Tarefa paper_repro.wooldridge_jtrain.ex14_1_grant.agent
paper_reproagentT2publicépoca 1, tentativa 1Execução: 20261015T183517Z-suite-paper_repro-gemini-0cda63Veredito
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.
The estimated fixed effects regression of the log scrap rate (`lscrap`) on the current job training grant indicator (`grant`), the lagged grant indicator (`grant_1`), and year indicators for 1988 and 1989 yields a coefficient on the current-year grant indicator of -0.252315. Key estimation details: - Point estimate: -0.252315 - Classical standard error (as reported in Wooldridge Example 14.1): 0.150629 (t = -1.67508, p = 0.0969239, 95% CI: [-0.551018, 0.0463881]) - Firm-clustered robust standard error: 0.176239 (95% CI: [-0.605806, 0.101177]) - Transformed percent effect: -22.3% (95% CI: [-42.3637%, 4.74808%]) - Sample size: 162 observations across 54 firms reporting scrap rates in all three years (1987–1989). For comparison, the coefficient on the lagged grant indicator (`grant_1`) is -0.42159 (classical se = 0.2102; cluster-robust se = 0.347049). The current-year grant effect is negative but not statistically significant at conventional levels (p = 0.0969239). Ressalvas: The estimation uses a balanced subpanel of 54 firms (162 observations) that reported scrap rates in all three years (1987-1989). A sign flip was observed between the pooled OLS slope (0.0543534) and the within-firm slope (-0.252315), demonstrating between-firm confounding (firms receiving grants had higher baseline scrap rates). Fixed effects control for time-invariant unobserved firm heterogeneity, but do not protect against time-varying unobservables correlated with both grant assignment and scrap rates. The effect of the current-year grant is not statistically significant at the 5% level under either classical or cluster-robust standard errors.
- The estimation uses a balanced subpanel of 54 firms (162 observations) that reported scrap rates in all three years (1987-1989).
- A sign flip was observed between the pooled OLS slope (0.0543534) and the within-firm slope (-0.252315), demonstrating between-firm confounding (firms receiving grants had higher baseline scrap rates).
- Fixed effects control for time-invariant unobserved firm heterogeneity, but do not protect against time-varying unobservables correlated with both grant assignment and scrap rates.
- The effect of the current-year grant is not statistically significant at the 5% level under either classical or cluster-robust standard errors.
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: 0,150629
0.15062899435450078· 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 conferidoRecorded 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: 0,151
0.151· published_cell d=3 (the SE as printed) · absoluta 0.000500001
Verificações
| # | Verificação | Obrigatória | Resultado | Esperado | Observado | Tolerância | Fonte | Chamada |
|---|---|---|---|---|---|---|---|---|
| 0 | resposta com lastro every number backed | obrigatória | passou | — | [] | — | — | — |
| 1 | figura da resposta escolhida answer figure call_63519219:coef.grant (infer.ols) | obrigatória | passou | — | "call_63519219:coef.grant" | — | — | call_63519219 |
| 2 | uma entre tool_called(infer.ols): ok | tool_called(infer.fe): answer call is infer.ols | obrigatória | passou | [{"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_63519219 |
| 3 | argumento igual infer.ols.y = 'lscrap' | obrigatória | passou | "lscrap" | "lscrap" | — | — | call_63519219 |
| 4 | argumento igual infer.ols.fe = ['fcode'] | obrigatória | passou | ["fcode"] | ["fcode"] | — | — | call_63519219 |
| 5 | argumento igual not applicable: the answer call is infer.ols, not infer.fe | obrigatória | passou | "lscrap" | — | — | — | call_63519219 |
| 6 | argumento igual not applicable: the answer call is infer.ols, not infer.fe | obrigatória | passou | "fcode" | — | — | — | call_63519219 |
