Tarefa crb_papers.q037

Veredito

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?

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
   ds_0019— | 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.
   ref_ever
   ref_panel
   ref_years
   Transformação "select" aplicada à base "ref_years" (17010 → 17010 linhas). {"columns":["treated","lobby","ticker","year"]}
   Transformação "dropna" aplicada à base "ref_years" (17010 → 17010 linhas). {"columns":["year","treated","lobby","ticker"]}
   Transformação "filter" aplicada à base "ref_years" (17010 → 5670 linhas). {"query":"year in [2002, 2006]"}
   Transformação "collapse" aplicada à base "ref_years" (5670 → 5670 linhas). {"aggs":[{"column":"lobby","fn":"count","name":"n"}],"by":["ticker","year"]}
   Transformação "collapse" aplicada à base "ref_years" (5670 → 2835 linhas). {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["ticker"]}
   Transformação "select" aplicada à base "ref_ever" (17010 → 17010 linhas). {"columns":["treated","lobby","ticker","year"]}
   Transformação "dropna" aplicada à base "ref_ever" (17010 → 17010 linhas). {"columns":["year","treated","lobby","ticker"]}
   Transformação "filter" aplicada à base "ref_ever" (17010 → 5670 linhas). {"query":"year in [2002, 2006]"}
   Transformação "collapse" aplicada à base "ref_ever" (5670 → 2835 linhas). {"aggs":[{"column":"treated","fn":"max","name":"ever_treated"}],"by":["ticker"]}
   Transformação "select" aplicada à base "ref_panel" (17010 → 17010 linhas). {"columns":["treated","lobby","ticker","year"]}
   Transformação "dropna" aplicada à base "ref_panel" (17010 → 17010 linhas). {"columns":["year","treated","lobby","ticker"]}
   Transformação "filter" aplicada à base "ref_panel" (17010 → 5670 linhas). {"query":"year in [2002, 2006]"}
   Transformação "merge" aplicada à base "ref_panel" (5670 → 5670 linhas). {"how":"left","on":["ticker"],"with":"ref_years"}
   Transformação "filter" aplicada à base "ref_panel" (5670 → 5670 linhas). {"query":"nyears == 2"}
   Transformação "merge" aplicada à base "ref_panel" (5670 → 5670 linhas). {"how":"inner","on":["ticker"],"with":"ref_ever"}
   Transformação "derive" aplicada à base "ref_panel" (5670 → 5670 linhas). {"args":{"cmp":"eq","column":"year","value":2006},"name":"post","op":"indicator"}
2. 2Chamada que gerou a resposta finalinfer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na respostacoincide com a referência

   Ver chamada call_b3ae7619 nos detalhes

   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)

   Por que este é o caminho de referência: 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 principais

   cluster:

   "ticker"

   controls:

   ["post","ever_treated"]

   dataset:

   "ref_panel"

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["treated"]

   y:

   "lobby"

   Resultado calculado

   • coef.treated = 0,031416 0.031415974836281134
     Coefficient on treated in the regression of lobby
   Diagnósticos: n_obs = 5.6705670 (observations used) · k = 44 (parameters estimated, fixed-effect dummies included) · r2 = 0,003694930.003694933910306286 · df_resid = 5.6665666 · se_type = "cluster" (cluster-robust standard errors (CR1) by 'ticker') · n_clusters = 2.8352835 (2835 clusters of 'ticker')
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: 0,03142
   Figuras citadas: call_b3ae7619:coef.treated
   • ✓
     #0resposta com lastro

     every number traced to a tool call or the question
   • ✓
     #1figura da resposta próxima

     CRB estimate_within_solution_ci: the answer figure inside the gold 95% interval effect ± 1.96·|SE| of source crb_papers.q037.gold (closed, evaluate_result.py; no --auto-rescale)
   • ✓
     #2figura da resposta próxima

     replication cell: the gold effect to rel 1e-6 (abs floor 1e-6·SE); never required
   • ✓
     #3figura da resposta próxima

     replication cell: the gold SE to rel 1e-6; never required

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

   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_e6bf6586referenceFim: 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"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
19. 18Ferramenta
    infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_b3ae7619decidido 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_b3ae7619

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.treated Coefficient on treated in the regression of lobby	0,031416	0.031415974836281134	0,01413380.01413383388929902	0,00370233 – 0,0591296

    Diagnósticos

    • n_obs = 5.6705670 (observations used)
    • k = 44 (parameters estimated, fixed-effect dummies included)
    • r2 = 0,003694930.003694933910306286
    • df_resid = 5.6665666
    • se_type = "cluster" (cluster-robust standard errors (CR1) by 'ticker')
    • n_clusters = 2.8352835 (2835 clusters of 'ticker')

    Derivação matemática e pressupostos

    coef.treated

    1. Coeficiente de treated na regressão por mínimos quadrados ordinários de lobby.→ 0,031416 (0.031415974836281134)
    2. Erro-padrão robusto por conglomerado (CR1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,0141338 (0.01413383388929902)
    3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 2834 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 'ticker' and may be correlated within them

    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"
        }
      ]
    }

    Hashes SHA-256 e metadados de reprodução

    Digests dos dados

    ref_panel d46fe601ac0edfa55aeef72cc5c4e0a09782418f70d21623b56cd8d70128dd45

    sha256 do resultado

    19e60aadeffd1f82de165ce49be18f23f7cec30f9bf3bbebdd80800e6f0a68a5

    Resultado mostrado ao modelo
20. 19Turno do modelo
    call_00062478referenceFim: 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_b3ae7619:coef.treated"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
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_b3ae7619:coef.treated · Parada: answered

    Figura da resposta: call_b3ae7619: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

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