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Tarefa crb_papers.q037

crb_papersagentT2publicépoca 1, tentativa 1Execução: 20261015T170050Z-suite-crb_papers-scripted-8b13b2

Veredito

passouPassou em todas as verificações obrigatórias.

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

Pergunta feita ao analista

Did designation as fully exempt from short-sale price restrictions alter the probability that firms reported any federal lobbying activity between 2002 and 2006?

Resposta

Estimativa (infer.ols): coef.treated = 0,03142. Reference path transcribed from the item's own estimation script.

Valores esperados e de onde vêm

crb_papers.q037.gold

Fonte
CausalReasoningBenchmark, causal_queries.json, index 37 (revision edd0fe1c1d1c)
Tipo
benchmark_gold
Localização
causal_queries.json['37'].effect and .standard_error
Trecho
index 37: effect 0.031416, standard_error 0.0141338
Valor como publicado
0.0314159748362815
Erro-padrão como publicado
0.014133833889299082
Valor
0,0314160.0314159748362815 (se 0,01413380.014133833889299082)
Conferido por
arquivo do benchmark · Read from the catalog at the pinned revision by scripts/freeze_crb_papers.py; the catalog states the value its estimation script prints.
Endereço
https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/causal_queries.json
Licença
MIT
  • #2 figura da resposta próxima: 0,0314160.0314159748362815 · relativa 0.000001 · absoluta 1.4133833889299081e-8
  • #3 figura da resposta próxima: 0,01413380.014133833889299082 · relativa 0.000001 · absoluta 1.4133833889299081e-8

crb_papers.q037.script

Fonte
CausalReasoningBenchmark reference estimation script for index 37
Tipo
software
Localização
solutions/research_papers/DiD/Kroeger_Silfa_2023/question_2/estimation.py
Valor
—
Conferido por
arquivo do benchmark · sha256 of the script's 1687 bytes at the pinned revision; the reference path of the item is a transcription of this script into typed transforms and tool calls (never executed here).
Implementação
independente da ferramenta
sha256 da cópia lida
c833fccb4d1faf1c5b46272f460870d4ac8d125aa40ec07fb1860bf6648360f7 (identifica a cópia lida, não um arquivo canônico)
Endereço
https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/solutions/research_papers/DiD/Kroeger_Silfa_2023/question_2/estimation.py
Licença
MIT

    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 próxima
    0.03141597484 in [0.003713660413, 0.05911828926]?
    obrigatóriapassou[0.003713660413255302,0.059118289259307705]0,0314160.031415974836281134interval · [0.003713660413255302, 0.059118289259307705]—call_67d201f2
    2figura da resposta próxima
    0.03141597484 vs 0.0314159748362815
    registradapassou0,0314160.03141597483628150,0314160.031415974836281134relativa 0.000001 · absoluta 1.4133833889299081e-8crb_papers.q037.goldcall_67d201f2
    3figura da resposta próxima
    0.01413383389 vs 0.014133833889299082
    registradapassou0,01413380.0141338338892990820,01413380.01413383388929902relativa 0.000001 · absoluta 1.4133833889299081e-8crb_papers.q037.goldcall_67d201f2

    Caminho de referência e caminho do agente

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

    caminho de referência do item · benchmark_script

    Referência: infer.olsChamada do agente: infer.ols (call_67d201f2)igual
    Argumentos da referência e do agente
    ArgumentoReferênciaAgente
    cluster"ticker""ticker"igual
    controls["post","ever_treated"]["post","ever_treated"]igual
    dataset"ref_panel""ref_panel"igual
    fe—[]não fixado pela referência
    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["treated"]["treated"]igual
    y"lobby""lobby"igual

    Trajetória

    Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.