| 7 | diagnóstico 162 vs 162 | obrigatória | passou | 162162 | 162162 | default rel 1e-6 (abs floor 1e-6) | — | call_63519219 |
| 8 | diagnóstico not applicable: the answer call is infer.ols, not infer.fe | obrigatória | passou | 162162 | — | default rel 1e-6 (abs floor 1e-6) | — | call_63519219 |
| 9 | figura da resposta próxima -0.2523148738 vs -0.25231487381477113 | obrigatória | passou | -0,252315-0.25231487381477113 | -0,252315-0.2523148738147904 | relativa 0.000001 · absoluta 1.5062899435450078e-7 | wooldridge_jtrain.ex14_1_grant.software | call_63519219 |
| 10 | figura da resposta próxima 0.1506289944 vs 0.15062899435450078 | registrada | passou | 0,1506290.15062899435450078 | 0,1506290.15062899435450128 | relativa 0.000001 · absoluta 1.5062899435450078e-7 | wooldridge_jtrain.ex14_1_grant.software | call_63519219 |
| 11 | figura da resposta próxima -0.2523148738 vs -0.252 | registrada | passou | -0,252-0.252 | -0,252315-0.2523148738147904 | published_cell d=3 · absoluta 0.000500001 | wooldridge_jtrain.ex14_1_grant.paper | call_63519219 |
| 12 | figura da resposta próxima 0.1506289944 vs 0.151 | registrada | passou | 0,1510.151 | 0,1506290.15062899435450128 | published_cell d=3 (the SE as printed) · absoluta 0.000500001 | wooldridge_jtrain.ex14_1_grant.paper | call_63519219 |
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)
| Argumento | Referência | Agente | |
|---|---|---|---|
| cluster | — | — | não fixado pela referência |
| controls | ["d88","d89"] | ["grant_1","d88","d89"] | diferente |
| dataset | "jtrain" | "jtrain" | igual |
| fe | ["fcode"] | ["fcode"] | igual |
| intercept | — | true | não fixado pela referência |
| level | — | 0,950.95 | não fixado pela referência |
| se | "classical" | "classical" | igual |
| subset | — | "scrap == scrap" | não fixado pela referência |
| weights | — | — | não fixado pela referência |
| x | ["grant","grant_1"] | ["grant"] | diferente |
| y | "lscrap" | "lscrap" | igual |
Trajetória
Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.
- 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.
file2820079b04c953a3555a3722e4b14430bc9efedfd6870715a863717de8177383· frame — - 1Turno do modelo
call_489c4c6dgemini-3.8-flashFim:STOP5.560 ms14725 de entrada · 20 de saída · 160 de raciocínio · 0 em cacheResumo do raciocínio, como declarado pelo modelo**My Thought Process** Alright, the user wants me to replicate Wooldridge's Example 14.1 from *Introductory Econometrics*. The core of this request is to estimate the effect of job training grants on firm scrap rates using a fixed-effects model. Specifically, they're interested in Michigan manufacturing firms between 1987 and 1989, and they want to use only firms that have scrap rate data for all three years. The model specified is a regression of the log of the scrap rate on: 1. A dummy variable indicating whether the firm received a grant in the current year. 2. A dummy variable indicating whether the firm received a grant in the previous year (lagged one year). 3. Year dummies for 1988 and 1989. The key output they're looking for is the estimated coefficient on the *current year's* grant indicator. Now, I need to think about how to execute this. I have access to various inference tools, like `infer__fe` and `infer__ols`. Since the request explicitly mentions "fixed effects" and "a separate intercept for each of the firms," this immediately tells me I need a panel data approach with firm-level fixed effects. The `panel-fixed-effects` skill seems perfectly suited for this task. I'll need to load that skill to proceed. Once loaded, I anticipate using a function within that skill that allows me to specify the dependent variable (log scrap rate), the independent variables (current grant, lagged grant, year dummies), and crucially, the firm identifier to absorb the fixed effects. I'll then extract the coefficient for the current grant indicator as requested.
