainfer.ols.y = 'l_homicide'
  11. 10Pontuação
    #4argumento igualpassouobrigatóriainfer.ols.x = ['cdl']
  12. 11Pontuação
    #5argumento igualpassouobrigatóriainfer.ols.weights = None
  13. 12Pontuação
    #6diagnósticopassouobrigatória550 vs 550
  14. 13Pontuação
    #7figura da resposta próximapassouobrigatória0.08109024142 vs 0.08109024141547702
  15. 14Pontuação
    #8figura da resposta próximapassouregistrada0.0808701735 vs 0.08087017349667114
  16. 15Pontuação
    #9figura da resposta próximapassouregistrada0.08109024142 vs 0.0811
  17. 16Pontuação
    #10figura da resposta próximafalhouregistrada0.0808701735 vs 0.0769

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