    1. 0Início

      Did designation as fully exempt from short-sale price restrictions alter the probability that firms reported any federal lobbying activity between 2002 and 2006?

      ds_0019 (agent)

      | Variable Name | Description | | --- | --- | | treated | Binary indicator (0/1) for whether the company’s security was listed by the U.S. Securities and Exchange Commission (SEC) as fully exempt from short-sale price tests under the Regulation SHO pilot in that calendar year. Based on official SEC pilot rosters first released in 2004 and updated in April 2005. Values are 1 in years when the exemption applied (primarily 2005–2007) and 0 otherwise (pre-pilot years are coded 0). | | lobby | Binary indicator (0/1) for whether the company reported any federally registered lobbying activity during the calendar year, as recorded in LobbyView from Lobbying Disclosure Act (LDA) filings. This captures participation (any filing recorded) rather than amounts or targets. | | ticker | Stock ticker symbol used to identify the firm across years. Tickers reflect exchange-listed identifiers for Russell 3000 companies in the early-to-mid 2000s; symbols may change over time due to corporate actions. | | year | Calendar year of the observation (2002–2007). | Dataset Context: This panel links two administrative sources for U.S. publicly traded firms in the Russell 3000 around the mid-2000s. One source is the SEC’s Regulation SHO pilot, adopted in 2004 to study market effects of relaxing short-sale price tests. The pilot began on May 2, 2005, and concluded on August 6, 2007. To populate the pilot roster, the SEC sorted securities by exchange, ranked them using prior-year average daily dollar volume, and selected every third stock for full exemption from short-sale price tests. The Commission released an initial list in 2004 and an updated list in April 2005 to reflect mergers, delistings, and other corporate changes during implementation. The second source is LobbyView, which compiles federal lobbying registrations and periodic activity reports mandated by the Lobbying Disclosure Act. During 2002–2007, filings were made on a semiannual schedule and include the registrant, clients, and issues lobbied. For this dataset, those filings are harmonized to a firm-year indicator that flags whether a company is observed to have any registered federal lobbying activity in a given calendar year. Each row in the file represents a ticker–year pairing from 2002 through 2007, spanning pre-pilot and pilot periods. Because the composition of exchange-listed firms evolves, the panel reflects contemporaneous Russell 3000 constituents and incorporates SEC updates to pilot participation where securities were added or removed due to corporate events. The resulting structure enables researchers to observe, for each firm and year, its exemption status under the SEC pilot and whether it engaged in registered federal lobbying, alongside stable identifiers and a consistent annual time scale.