Chamadas pedidasload_skill(meta-ferramenta){ "name": "panel-fixed-effects" }
- 2Habilidade
panel-fixed-effects - 3Turno do modelo
call_bd81983cgemini-3.8-flashFim:STOP6.711 ms15278 de entrada · 17 de saída · 350 de raciocínio · 0 em cacheResumo do raciocínio, como declarado pelo modelo**My Thought Process on Wooldridge Example 14.1 and the `jtrain` Dataset** Alright, so my primary objective here is to fully grasp the specifics of Wooldridge's Example 14.1, particularly regarding the data subset used and the estimation procedure. The instruction explicitly states: "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." This is a very precise instruction, and I need to ensure my execution aligns perfectly. First, I need to understand the structure of the `jtrain` dataset. I'm looking at the column descriptions: `fcode` (firm code), `year` (1987, 1988, 1989), `lscrap` (log of scrap rate), `grant` (grant indicator), `grant_1` (lagged grant indicator), and `d88`, `d89` (year dummies). This all looks standard for panel data analysis. Now, a crucial point arises from the instruction: "using the firms with scrap rates in all three years." This immediately triggers a question about how the fixed effects estimation function, let's assume it's something like `infer.fe`, handles missing values or unbalanced panels. Does it automatically subset to firms with complete data for the specified years, or do I need to perform that subsetting manually? Or, does `infer.fe` (or a similar function, or even `infer.ols` if that's what's being used) implicitly drop observations with missing values within the analysis, effectively creating a balanced panel for the selected variables? The phrase "firms with scrap rates in all three years" is key. Does this imply that the analysis is restricted to a *balanced panel* of 54 firms, yielding exactly 162 observations (54 firms * 3 years)? Or is it possible that the function handles the missing `lscrap` values in a way that keeps firms with data for at least some years, but only analyzes the complete observations? This distinction is critical for interpreting the results accurately. To clarify this, my next logical step should be to investigate the behavior of the estimation function regarding missing data. Before even running the regression, I need to verify how many firms actually have `lscrap` values present for *all three* years (1987, 1988, and 1989). I can achieve this by describing the `jtrain` dataset more thoroughly, perhaps by counting the non-missing `lscrap` values for each firm across the years, or by using a `data.describe` equivalent that can aggregate this information. This would confirm whether the dataset is indeed balanced for the purpose of this specific regression or if explicit subsetting is required by the command or by me. I should also check the documentation or common behavior of the `infer.fe` and `infer.ols` functions to understand their default handling of missing data in panel settings. Essentially, I need to be absolutely sure I'm working with the *exact* sample of firms and observations that Wooldridge intends.
Chamadas pedidasdata.describe{ "dataset": "jtrain" }
- 4Ferramenta
data.describev0.1.0call_51f3db87decidido pelo modeloreexecutávelArgumentos
{ "columns": null, "dataset": "jtrain" }Figuras call_51f3db87 Chave Valor Valor bruto Erro-padrão IC n_rows Rows in the dataset471 rows 471— — Tabelas (1)
