Tarefa causcibench_real.q030

Veredito

Pergunta feita ao analista

What is the effect of fish prices on quantity demanded?

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

   What is the effect of fish prices on quantity demanded?

   graddy_markets_fulton (agent)

   The dataset was collected from the Fulton Fish Market, a historic wholesale fish market in New York City. The purpose of the data collection was to examine the economic and institutional dynamics of how the market functioned. Fish were sold in bulk to a wide range of buyers, including restaurants, retail shops, and independent vendors. Transactions typically took place in the early morning hours, with prices and quantities influenced by factors such as day of the week, weather conditions, and supply levels. Sales were higher on Mondays, Thursdays, and Fridays, compared to Tuesdays and Wednesdays. Sellers in the market set prices individually for each customer. The dataset focuses specifically on whiting, a commonly traded fish in the market. Price information was gathered directly through daily visits to the market and from reports provided by salesmen. Variables: mon: 1 if the day is Monday, 0 otherwise tu: 1 if the day is Tuesday, 0 otherwise wed: 1 if the day is Wednesday, 0 otherwise thu: 1 if the day is Thursday, 0 otherwise date: Date in Year Month Day format stormy: whether or not the weather was stormy; 1 if stormy, 0 otherwise mixed: 1 if the weather was mixed, 0 otherwise p: logarithm of the price per pound q: logarithm of the total quantity sold in pounds rainy: 1 if the weather was rainy, 0 otherwise cold: 1 if the weather was cold, 0 otherwise wind: wind speed

   file 8483868338caa50ffd745475890ce33fd2c24bee6980c5b01f67827008ce6e04 · frame —
2. 1Turno do modelo
   call_217c151dreferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   causcibench_metadata_rule/1: iv

   Chamadas pedidas

   • infer.iv_2sls

     {
       "dataset": "graddy_markets_fulton",
       "endog": [
         "p"
       ],
       "exog": [
         "mon",
         "tue",
         "wed",
         "thu",
         "cold",
         "rainy"
       ],
       "instruments": [
         "stormy"
       ],
       "y": "q"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.iv_2slsv0.1.0call_b290a231decidido pelo modeloreexecutável

   Motivo: causcibench_metadata_rule/1: iv

   Argumentos

   {
     "cluster": null,
     "dataset": "graddy_markets_fulton",
     "endog": [
       "p"
     ],
     "exog": [
       "mon",
       "tue",
       "wed",
       "thu",
       "cold",
       "rainy"
     ],
     "instruments": [
       "stormy"
     ],
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "y": "q"
   }

   Figuras call_b290a231

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.p 2SLS effect of p on q	-1,2228	-1.2227961255954154	0,5444260.5444260408231231	-2,30254 – -0,143055

   Diagnósticos

   • first_stage_F.p = 16,272516.27249139172
   • partial_r2.p = 0,1242380.1242383793595031
   • weak_instrument = false
   • n_obs = 111111
   • se_type = "HC1"
   • n_clusters = —

   Tabelas (3)

   {
     "coefficients": [
       {
         "ci_high": 8.850153332427293,
         "ci_low": 8.033336845637436,
         "coef": 8.441745089032365,
         "p": 1.3420750105382108e-65,
         "se": 0.2059273064917834,
         "t": 40.99381103384264,
         "term": "Intercept"
       },
       {
         "ci_high": 0.43585355652193225,
         "ci_low": -0.5024394658333136,
         "coef": -0.03329295465569063,
         "p": 0.8883484675938775,
         "se": 0.23655271155583382,
         "t": -0.14074222373829123,
         "term": "mon"
       },
       {
         "ci_high": -0.107527932457394,
         "ci_low": -0.9580223976130225,
         "coef": -0.5327751650352083,
         "p": 0.014574943615062463,
         "se": 0.21441784933110322,
         "t": -2.4847519303884953,
         "term": "tue"
       },
       {
         "ci_high": -0.1367061037168431,
         "ci_low": -1.0144477315962073,
         "coef": -0.5755769176565252,
         "p": 0.010661172080894451,
         "se": 0.2212871215850137,
         "t": -2.6010411881804925,
         "term": "wed"
       },
       {
         "ci_high": 0.4848555132988591,
         "ci_low": -0.24910174552925854,
         "coef": 0.1178768838848003,
         "p": 0.5255105393341747,
         "se": 0.18503769675923726,
         "t": 0.6370425375439925,
         "term": "thu"
       },
       {
         "ci_high": 0.4049541624135344,
         "ci_low": -0.2688470417186741,
         "coef": 0.06805356034743015,
         "p": 0.689531727297729,
         "se": 0.16987177575611723,
         "t": 0.40061723052294335,
         "term": "cold"
       },
       {
         "ci_high": 0.38659937809180467,
         "ci_low": -0.24254351505803853,
         "coef": 0.07202793151688305,
         "p": 0.6507042728062967,
         "se": 0.15861298526669745,
         "t": 0.454111190176345,
         "term": "rainy"
       },
       {
         "ci_high": -0.14305547934994522,
         "ci_low": -2.3025367718408853,
         "coef": -1.2227961255954154,
         "p": 0.026837377596898613,
         "se": 0.5444260408231231,
         "t": -2.246027988937961,
         "term": "p"
       }
     ],
     "first_stage": [
       {
         "ci_high": 0.4663758623874241,
         "ci_low": 0.15894073361481612,
         "coef": 0.3126582980011201,
         "endog": "p",
         "instrument": "stormy",
         "p": 0.0001054824297292162,
         "se": 0.07750735815569477,
         "t": 4.033917623318553
       }
     ],
     "reduced_form": [
       {
         "ci_high": -0.0661663567114893,
         "ci_low": -0.6984683541505637,
         "coef": -0.3823173554310265,
         "instrument": "stormy",
         "p": 0.018269053556881444,
         "se": 0.1594094258965441,
         "t": -2.39833594080659
       }
     ]
   }

   Digests dos dados

   graddy_markets_fulton 91d6153b52e63629c6d2375873d3efa34d54fce4526875b3141fe1fda8dd70f3

   sha256 do resultado

   e8c6f6f721bc0454077db62932bb0fca9ac74a6057c045e75ea36a01339fec9d

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_6f8e8467referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Estimativa (infer.iv_2sls): coef.p = -1,223.",
       "caveats": [],
       "figure_refs": [
         "call_b290a231:coef.p"
       ]
     }

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

   Números encontrados

   -1,223

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.iv_2sls): coef.p = -1,223.

   Figuras citadas: call_b290a231:coef.p · Parada: answered

   Figura da resposta: call_b290a231:coef.p = -1,2228-1.2227961255954154 (se 0,5444260.5444260408231231)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximapassouobrigatória-1.222796126 vs -1.223

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)

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