      file df584143267596bb0072e20d3c0d280681eb8d7110ec9f356bc1ad8a1108570d · frame —
      ref_ever (reference)
      file df584143267596bb0072e20d3c0d280681eb8d7110ec9f356bc1ad8a1108570d · frame —
      1. select {"columns":["treated","lobby","ticker","year"]}
      2. dropna {"columns":["year","treated","lobby","ticker"]}
      3. filter {"query":"year in [2002, 2006]"}
      4. collapse {"aggs":[{"column":"treated","fn":"max","name":"ever_treated"}],"by":["ticker"]}
      ref_panel (reference)
      file df584143267596bb0072e20d3c0d280681eb8d7110ec9f356bc1ad8a1108570d · frame —
      1. select {"columns":["treated","lobby","ticker","year"]}
      2. dropna {"columns":["year","treated","lobby","ticker"]}
      3. filter {"query":"year in [2002, 2006]"}
      4. merge {"how":"left","on":["ticker"],"with":"ref_years"}
      5. filter {"query":"nyears == 2"}
      6. merge {"how":"inner","on":["ticker"],"with":"ref_ever"}
      7. derive {"args":{"cmp":"eq","column":"year","value":2006},"name":"post","op":"indicator"}
      ref_years (reference)
      file df584143267596bb0072e20d3c0d280681eb8d7110ec9f356bc1ad8a1108570d · frame —
      1. select {"columns":["treated","lobby","ticker","year"]}
      2. dropna {"columns":["year","treated","lobby","ticker"]}
      3. filter {"query":"year in [2002, 2006]"}
      4. collapse {"aggs":[{"column":"lobby","fn":"count","name":"n"}],"by":["ticker","year"]}
      5. collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["ticker"]}
    2. 1Transformação
      ref_years · select {"columns":["treated","lobby","ticker","year"]}
      17010 → 17010 linhas
      Frame antes → depois: 053d54092dea → 053d54092dea
    3. 2Transformação
      ref_years · dropna {"columns":["year","treated","lobby","ticker"]}
      17010 → 17010 linhas
      Frame antes → depois: 053d54092dea → 053d54092dea
    4. 3Transformação
      ref_years · filter {"query":"year in [2002, 2006]"}
      17010 → 5670 linhas
      Frame antes → depois: 053d54092dea → 461e048c621f
    5. 4Transformação
      ref_years · collapse {"aggs":[{"column":"lobby","fn":"count","name":"n"}],"by":["ticker","year"]}
      5670 → 5670 linhas
      Frame antes → depois: 461e048c621f → eccb83382cfa
    6. 5Transformação
      ref_years · collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["ticker"]}
      5670 → 2835 linhas
      Frame antes → depois: eccb83382cfa → c1ec65da028a
    7. 6Transformação
      ref_ever · select {"columns":["treated","lobby","ticker","year"]}
      17010 → 17010 linhas
      Frame antes → depois: 053d54092dea → 053d54092dea
    8. 7Transformação
      ref_ever · dropna {"columns":["year","treated","lobby","ticker"]}
      17010 → 17010 linhas
      Frame antes → depois: 053d54092dea → 053d54092dea
    9. 8Transformação
      ref_ever · filter {"query":"year in [2002, 2006]"}
      17010 → 5670 linhas
      Frame antes → depois: 053d54092dea → 461e048c621f
    10. 9Transformação
      ref_ever · collapse {"aggs":[{"column":"treated","fn":"max","name":"ever_treated"}],"by":["ticker"]}
      5670 → 2835 linhas
      Frame antes → depois: 461e048c621f → 0d941e3038ab
    11. 10Transformação
      ref_panel · select {"columns":["treated","lobby","ticker","year"]}
      17010 → 17010 linhas
      Frame antes → depois: 053d54092dea → 053d54092dea
    12. 11Transformação
      ref_panel · dropna {"columns":["year","treated","lobby","ticker"]}
      17010 → 17010 linhas
      Frame antes → depois: 053d54092dea → 053d54092dea
    13. 12Transformação
      ref_panel · filter {"query":"year in [2002, 2006]"}
      17010 → 5670 linhas
      Frame antes → depois: 053d54092dea → 461e048c621f
    14. 13Transformação
      ref_panel · merge {"how":"left","on":["ticker"],"with":"ref_years"}
      5670 → 5670 linhas
      Frame antes → depois: 461e048c621f → afa6a0fc593d
    15. 14Transformação
      ref_panel · filter {"query":"nyears == 2"}
      5670 → 5670 linhas
      Frame antes → depois: afa6a0fc593d → afa6a0fc593d
    16. 15Transformação
      ref_panel · merge {"how":"inner","on":["ticker"],"with":"ref_ever"}
      5670 → 5670 linhas
      Frame antes → depois: afa6a0fc593d → d77ab9c4d1bb
    17. 16Transformação
      ref_panel · derive {"args":{"cmp":"eq","column":"year","value":2006},"name":"post","op":"indicator"}
      5670 → 5670 linhas
      Frame antes → depois: d77ab9c4d1bb → d46fe601ac0e
    18. 17Turno do modelo
      call_a588c170referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache
      Texto visível

      smf.ols('lobby ~ post + ever_treated + treated').fit(cov_type='cluster', groups=ticker) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

      Chamadas pedidas
      • infer.ols
        {
          "cluster": "ticker",
          "controls": [
            "post",
            "ever_treated"
          ],
          "dataset": "ref_panel",
          "x": [
            "treated"
          ],
          "y": "lobby"
        }
    19. 18Ferramenta
      infer.olsv0.1.0call_67d201f2decidido pelo modeloreexecutável