{ "columns": [ { "column": "rownames", "dtype": "int64", "max": 471, "mean": 236, "min": 1, "n_missing": 0, "n_unique": 471, "sd": 136.11024943037904 }, { "column": "year", "dtype": "int64", "max": 1989, "mean": 1988, "min": 1987, "n_missing": 0, "n_unique": 3, "sd": 0.8173647327741629 }, { "column": "fcode", "dtype": "int64", "max": 419486, "mean": 415708.88535031845, "min": 410032, "n_missing": 0, "n_unique": 157, "sd": 4022.9218913961395 }, { "column": "employ", "dtype": "float64", "max": 525, "mean": 59.31590909090909, "min": 4, "n_missing": 31, "n_unique": 125, "sd": 74.12378197205925 }, { "column": "sales", "dtype": "float64", "max": 54000000, "mean": 6116036.53619303, "min": 110000, "n_missing": 98, "n_unique": 259, "sd": 7912517.304885553 }, { "column": "avgsal", "dtype": "float64", "max": 42583, "mean": 18872.82512315271, "min": 4237, "n_missing": 65, "n_unique": 230, "sd": 6703.156360369702 }, { "column": "scrap", "dtype": "float64", "max": 30, "mean": 3.8436419727901616, "min": 0.0099999997764825, "n_missing": 309, "n_unique": 100, "sd": 6.007769894302405 }, { "column": "rework", "dtype": "float64", "max": 40, "mean": 3.473983741766675, "min": 0, "n_missing": 348, "n_unique": 65, "sd": 5.462482732633984 }, { "column": "tothrs", "dtype": "float64", "max": 320, "mean": 29.2, "min": 0, "n_missing": 56, "n_unique": 66, "sd": 47.699755415166095 }, { "column": "union", "dtype": "int64", "max": 1, "mean": 0.19745222929936307, "min": 0, "n_missing": 0, "n_unique": 2, "sd": 0.3984996934651622 }, { "column": "grant", "dtype": "int64", "max": 1, "mean": 0.14012738853503184, "min": 0, "n_missing": 0, "n_unique": 2, "sd": 0.34748822835315846 }, { "column": "d89", "dtype": "int64", "max": 1, "mean": 0.3333333333333333, "min": 0, "n_missing": 0, "n_unique": 2, "sd": 0.47190574849326955 }, { "column": "d88", "dtype": "int64", "max": 1, "mean": 0.3333333333333333, "min": 0, "n_missing": 0, "n_unique": 2, "sd": 0.47190574849326955 }, { "column": "totrain", "dtype": "float64", "max": 350, "mean": 23.094623655913978, "min": 0, "n_missing": 6, "n_unique": 76, "sd": 45.17837817643876 }, { "column": "hrsemp", "dtype": "float64", "max": 163.9166717529297, "mean": 14.967538482242096, "min": 0, "n_missing": 81, "n_unique": 177, "sd": 25.7106427343588 }, { "column": "lscrap", "dtype": "float64", "max": 3.4011974334716797, "mean": 0.3936814002255783, "min": -4.605170249938965, "n_missing": 309, "n_unique": 100, "sd": 1.4864712658019084 }, { "column": "lemploy", "dtype": "float64", "max": 6.263398170471191, "mean": 3.531250030615113, "min": 1.3862943649291992, "n_missing": 31, "n_unique": 125, "sd": 1.0331803330025695 }, { "column": "lsales", "dtype": "float64", "max": 17.804494857788086, "mean": 15.028403136429773, "min": 11.608235359191896, "n_missing": 98, "n_unique": 259, "sd": 1.0977016417463359 }, { "column": "lrework", "dtype": "float64", "max": 3.6888794898986816, "mean": 0.16417518639860074, "min": -4.605170249938965, "n_missing": 350, "n_unique": 64, "sd": 1.7289226336299335 }, { "column": "lhrsemp", "dtype": "float64", "max": 5.105440139770508, "mean": 1.6499651404933477, "min": 0, "n_missing": 81, "n_unique": 177, "sd": 1.540141527292993 }, { "column": "lscrap_1", "dtype": "float64", "max": 3.4011974334716797, "mean": 0.5129374851196729, "min": -4.605170249938965, "n_missing": 363, "n_unique": 71, "sd": 1.5004967511737295 }, { "column": "grant_1", "dtype": "int64", "max": 1, "mean": 0.07643312101910828, "min": 0, "n_missing": 0, "n_unique": 2, "sd": 0.26597235355974663 }, { "column": "clscrap", "dtype": "float64", "max": 2.397895336151123, "mean": -0.22113240934494469, "min": -3.3141860961914062, "n_missing": 363, "n_unique": 82, "sd": 0.57924805294976 }, { "column": "cgrant", "dtype": "int64", "max": 1, "mean": 0.06369426751592357, "min": -1, "n_missing": 0, "n_unique": 3, "sd": 0.4614712180105874 }, { "column": "clemploy", "dtype": "float64", "max": 1.6739764213562012, "mean": 0.0820223931608529, "min": -0.9808292388916016, "n_missing": 181, "n_unique": 158, "sd": 0.25352428775474883 }, { "column": "clsales", "dtype": "float64", "max": 2.896700859069824, "mean": 0.11587311491674307, "min": -1.9828739166259768, "n_missing": 226, "n_unique": 204, "sd": 0.4389013967921567 }, { "column": "lavgsal", "dtype": "float64", "max": 10.659210205078123, "mean": 9.784860831763357, "min": 8.351611137390137, "n_missing": 65, "n_unique": 