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Tarefa paper_repro.wooldridge_jtrain.ex14_1_grant.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

Wooldridge, Introductory Econometrics, Example 14.1 (the effect of job training grants on firm scrap rates, Michigan manufacturing firms, 1987-1989): using the firms with scrap rates in all three years, estimate by fixed effects (a separate intercept for each of the firms) the regression of the log scrap rate on the grant indicator, the grant indicator lagged one year, and year dummies for 1988 and 1989. Report the coefficient on the grant indicator for the current year.

Resposta

Estimativa (infer.ols): coef.grant = -0,2523.

Valores esperados e de onde vêm

wooldridge_jtrain.ex14_1_grant.software

Fonte
statsmodels 0.15.0: the reference specification of wooldridge_jtrain ex14_1_grant (infer.ols, figure 'coef.grant') on an independent code path
Tipo
software
Localização
wooldridge_jtrain: Example 14.1 / Table 14.1 (edition not confirmed; fixed effects estimation, dependent variable log(scrap)), row 'grant'
Valor
-0,252315-0.25231487381477113 (se 0,1506290.15062899435450078)
Conferido por
execução de software · sm.OLS(lscrap, add_constant(['grant', 'grant_1', 'd88', 'd89']), fe dummies ['fcode']; 58 independent columns).fit(cov_type='nonrobust') on jtrain (the file pinned at sha256 2820079b04c953a3555a3722e4b14430bc9efedfd6870715a863717de8177383); n_obs = 162. 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/
  • #9 figura da resposta próxima: -0,252315-0.25231487381477113 · relativa 0.000001 · absoluta 1.5062899435450078e-7
  • #10 figura da resposta próxima: 0,1506290.15062899435450078 · relativa 0.000001 · absoluta 1.5062899435450078e-7

wooldridge_jtrain.ex14_1_grant.paper

Fonte
Wooldridge, J. M. Introductory Econometrics: A Modern Approach (edition not confirmed). Mason, OH: South-Western Cengage. Example 14.1 (Effect of job training on firm scrap rates), Table 14.1.
Tipo
textbook
Localização
Example 14.1 / Table 14.1 (edition not confirmed; fixed effects estimation, dependent variable log(scrap)), row 'grant'
Página
not read
Trecho
grant −.252 (.151)
Valor como publicado
−.252
Erro-padrão como publicado
.151
Valor
-0,252-0.252 (se 0,1510.151) log points of the scrap rate
Conferido por
não conferido · Not verified against the book or a readable reproduction (2026-10-15). The figures are those of research-papers.md section 3.21 and agree with a search engine's summary of a University of Regensburg exercise sheet (WOE_ws19_bl7.pdf) that reprints Table 14.1; that sheet now returns 404 and no archived copy exists. Data fact checked before any estimate: 162 firm-years (54 firms x 1987-1989) are complete on lscrap, grant, grant_1, d88 and d89.
Desvio conhecido
não conferido
Recorded before this track's software-reference run and before any dry run on this target; from the source's status: the textbook was not read and no reproduction of its printed Example 14.1 output could be opened (a Regensburg exercise sheet that reprints Table 14.1 now answers 404; only a search engine's summary of it was seen, which gives grant -0.252 (0.151) and grant_1 -0.422 (0.210), n = 162, the figures research-papers.md section 3.21 records). The printed cells are therefore 'unverified': recorded and counted in replication fidelity, non-required on T1.
Endereço
https://www.cengage.com/c/introductory-econometrics-a-modern-approach-7e-wooldridge/
Licença
Copyright Cengage; cited, not reproduced.
  • #11 figura da resposta próxima: -0,252-0.252 · published_cell d=3 · absoluta 0.000500001
  • #12 figura da resposta próxima: 0,1510.151 · published_cell d=3 (the SE as printed) · absoluta 0.000500001