      Motivo: smf.ols('lobby ~ post + ever_treated + treated').fit(cov_type='cluster', groups=ticker) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

      Argumentos
      {
        "cluster": "ticker",
        "controls": [
          "post",
          "ever_treated"
        ],
        "dataset": "ref_panel",
        "fe": [],
        "intercept": true,
        "level": 0.95,
        "se": "HC1",
        "subset": null,
        "weights": null,
        "x": [
          "treated"
        ],
        "y": "lobby"
      }
      Figuras call_67d201f2
      ChaveValorValor brutoErro-padrãoIC
      coef.treated
      Coefficient on treated in the regression of lobby
      0,031416 0.0314159748362811340,01413380.014133833889299020,00370233 – 0,0591296
      Diagnósticos
      • n_obs = 5.6705670
      • k = 44
      • r2 = 0,003694930.003694933910306286
      • df_resid = 5.6665666
      • se_type = "cluster"
      • n_clusters = 2.8352835
      Tabelas (1)
      {
        "coefficients": [
          {
            "ci_high": 0.2982178167343167,
            "ci_low": 0.25780755324454163,
            "coef": 0.27801268498942916,
            "p": 6.872283141367422e-143,
            "se": 0.010304527336758984,
            "t": 26.979663977180603,
            "term": "Intercept"
          },
          {
            "ci_high": 0.059129616273760496,
            "ci_low": 0.003702333398801768,
            "coef": 0.031415974836281134,
            "p": 0.026311304113811117,
            "se": 0.01413383388929902,
            "t": 2.2227496857782327,
            "term": "treated"
          },
          {
            "ci_high": 0.0580728060936879,
            "ci_low": 0.02543670764838398,
            "coef": 0.04175475687103594,
            "p": 5.56348810166744e-7,
            "se": 0.008322132536450427,
            "t": 5.0173145750987125,
            "term": "post"
          },
          {
            "ci_high": 0.006515033124303477,
            "ci_low": -0.06201018040963055,
            "coef": -0.027747573642663535,
            "p": 0.11240804204611904,
            "se": 0.017473777083793045,
            "t": -1.5879551118000386,
            "term": "ever_treated"
          }
        ]
      }
      Digests dos dados
      ref_panel d46fe601ac0edfa55aeef72cc5c4e0a09782418f70d21623b56cd8d70128dd45
      sha256 do resultado
      160697367d5d411a8fced740aba7b03682daa106ebdb5d952b0843c076adde15
    20. 19Turno do modelo
      call_357b2ba3referenceFim: 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.treated = 0,03142. Reference path transcribed from the item's own estimation script.",
          "caveats": [],
          "figure_refs": [
            "call_67d201f2:coef.treated"
          ]
        }
    21. 20Guarda
      resposta aceita
      Números encontrados
      0,03142
      Sem lastro
      —
      Não citados
      —
      1 nova(s) tentativa(s) restante(s)
    22. 21Resposta

      Estimativa (infer.ols): coef.treated = 0,03142. Reference path transcribed from the item's own estimation script.

      Figuras citadas: call_67d201f2:coef.treated · Parada: answered
      Figura da resposta: call_67d201f2:coef.treated = 0,0314160.031415974836281134 (se 0,01413380.01413383388929902)
    23. 22Pontuação
      #0resposta com lastropassouobrigatóriaevery number backed
    24. 23Pontuação
      #1figura da resposta próximapassouobrigatória0.03141597484 in [0.003713660413, 0.05911828926]?
    25. 24Pontuação
      #2figura da resposta próximapassouregistrada0.03141597484 vs 0.0314159748362815
    26. 25Pontuação
      #3figura da resposta próximapassouregistrada0.01413383389 vs 0.014133833889299082

    Épocas × verificações

    Resultado de cada verificação em cada época pontuada
    #0123
    Época 1passoupassoupassoupassou

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