230, "sd": 0.35015666409437624 }, { "column": "clavgsal", "dtype": "float64", "max": 0.5689115524291992, "mean": 0.06025770094510765, "min": -0.4054651260375976, "n_missing": 204, "n_unique": 198, "sd": 0.07647106013304053 }, { "column": "cgrant_1", "dtype": "float64", "max": 1, "mean": 0.11464968152866242, "min": 0, "n_missing": 157, "n_unique": 2, "sd": 0.31910723840197863 }, { "column": "chrsemp", "dtype": "float64", "max": 142, "mean": 5.935912563602822, "min": -88.62255096435547, "n_missing": 220, "n_unique": 176, "sd": 26.618121698171095 }, { "column": "clhrsemp", "dtype": "float64", "max": 4.394449234008789, "mean": 0.5037017871340017, "min": -4.025351524353027, "n_missing": 220, "n_unique": 184, "sd": 1.604679026523171 } ] }- Digests dos dados
jtraince556c6a511400be9674bea971d3ba4cff3c7e1149b038c7ebbcf69c15dab73c- sha256 do resultado
b7f42678b929e0bf50a6f58e29cc22bae0d40284d0b748378aee58a31ad3660c
- 5Turno do modelo
call_b69bd807gemini-3.8-flashFim:STOP7.762 ms16163 de entrada · 49 de saída · 591 de raciocínio · 12633 em cacheResumo do raciocínio, como declarado pelo modelo**My Thought Process Regarding `infer.fe` and Wooldridge Example 14.1** I'm trying to understand how the `infer.fe` function handles missing data in a fixed effects estimation context, specifically by looking at Wooldridge's Example 14.1. My initial thought was to run `infer.fe` with `y="lscrap"`, `x=["grant", "grant_1"]`, `unit="fcode"`, and `time="year"`. However, I immediately ran into a snag: `infer.fe` doesn't have a `subset` parameter. This means I can't directly filter the data *before* passing it to the function in the way I might have initially planned. My focus then shifted to how `infer.fe` itself handles missing observations within its defined parameters. Specifically, I'm wondering if `infer.fe` automatically drops units (firms, in this case) or rows that have any missing values. This is crucial because it impacts the sample size and, consequently, the estimation results. My next thought is to consider a scenario: what happens if a firm has a missing `lscrap` value in one year (say, 1987), but has complete data for the subsequent years (1988 and 1989)? Does `infer.fe` keep those two non-missing observations for that firm, or does it discard the entire unit? This is where Wooldridge's Example 14.1 becomes my reference point. I then recalled the details of Wooldridge's Example 14.1 from Chapter 14 on Fixed Effects Estimation. The example states, "Using the 54 firms that reported scrap rates in all three years (N = 54, T = 3, so that N = 162), we obtain the fixed effects estimates...". This "all three years" condition is a key piece of information. This immediately sparked a question in my mind: how many non-missing rows does `lscrap` actually have in the dataset? I checked the `data.describe()` output and found that `scrap` (which I assume is `lscrap` or closely related) has `n_missing: 309`. The total number of rows in the dataset is 471. Therefore, 471 - 309 = 162 rows have non-missing values for `scrap`. This number, 162, is highly significant because it directly matches the calculation from Wooldridge: 54 firms * 3 years = 162 observations. This strongly suggests that the fixed effects estimation in Wooldridge's example was performed on *only* those observations where *all* required variables (`lscrap` in this case, and presumably the `grant` variables) were present for *all* time periods for each of the 54 firms. My next step is to verify this directly. I need to confirm if the 162 non-missing rows for `lscrap` indeed correspond precisely to the data from the 54 firms across all three years. This will definitively tell me how `infer.fe` is behaving and if it's implicitly performing the same type of data filtering as demonstrated in Wooldridge's example. The next logical action is to proceed with running `infer.fe` to observe its behavior firsthand.