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_c8571a1a:coef.grant (infer.ols)
obrigatóriapassou—"call_c8571a1a:coef.grant"——call_c8571a1a
2uma entre
tool_called(infer.ols): ok | tool_called(infer.fe): answer call is infer.ols
obrigatóriapassou[{"abs_tol":null,"bind":"answer_call","column":null,"failure_class":null,"field":"value","interval":false,"key":null,"kind":"tool_called","min_lre":null,"note":"","open_interval":false,"rel_tol":null,"required":true,"source_id":null,"table":null,"tool":"infer.ols","value":null,"weight":1,"where":null},{"abs_tol":null,"bind":"answer_call","column":null,"failure_class":null,"field":"value","interval":false,"key":null,"kind":"tool_called","min_lre":null,"note":"","open_interval":false,"rel_tol":null,"required":true,"source_id":null,"table":null,"tool":"infer.fe","value":null,"weight":1,"where":null}]"infer.ols"——call_c8571a1a
3argumento igual
infer.ols.y = 'lscrap'
obrigatóriapassou"lscrap""lscrap"——call_c8571a1a
4argumento igual
infer.ols.fe = ['fcode']
obrigatóriapassou["fcode"]["fcode"]——call_c8571a1a
5argumento igual
not applicable: the answer call is infer.ols, not infer.fe
obrigatóriapassou"lscrap"———call_c8571a1a
6argumento igual
not applicable: the answer call is infer.ols, not infer.fe
obrigatóriapassou"fcode"———call_c8571a1a
7diagnóstico
162 vs 162
obrigatóriapassou162162162162default rel 1e-6 (abs floor 1e-6)—call_c8571a1a
8diagnóstico
not applicable: the answer call is infer.ols, not infer.fe
obrigatóriapassou162162—default rel 1e-6 (abs floor 1e-6)—call_c8571a1a
9figura da resposta próxima
-0.2523148738 vs -0.25231487381477113
obrigatóriapassou-0,252315-0.25231487381477113-0,252315-0.2523148738147904relativa 0.000001 · absoluta 1.5062899435450078e-7wooldridge_jtrain.ex14_1_grant.softwarecall_c8571a1a
10figura da resposta próxima
0.1506289944 vs 0.15062899435450078
registradapassou0,1506290.150628994354500780,1506290.1506289943545013relativa 0.000001 · absoluta 1.5062899435450078e-7wooldridge_jtrain.ex14_1_grant.softwarecall_c8571a1a
11figura da resposta próxima
-0.2523148738 vs -0.252
registradapassou-0,252-0.252-0,252315-0.2523148738147904published_cell d=3 · absoluta 0.000500001wooldridge_jtrain.ex14_1_grant.papercall_c8571a1a
12figura da resposta próxima
0.1506289944 vs 0.151
registradapassou0,1510.1510,1506290.1506289943545013published_cell d=3 (the SE as printed) · absoluta 0.000500001wooldridge_jtrain.ex14_1_grant.papercall_c8571a1a

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.wooldridge_jtrain.ex14_1_grant.tool)

Referência: infer.olsChamada do agente: infer.ols (call_c8571a1a)igual
Argumentos da referência e do agente
ArgumentoReferênciaAgente
cluster——não fixado pela referência
controls["d88","d89"]["d88","d89"]igual
dataset"jtrain""jtrain"igual
fe["fcode"]["fcode"]igual
intercept—truenão fixado pela referência
level—0,950.95não fixado pela referência
se"classical""classical"igual
subset——não fixado pela referência
weights——não fixado pela referência
x["grant","grant_1"]["grant","grant_1"]igual
y"lscrap""lscrap"igual

Trajetória

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

  1. 0Início

    Wooldridge, Introductory Econometrics, Example 14.1 (the effect of job training grants on firm scrap rates, Michigan manufacturing firms, 1987-1989): using the firms with scrap rates in all three years, estimate by fixed effects (a separate intercept for each of the firms) the regression of the log scrap rate on the grant indicator, the grant indicator lagged one year, and year dummies for 1988 and 1989. Report the coefficient on the grant indicator for the current year.

    jtrain (agent)

    Wooldridge's JTRAIN data (Holzer, Block, Cheatham and Knott's Michigan job-training survey; R package wooldridge): one row per firm and year, 157 firms over 1987-1989 (471 rows); scrap rates exist for a subset of firms. Columns: rownames, a row number; year; fcode, firm code; employ, employees; sales, annual sales (dollars); avgsal, average employee salary; scrap, scrap rate (per 100 items); rework, rework rate; tothrs, total hours of training; union, 1 if unionized; grant, 1 if the firm received a job-training grant in that year; d88 and d89, 1 for 1988 and 1989; totrain, total employees trained; hrsemp, training hours per employee; lscrap, log of scrap; lemploy, lsales, lrework, lhrsemp, logs of those columns; lscrap_1, lscrap lagged one year; grant_1, grant lagged one year; clscrap, cgrant, clemploy, clsales, lavgsal, clavgsal, cgrant_1, chrsemp, clhrsemp, first differences and logs of the named columns.

    file 2820079b04c953a3555a3722e4b14430bc9efedfd6870715a863717de8177383 · frame —
  2. 1Turno do modelo
    call_a7933b49referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache
    Texto visível

    Wooldridge Example 14.1: fixed effects regression of the log scrap rate on the job-training grant, its one-year lag and year dummies for 1988 and 1989, firm fixed effects, classical errors.

    Chamadas pedidas
    • infer.ols
      {
        "controls": [
          "d88",
          "d89"
        ],
        "dataset": "jtrain",
        "fe": [
          "fcode"
        ],
        "se": "classical",
        "x": [
          "grant",
          "grant_1"
        ],
        "y": "lscrap"
      }
  3. 2Ferramenta
    infer.olsv0.1.0call_c8571a1adecidido pelo modeloreexecutável

    Motivo: Wooldridge Example 14.1: fixed effects regression of the log scrap rate on the job-training grant, its one-year lag and year dummies for 1988 and 1989, firm fixed effects, classical errors.

