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Tarefa paper_repro.mrw_1992.tab1_nonoil_lngd.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

Mankiw, Romer and Weil (1992), 'A Contribution to the Empirics of Economic Growth', Table I (the unrestricted textbook Solow regression), Non-oil sample: in the 98 non-oil countries, estimate by ordinary least squares the regression of log GDP per working-age person in 1985 on the log of the investment share of GDP and the log of (n + g + δ), where n is the 1960-1985 growth rate of the working-age population and g + δ is assumed to be 0.05. Report the coefficient on the log of (n + g + δ).

Resposta

Estimativa (infer.ols): coef.lngd = -1,990.

Valores esperados e de onde vêm

mrw_1992.tab1_nonoil_lngd.software

Fonte
statsmodels 0.15.0: the reference specification of mrw_1992 tab1_nonoil_lngd (infer.ols, figure 'coef.lngd') on an independent code path
Tipo
software
Localização
mrw_1992: Table I (Estimation of the textbook Solow model), unrestricted regression, column 'Non-oil', row 'ln(n + g + δ)'
Valor
-1,98977-1.9897744846061867 (se 0,5633620.5633618948953987)
Conferido por
execução de software · sm.OLS(ly, add_constant(['lngd', 'ls'])).fit(cov_type='nonrobust') on growthdj (the file pinned at sha256 3d06303f1da9959175e990eb068bf242fac0e9bc1f9daad1f5fffe96d100e796, after its 7 declared transforms, rows where oil == 'no'); n_obs = 98. 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/
  • #5 figura da resposta próxima: -1,98977-1.9897744846061867 · relativa 0.000001 · absoluta 5.633618948953987e-7
  • #6 figura da resposta próxima: 0,5633620.5633618948953987 · relativa 0.000001 · absoluta 5.633618948953987e-7

mrw_1992.tab1_nonoil_lngd.paper

Fonte
Mankiw, N. G., Romer, D. and Weil, D. N. (1992). A Contribution to the Empirics of Economic Growth. Quarterly Journal of Economics 107(2): 407-437.
Tipo
paper
Localização
Table I (Estimation of the textbook Solow model), unrestricted regression, column 'Non-oil' (N = 98), row 'ln(n + g + δ)'
Página
414
Trecho
ln(n + g + δ) Non-oil −1.97 (0.56)
Valor como publicado
−1.97
Erro-padrão como publicado
0.56
Valor
-1,97-1.97 (se 0,560.56) elasticity of GDP per working-age person
Conferido por
fonte primária · Read on 2026-10-15 from the QJE copy on David Romer's Berkeley page (31-page PDF) with pdftotext -layout and from the rendered page image; Table I is on PDF page 8 (journal page 414). The note says 'Standard errors are in parentheses' and '(g + δ) is assumed to be 0.05'. Observations as printed: 98; the replication file has 98 complete rows in this sample.
sha256 da cópia lida
1bd560f2d4c6bc80f72557fff2f03e785b04aacd7f032c41ef210faaf830c80f (identifica a cópia lida, não um arquivo canônico)
Desvio conhecido
versão dos dados
Recorded before this track's software-reference run and before any dry run on this target; from the data's provenance. The file is Durlauf and Johnson's transcription of the MRW data appendix (R package AER, GrowthDJ), whose population growth and investment shares are printed rounded to one decimal of a percent, while MRW estimated on the unrounded Summers-Heston data; the research probe of 2026-10-14 (research-papers.md section 3.5) already found the constant off by about 0.05 and some slopes off by 0.01 to 0.02 on this file. Every MRW cell is therefore a data_version deviation: the printed cell is recorded and counted in replication fidelity, and non-required on T1.
Endereço
https://eml.berkeley.edu/~dromer/papers/MRW_QJE1992.pdf
Licença
Copyright the President and Fellows of Harvard College (QJE); cited, not reproduced.
  • #7 figura da resposta próxima: -1,97-1.97 · published_cell d=2 · absoluta 0.00500000197
  • #8 figura da resposta próxima: 0,560.56 · published_cell d=2 (the SE as printed) · absoluta 0.0050000010000000004

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_3c360132:coef.lngd (infer.ols)
obrigatóriapassou—"call_3c360132:coef.lngd"——call_3c360132
2ferramenta chamada
answer call is infer.ols
obrigatóriapassou—"infer.ols"——call_3c360132
3argumento igual
infer.ols.y = 'ly'
obrigatóriapassou"ly""ly"——call_3c360132
4diagnóstico
98 vs 98
obrigatóriapassou98989898default rel 1e-6 (abs floor 1e-6)—call_3c360132
5figura da resposta próxima
-1.989774485 vs -1.9897744846061867
obrigatóriapassou-1,98977-1.9897744846061867-1,98977-1.9897744846061867relativa 0.000001 · absoluta 5.633618948953987e-7mrw_1992.tab1_nonoil_lngd.softwarecall_3c360132
6figura da resposta próxima
0.5633618949 vs 0.5633618948953987
registradapassou0,5633620.56336189489539870,5633620.5633618948953983relativa 0.000001 · absoluta 5.633618948953987e-7mrw_1992.tab1_nonoil_lngd.softwarecall_3c360132
7figura da resposta próxima
-1.989774485 vs -1.97
registradafalhou-1,97-1.97-1,98977-1.9897744846061867published_cell d=2 · absoluta 0.00500000197mrw_1992.tab1_nonoil_lngd.papercall_3c360132
8figura da resposta próxima
0.5633618949 vs 0.56
registradapassou0,560.560,5633620.5633618948953983published_cell d=2 (the SE as printed) · absoluta 0.0050000010000000004mrw_1992.tab1_nonoil_lngd.papercall_3c360132