Chamadas pedidasinfer.fe{ "dataset": "jtrain", "time": "year", "unit": "fcode", "x": [ "grant", "grant_1" ], "y": "lscrap" }
- 6Ferramenta
infer.fev0.1.0call_b65e9e83decidido pelo modeloreexecutávelArgumentos
{ "cluster": null, "dataset": "jtrain", "level": 0.95, "time": "year", "unit": "fcode", "x": [ "grant", "grant_1" ], "y": "lscrap" }Figuras call_b65e9e83 Chave Valor Valor bruto Erro-padrão IC coef.grant Within-unit effect of grant on lscrap-0,252315 -0.25231487381479040,176239 0.17623939428884114-0,605806 – 0,101177 coef.grant_1 Within-unit effect of grant_1 on lscrap-0,42159 -0.421589508529215340,347049 0.34704893021769306-1,11768 – 0,274502 Diagnósticosn_units= 5454n_obs= 162162k= 5858r2_within= 0,2010470.20104711932151642n_periods= 33within_sd.grant= 0,346530.34652967511454147within_sd.grant_1= 0,2804910.2804905492885021sign_flip=truese_type="cluster"n_clusters= 5454
Tabelas (1)
{ "pooled_vs_within": [ { "pooled": 0.05435340253707231, "pooled_se": 0.27119920884881854, "regressor": "grant", "sd": 0.3845513799504467, "within": -0.2523148738147904, "within_sd": 0.34652967511454147, "within_se": 0.17623939428884114 }, { "pooled": -0.2652102426968907, "pooled_se": 0.35290491652383604, "regressor": "grant_1", "sd": 0.3227560367273136, "within": -0.42158950852921534, "within_sd": 0.2804905492885021, "within_se": 0.34704893021769306 } ] }- 309 rows were dropped because one of 'lscrap', 'grant', 'grant_1', 'fcode', 'year' was missing
- Digests dos dados
jtraince556c6a511400be9674bea971d3ba4cff3c7e1149b038c7ebbcf69c15dab73c- sha256 do resultado
647d6436762f8ff4574617b91599fdeab6edd34433be78dc863a5296ba9428e9
- 7Turno do modelo
call_24383017gemini-3.8-flashFim:STOP6.243 ms17306 de entrada · 71 de saída · 375 de raciocínio · 12702 em cacheResumo do raciocínio, como declarado pelo modelo**My Thought Process Regarding the Fixed Effects Estimation of Job Training Grants on Firm Scrap Rates** As an expert in econometrics, I'm meticulously analyzing the provided `infer.fe` output and cross-referencing it with the specified problem from Wooldridge's *Introductory Econometrics* (Example 14.1). My primary objective is to accurately estimate the effect of job training grants on firm scrap rates using fixed effects and to report the coefficient on the current-year grant indicator. Initially, the `infer.fe` output presents a coefficient for `grant` of -0.252315 with a cluster-robust standard error of 0.176239, yielding a confidence interval of [-0.605806, 0.101177]. This output is informative, but it immediately prompts me to consider alternative estimation strategies and how they align with the nuances of the problem statement and Wooldridge's own presentation. I find myself questioning whether `infer.ols` with explicitly defined dummy variables for firm fixed effects would yield the same results. More importantly, I need to ascertain if Wooldridge's reported standard errors are classical or robust, as this is a critical distinction for interpretation and comparison. The problem statement specifically asks for a fixed effects estimation, which implies controlling for unobserved firm-specific heterogeneity. My recollection of Wooldridge's text is that the estimated equation, incorporating firm fixed effects (represented by $a_i$), is: log(scrap\_it) = -0.080 d88\_t - 0.247 d89\_t - 0.252 grant\_it - 0.422 grant\_{i,t-1} + a_i Crucially, Wooldridge reports the standard error for the `grant` coefficient (grant\_it) as 0.151. This is a **classical standard error**, which is a vital piece of information that the `infer.fe` output, by default, does not directly provide. The `infer.fe` output, as shown, is reporting a cluster-robust standard error. Therefore, my next step is to utilize `infer.ols` with the firm fixed effects specification (`fe=["fcode"]` or equivalent dummy variable approach) to explicitly compare the classical standard error reported by Wooldridge with the robust standard error generated by the `infer.fe` output. This comparison is essential to confirm that I am indeed replicating Wooldridge's results and to understand the implications of using different standard error estimators in this context. The goal is to provide a comprehensive summary that not only presents the coefficient but also addresses the nuances of its statistical significance as reported in the original source.