    Argumentos
    {
      "cluster": null,
      "controls": [
        "d88",
        "d89"
      ],
      "dataset": "jtrain",
      "fe": [
        "fcode"
      ],
      "intercept": true,
      "level": 0.95,
      "se": "classical",
      "subset": null,
      "weights": null,
      "x": [
        "grant",
        "grant_1"
      ],
      "y": "lscrap"
    }
    Figuras call_c8571a1a
    ChaveValorValor brutoErro-padrãoIC
    coef.grant
    Coefficient on grant in the regression of lscrap
    -0,252315 -0.25231487381479040,1506290.1506289943545013-0,551018 – 0,0463881
    coef.grant_1
    Coefficient on grant_1 in the regression of lscrap
    -0,42159 -0.421589508529215340,21020.21019996441849445-0,838424 – -0,00475509
    Diagnósticos
    • n_obs = 162162
    • k = 5858
    • r2 = 0,9275720.9275719385145765
    • df_resid = 104104
    • se_type = "classical"
    • n_clusters = —
    • fixed_effects = "fcode (54 levels)"
    Tabelas (1)
    {
      "coefficients": [
        {
          "ci_high": -2.2384743154419002,
          "ci_low": -3.4131636211165306,
          "coef": -2.8258189682792154,
          "p": 7.148679426290452e-16,
          "se": 0.29618433589543847,
          "t": -9.540744144136003,
          "term": "Intercept"
        },
        {
          "ci_high": 0.04638807456669097,
          "ci_low": -0.5510178221962718,
          "coef": -0.2523148738147904,
          "p": 0.09692391893977843,
          "se": 0.1506289943545013,
          "t": -1.6750750736672522,
          "term": "grant"
        },
        {
          "ci_high": -0.004755091021796887,
          "ci_low": -0.8384239260366337,
          "coef": -0.42158950852921534,
          "p": 0.04748974265972461,
          "se": 0.21019996441849445,
          "t": -2.00565927637294,
          "term": "grant_1"
        },
        {
          "ci_high": 0.1368776115185822,
          "ci_low": -0.29730896145103164,
          "coef": -0.08021567496622473,
          "p": 0.46537157858748945,
          "se": 0.1094751277197217,
          "t": -0.732729676932581,
          "term": "d88"
        },
        {
          "ci_high": 0.016974077007711197,
          "ci_low": -0.5113796650806335,
          "coef": -0.24720279403646112,
          "p": 0.06633915572683147,
          "se": 0.1332182913918953,
          "t": -1.8556220129655585,
          "term": "d89"
        }
      ]
    }
    • 309 rows were dropped because one of 'lscrap', 'grant', 'grant_1', 'd88', 'd89', 'fcode' was missing
    Digests dos dados
    jtrain ce556c6a511400be9674bea971d3ba4cff3c7e1149b038c7ebbcf69c15dab73c
    sha256 do resultado
    5c009c4d415bb861929a559521b2c1a2487239196f5ba6a5dbbd506d15148bf6
  4. 3Turno do modelo
    call_34d8c0fbreferenceFim: 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.grant = -0,2523.",
        "caveats": [],
        "figure_refs": [
          "call_c8571a1a:coef.grant"
        ]
      }
  5. 4Guarda
    resposta aceita
    Números encontrados
    -0,2523
    Sem lastro
    —
    Não citados
    —
    1 nova(s) tentativa(s) restante(s)
  6. 5Resposta

    Estimativa (infer.ols): coef.grant = -0,2523.

    Figuras citadas: call_c8571a1a:coef.grant · Parada: answered
    Figura da resposta: call_c8571a1a:coef.grant = -0,252315-0.2523148738147904 (se 0,1506290.1506289943545013)
  7. 6Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
  8. 7Pontuação
    #1figura da resposta escolhidapassouobrigatóriaanswer figure call_c8571a1a:coef.grant (infer.ols)
  9. 8Pontuação
    #2uma entrepassouobrigatóriatool_called(infer.ols): ok | tool_called(infer.fe): answer call is infer.ols
  10. 9Pontuação
    #3argumento igualpassouobrigatóriainfer.ols.y = 'lscrap'
  11. 10Pontuação
    #4argumento igualpassouobrigatóriainfer.ols.fe = ['fcode']
  12. 11Pontuação
    #5argumento igualpassouobrigatórianot applicable: the answer call is infer.ols, not infer.fe
  13. 12Pontuação
    #6argumento igualpassouobrigatórianot applicable: the answer call is infer.ols, not infer.fe
  14. 13Pontuação
    #7diagnósticopassouobrigatória162 vs 162
  15. 14Pontuação
    #8diagnósticopassouobrigatórianot applicable: the answer call is infer.ols, not infer.fe
  16. 15Pontuação
    #9figura da resposta próximapassouobrigatória-0.2523148738 vs -0.25231487381477113
  17. 16Pontuação
    #10figura da resposta próximapassouregistrada0.1506289944 vs 0.15062899435450078
  18. 17Pontuação
    #11figura da resposta próximapassouregistrada-0.2523148738 vs -0.252
  19. 18Pontuação
    #12figura da resposta próximapassouregistrada0.1506289944 vs 0.151

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