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.mrw_1992.tab1_nonoil_lngd.tool)

Referência: infer.olsChamada do agente: infer.ols (call_3c360132)igual
Argumentos da referência e do agente
ArgumentoReferênciaAgente
cluster——não fixado pela referência
controls[][]igual
dataset"growthdj""growthdj"igual
fe[][]igual
intercept—truenão fixado pela referência
level—0,950.95não fixado pela referência
se"classical""classical"igual
subset"oil == 'no'""oil == 'no'"igual
weights——não fixado pela referência
x["lngd","ls"]["lngd","ls"]igual
y"ly""ly"igual

Trajetória

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

  1. 0Início

    Mankiw, Romer and Weil (1992), 'A Contribution to the Empirics of Economic Growth', Table I (the unrestricted textbook Solow regression), Non-oil sample: in the 98 non-oil countries, estimate by ordinary least squares the regression of log GDP per working-age person in 1985 on the log of the investment share of GDP and the log of (n + g + δ), where n is the 1960-1985 growth rate of the working-age population and g + δ is assumed to be 0.05. Report the coefficient on the log of (n + g + δ).

    growthdj (agent)

    The data of Mankiw, Romer and Weil (1992), 'A Contribution to the Empirics of Economic Growth', as transcribed by Durlauf and Johnson (R package AER, GrowthDJ): one row per country (121 rows). Columns: rownames, a row number; oil, 'yes' for the oil producers MRW exclude from the non-oil sample, else 'no'; inter, 'yes' for the 'intermediate' sample (no Summers-Heston grade of D, 1960 population of at least one million); oecd, 'yes' for the OECD sample; gdp60 and gdp85, real GDP per working-age person in 1960 and 1985 (dollars); gdpgrowth, average annual growth of GDP per working-age person 1960-1985 (percent); popgrowth, average annual growth of the working-age population 1960-1985 (percent); invest, average share of real investment in real GDP 1960-1985 (percent); school, percent of the working-age population in secondary school; literacy60, adult literacy rate in 1960 (percent). Columns built from the paper's definitions (g + δ = 0.05): ly, log of gdp85; s_share, invest divided by 100; ls, log of s_share (ln(I/GDP)); n_rate, popgrowth divided by 100; g_plus_delta, the constant 0.05; ngd, n_rate + g_plus_delta; lngd, log of ngd (ln(n + g + δ)). Some countries miss gdp85 or popgrowth.

    file 3d06303f1da9959175e990eb068bf242fac0e9bc1f9daad1f5fffe96d100e796 · frame —
    1. derive {"args":{"column":"gdp85"},"name":"ly","op":"log"}
    2. derive {"args":{"column":"invest","factor":0.01},"name":"s_share","op":"scale"}
    3. derive {"args":{"column":"s_share"},"name":"ls","op":"log"}
    4. derive {"args":{"column":"popgrowth","factor":0.01},"name":"n_rate","op":"scale"}
    5. derive {"args":{"value":0.05},"name":"g_plus_delta","op":"constant"}
    6. derive {"args":{"columns":["n_rate","g_plus_delta"]},"name":"ngd","op":"sum"}
    7. derive {"args":{"column":"ngd"},"name":"lngd","op":"log"}
  2. 1Transformação
    growthdj · derive {"args":{"column":"gdp85"},"name":"ly","op":"log"}
    121 → 121 linhas
    Frame antes → depois: 6f822eca9c87 → 256189baba87
  3. 2Transformação
    growthdj · derive {"args":{"column":"invest","factor":0.01},"name":"s_share","op":"scale"}
    121 → 121 linhas
    Frame antes → depois: 256189baba87 → ddc60cb0ca4e
  4. 3Transformação
    growthdj · derive {"args":{"column":"s_share"},"name":"ls","op":"log"}
    121 → 121 linhas
    Frame antes → depois: ddc60cb0ca4e → 832bd9c4b876
  5. 4Transformação
    growthdj · derive {"args":{"column":"popgrowth","factor":0.01},"name":"n_rate","op":"scale"}
    121 → 121 linhas
    Frame antes → depois: 832bd9c4b876 → ff997d0e044f
  6. 5Transformação
    growthdj · derive {"args":{"value":0.05},"name":"g_plus_delta","op":"constant"}
    121 → 121 linhas
    Frame antes → depois: ff997d0e044f → b9e3acd5adf8
  7. 6Transformação
    growthdj · derive {"args":{"columns":["n_rate","g_plus_delta"]},"name":"ngd","op":"sum"}
    121 → 121 linhas
    Frame antes → depois: b9e3acd5adf8 → 837704298f36
  8. 7Transformação
    growthdj · derive {"args":{"column":"ngd"},"name":"lngd","op":"log"}
    121 → 121 linhas
    Frame antes → depois: 837704298f36 → e3f509733efe
  9. 8Turno do modelo
    call_dd214c6breferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache
    Texto visível