Chamadas pedidasinfer.ols{ "controls": [ "grant_1", "d88", "d89" ], "dataset": "jtrain", "fe": [ "fcode" ], "se": "classical", "subset": "scrap == scrap", "x": [ "grant" ], "y": "lscrap" }
- 8Ferramenta
infer.olsv0.1.0call_63519219decidido pelo modeloreexecutávelArgumentos
{ "cluster": null, "controls": [ "grant_1", "d88", "d89" ], "dataset": "jtrain", "fe": [ "fcode" ], "intercept": true, "level": 0.95, "se": "classical", "subset": "scrap == scrap", "weights": null, "x": [ "grant" ], "y": "lscrap" }Figuras call_63519219 Chave Valor Valor bruto Erro-padrão IC coef.grant Coefficient on grant in the regression of lscrap-0,252315 -0.25231487381479040,150629 0.15062899435450128-0,551018 – 0,0463881 Diagnósticosn_obs= 162162k= 5858r2= 0,9275720.9275719385145765df_resid= 104104se_type="classical"n_clusters= —fixed_effects="fcode (54 levels)"
Tabelas (1)
{ "coefficients": [ { "ci_high": -2.238474315441898, "ci_low": -3.413163621116533, "coef": -2.8258189682792154, "p": 7.1486794262917715e-16, "se": 0.2961843358954396, "t": -9.540744144135967, "term": "Intercept" }, { "ci_high": 0.04638807456669092, "ci_low": -0.5510178221962717, "coef": -0.2523148738147904, "p": 0.09692391893977838, "se": 0.15062899435450128, "t": -1.6750750736672526, "term": "grant" }, { "ci_high": -0.004755091021797275, "ci_low": -0.8384239260366334, "coef": -0.42158950852921534, "p": 0.04748974265972443, "se": 0.21019996441849426, "t": -2.005659276372942, "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": 0.016974077007711086, "ci_low": -0.5113796650806333, "coef": -0.24720279403646112, "p": 0.06633915572683136, "se": 0.13321829139189525, "t": -1.8556220129655594, "term": "d89" } ] }- Digests dos dados
jtraince556c6a511400be9674bea971d3ba4cff3c7e1149b038c7ebbcf69c15dab73c- sha256 do resultado
ba13a4db3ebf4dd24786553295a671c018c626d4926881a4d4c86c0b6623e79a
- 9Turno do modelo
call_c0b1c467gemini-3.8-flashFim:STOP16.541 ms18211 de entrada · 556 de saída · 1382 de raciocínio · 15898 em cacheResumo do raciocínio, como declarado pelo modelo**My Thought Process on Estimating the Impact of Job Training Grants on Firm Scrap Rates** Here's a breakdown of how I approached this analysis, mirroring the steps and considerations I went through. First, I meticulously examined the output from the `infer.ols` and `infer.fe` tools. My immediate focus was on the point estimates and standard errors for the 'grant' coefficient. I noted that both methods yielded the identical point estimate of -0.252315. This consistency in the point estimate is a good starting sign. I also observed the standard errors: 0.150629 for OLS with classical standard errors and 0.176239 for fixed effects with cluster-robust standard errors. The difference between these is expected, with the cluster-robust standard error being larger, reflecting potential within-firm correlation over time. Then, I turned my attention to the user's request. I confirmed it was in English and specifically asked for the "coefficient on the grant indicator for the current year." This is precisely what I had identified. A key constraint I recalled was Rule 7, which dictates the format and content of the final answer. I needed to ensure it was written in the language of the question (English), was concise, included the estimate with its uncertainty, the identification strategy, and potential limitations, and followed specific formatting for figure references and caveats. Regarding the language of the answer, the instruction "Write the answer in the language of the question (Portuguese: decimal comma)" was a bit of a cognitive fork. Since the user's question was unequivocally in English, I interpreted this to mean the *primary* language of the answer should be English, but I needed to be mindful of the decimal notation. In English, the standard is a decimal point. I decided to use decimal points but ensure clarity. Next, I systematically gathered the necessary figure references. I extracted the call IDs and the specific figure keys (`coef.grant`, `coef.grant_1`) from both `infer.fe` (call ID `call_b65e9e83`) and `infer.ols` (call ID `call_63519219`). I then precisely identified the requested coefficient: -0.252315. I also pulled the associated uncertainty measures: the classical standard error (0.150629) and the confidence interval from OLS, and the cluster-robust standard error (0.176239) and its corresponding confidence interval from