Tarefa paper_repro.cheng_hoekstra_2013.tab5_pa_c1.agent

Veredito

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.

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
   castle— 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).
2. 2Chamada que gerou a resposta finalinfer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na respostacoincide com a referência

   Ver chamada call_6e86f3fa nos detalhes

   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.

   Por que este é o caminho de referência: 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 principais

   cluster:

   "sid"

   dataset:

   "castle"

   fe:

   ["sid","year"]

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   weights:

   "popwt"

   x:

   ["cdl"]

   y:

   "l_homicide"

   Resultado calculado

   • coef.cdl = 0,0800635 0.08006351033824458
     Coefficient on cdl in the regression of l_homicide
   Diagnósticos: n_obs = 550550 (observations used) · k = 6161 (parameters estimated, fixed-effect dummies included) · r2 = 0,9341670.9341667643844439 · df_resid = 489489 · se_type = "cluster" (cluster-robust standard errors (CR1) by 'sid') · n_clusters = 5050 (50 clusters of 'sid') · fixed_effects = "sid (50 levels), year (11 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: 0,08006
   Figuras citadas: call_6e86f3fa:coef.cdl
   • ✓
     #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
   • ✓
     #2ferramenta chamadainfer.ols

     Exige que o resultado venha da ferramenta infer.ols.
   • ✓
     #3argumento igualy

     outcome: log homicide rate
   • ✓
     #4argumento igualx

     the treatment is the fractional law variable, not the binary post indicator
   • ✓
     #5argumento igualweights

     Panel A is weighted by state population
   • ✓
     #6diagnósticon_obs

     Confere o diagnóstico "n_obs" reportado pela ferramenta.
   • ✓
     #7figura da resposta próxima

     software reference
   • ✓
     #8figura da resposta próxima

     software reference, SE: recorded, not required (the question does not state the convention)
   • ✓
     #9figura da resposta próxima

     published cell: recorded, never required on T2/T3
   • ✗
     #10figura da resposta próxima

     published cell, SE: recorded, never required on T2/T3

Valores esperados e de onde vêm

Verificações

Caminho de referência e caminho do agente

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

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 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).

   file 804633c161827b6c0824f86f239046386d1a8266a866f83bf5ddb2aa762a5f29 · frame —
2. 1Turno do modelo
   call_ce8ec9cereferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   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.

   Chamadas pedidas

   • infer.ols

     {
       "cluster": "sid",
       "controls": [],
       "dataset": "castle",
       "fe": [
         "sid",
         "year"
       ],
       "weights": "popwt",
       "x": [
         "cdl"
       ],
       "y": "l_homicide"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_6e86f3fadecidido pelo modeloreexecutável

   Motivo: 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_homicide	0,0800635	0.08006351033824458	0,03589850.0358984941171576	0,00792279 – 0,152204

   Diagnósticos

   • n_obs = 550550 (observations used)
   • k = 6161 (parameters estimated, fixed-effect dummies included)
   • r2 = 0,9341670.9341667643844439
   • df_resid = 489489
   • se_type = "cluster" (cluster-robust standard errors (CR1) by 'sid')
   • n_clusters = 5050 (50 clusters of 'sid')
   • fixed_effects = "sid (50 levels), year (11 levels)" (absorbed as dummies; their coefficients are not tabulated)

   Derivação matemática e pressupostos

   coef.cdl

   1. Coeficiente de cdl na regressão por mínimos quadrados ponderados de l_homicide, efeitos fixos de sid, year.→ 0,0800635 (0.08006351033824458)
   2. Erro-padrão robusto por conglomerado (CR1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,0358985 (0.0358984941171576)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 49 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 'sid' and may be correlated within them

   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"
       }
     ]
   }

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   castle 6a9af2b4a14b985d82e0f3aea93c535c24cfc2497c4d9f07242bac5f84ae11c1

   sha256 do resultado

   ded38c2380f9b375e6b2aed585e79881ac0dca015e577e8c24addfcbfbe5a607

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_44a79161referenceFim: 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 = 0,08006.",
       "caveats": [],
       "figure_refs": [
         "call_6e86f3fa:coef.cdl"
       ]
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
5. 4Guarda
   resposta aceita

   Números encontrados

   0,08006

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
6. 5Resposta

   Estimativa (infer.ols): coef.cdl = 0,08006.

   Figuras citadas: call_6e86f3fa:coef.cdl · Parada: answered

   Figura da resposta: call_6e86f3fa:coef.cdl = 0,08006350.08006351033824458 (se 0,03589850.0358984941171576)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta escolhidapassouobrigatóriaanswer figure call_6e86f3fa: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 = 'popwt'
13. 12Pontuação
    #6diagnósticopassouobrigatória550 vs 550
14. 13Pontuação
    #7figura da resposta próximapassouobrigatória0.08006351034 vs 0.08006351033824417
15. 14Pontuação
    #8figura da resposta próximapassouregistrada0.03589849412 vs 0.03589849411715782
16. 15Pontuação
    #9figura da resposta próximapassouregistrada0.08006351034 vs 0.0801
17. 16Pontuação
    #10figura da resposta próximafalhouregistrada0.03589849412 vs 0.0342

Épocas × verificações

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