    Table I, Non-oil sample: OLS of log GDP per working-age person in 1985 on ln(I/GDP) and ln(n + g + δ) with g + δ = 0.05, classical errors.

    Chamadas pedidas
    • infer.ols
      {
        "controls": [],
        "dataset": "growthdj",
        "fe": [],
        "se": "classical",
        "subset": "oil == 'no'",
        "x": [
          "lngd",
          "ls"
        ],
        "y": "ly"
      }
  10. 9Ferramenta
    infer.olsv0.1.0call_3c360132decidido pelo modeloreexecutável

    Motivo: Table I, Non-oil sample: OLS of log GDP per working-age person in 1985 on ln(I/GDP) and ln(n + g + δ) with g + δ = 0.05, classical errors.

    Argumentos
    {
      "cluster": null,
      "controls": [],
      "dataset": "growthdj",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "classical",
      "subset": "oil == 'no'",
      "weights": null,
      "x": [
        "lngd",
        "ls"
      ],
      "y": "ly"
    }
    Figuras call_3c360132
    ChaveValorValor brutoErro-padrãoIC
    coef.lngd
    Coefficient on lngd in the regression of ly
    -1,98977 -1.98977448460618670,5633620.5633618948953983-3,10819 – -0,87136
    coef.ls
    Coefficient on ls in the regression of ly
    1,42401 1.4240142833694190,1431060.143105816497321,13991 – 1,70812
    Diagnósticos
    • n_obs = 9898
    • k = 33
    • r2 = 0,6008650.6008648011032094
    • df_resid = 9595
    • se_type = "classical"
    • n_clusters = —
    Tabelas (1)
    {
      "coefficients": [
        {
          "ci_high": 8.574302070149807,
          "ci_low": 2.285464121413495,
          "coef": 5.429883095781651,
          "p": 0.0008999523718049453,
          "se": 1.5838898803304136,
          "t": 3.4281948279440546,
          "term": "Intercept"
        },
        {
          "ci_high": -0.8713597174283383,
          "ci_low": -3.108189251784035,
          "coef": -1.9897744846061867,
          "p": 0.0006385352057009347,
          "se": 0.5633618948953983,
          "t": -3.531964981365373,
          "term": "lngd"
        },
        {
          "ci_high": 1.708115249178197,
          "ci_low": 1.1399133175606408,
          "coef": 1.424014283369419,
          "p": 2.104644525379704e-16,
          "se": 0.14310581649732,
          "t": 9.950778509384257,
          "term": "ls"
        }
      ]
    }
    Digests dos dados
    growthdj e3f509733efe06358f9503c526bd45111b6a64feb75b292419aae56055b598f2
    sha256 do resultado
    b90f0307e98b6927b4a61a13b99789240d822d653d9a90d349db43f0a8f51ebe
  11. 10Turno do modelo
    call_659833a6referenceFim: 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.lngd = -1,990.",
        "caveats": [],
        "figure_refs": [
          "call_3c360132:coef.lngd"
        ]
      }
  12. 11Guarda
    resposta aceita
    Números encontrados
    -1,990
    Sem lastro
    —
    Não citados
    —
    1 nova(s) tentativa(s) restante(s)
  13. 12Resposta

    Estimativa (infer.ols): coef.lngd = -1,990.

    Figuras citadas: call_3c360132:coef.lngd · Parada: answered
    Figura da resposta: call_3c360132:coef.lngd = -1,98977-1.9897744846061867 (se 0,5633620.5633618948953983)
  14. 13Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
  15. 14Pontuação
    #1figura da resposta escolhidapassouobrigatóriaanswer figure call_3c360132:coef.lngd (infer.ols)
  16. 15Pontuação
    #2ferramenta chamadapassouobrigatóriaanswer call is infer.ols
  17. 16Pontuação
    #3argumento igualpassouobrigatóriainfer.ols.y = 'ly'
  18. 17Pontuação
    #4diagnósticopassouobrigatória98 vs 98
  19. 18Pontuação
    #5figura da resposta próximapassouobrigatória-1.989774485 vs -1.9897744846061867
  20. 19Pontuação
    #6figura da resposta próximapassouregistrada0.5633618949 vs 0.5633618948953987
  21. 20Pontuação
    #7figura da resposta próximafalhouregistrada-1.989774485 vs -1.97
  22. 21Pontuação
    #8figura da resposta próximapassouregistrada0.5633618949 vs 0.56

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