fixed effects. As I was preparing the final answer, I meticulously reviewed the caveats that would limit my conclusion. * I noted that the sample was restricted to a balanced panel of 54 firms, resulting in 162 observations across the three years (1987-1989). This is a crucial detail about the scope of the analysis. * I recalled the pooled OLS result (a positive coefficient of 0.0543534), which starkly contrasted with the fixed-effects estimate. This immediately brought to mind the potential for Simpson's paradox or, more generally, confounding due to unobserved factors that correlate with both grant receipt and scrap rates, especially since firms receiving grants initially had higher scrap rates. * I recognized that while fixed effects control for time-invariant firm-specific unobservables, time-varying factors that are correlated with both grant decisions and scrap rates could still introduce bias. This is a standard limitation in panel data analysis. * Crucially, I observed that the coefficient on the current-year grant was not statistically significant at the 5% level. The p-value from the classical OLS was 0.0969, and the confidence interval for the fixed-effects estimate crossed zero, reinforcing the lack of statistically significant evidence at conventional levels. Finally, I performed a comprehensive double-check against all instructions, ensuring that every number I intended to use in the final answer was directly from the tool outputs or the original question, and that all required components, including figure references and caveats, were present and correctly formatted. This thorough review was to ensure adherence to the strict output guidelines. The plan was then to use `submit_answer` to generate the final output.
Chamadas pedidassubmit_answer(meta-ferramenta){ "answer": "The estimated fixed effects regression of the log scrap rate (`lscrap`) on the current job training grant indicator (`grant`), the lagged grant indicator (`grant_1`), and year indicators for 1988 and 1989 yields a coefficient on the current-year grant indicator of -0.252315.\n\nKey estimation details:\n- Point estimate: -0.252315\n- Classical standard error (as reported in Wooldridge Example 14.1): 0.150629 (t = -1.67508, p = 0.0969239, 95% CI: [-0.551018, 0.0463881])\n- Firm-clustered robust standard error: 0.176239 (95% CI: [-0.605806, 0.101177])\n- Sample size: 162 observations across 54 firms observed in all 3 years (1987–1989).\n\nBy comparison, the lagged grant coefficient is -0.42159 (classical se = 0.2102; cluster-robust se = 0.347049). The current-year grant effect indicates approximately a 25.2% reduction in scrap rates, but is not statistically significant at the 5% level (p = 0.0969239).", "caveats": [ "The analysis is restricted to the balanced subpanel of 54 firms (162 observations) that reported scrap rates in all three years (1987-1989).", "A sign flip was detected between pooled OLS (0.0543534) and within-firm estimation (-0.252315), demonstrating between-firm confounding (firms receiving grants had higher baseline scrap rates).", "While firm fixed effects absorb time-invariant unobserved firm heterogeneity, they do not control for time-varying shocks that may be correlated with both grant award timing and scrap rates.", "The current-year grant effect is not statistically distinguishable from zero at the 5% significance level under either classical or cluster-robust standard errors." ], "figure_refs": [ "call_63519219:coef.grant", "call_b65e9e83:coef.grant" ] }
- 10Guardaresposta recusada
- Números encontrados
- 1988 · 1989 · -0.252315 · -0.252315 · 14.1 · 0.150629 · -1.67508 · 0.0969239 · 95% · -0.551018 · 0.0463881 · 0.176239 · 95% · -0.605806 · 0.101177 · 162 · 54 · 3 · 1987 · 1989 · -0.42159 · 0.2102 · 0.347049 · 25.2% · 5% · 0.0969239 · 54 · 162 · 1987 · 1989 · 0.0543534 · -0.252315 · 5%
- Sem lastro
- 25.2%
- Não citados
- —
0 nova(s) tentativa(s) restante(s) - 11Turno do modelo
call_f91ed606gemini-3.8-flashFim:STOP4.037 ms20234 de entrada · 43 de saída · 138 de raciocínio · 16227 em cacheResumo do raciocínio, como declarado pelo modelo**Aha! A Misstep in Numerical Representation** I've just encountered an error message that immediately flags a critical oversight in my execution: "Numbers no tool call stated: 25.2%." This isn't just a minor typo; it's a direct violation of Rule 1, which is fundamental to my operation. The rule is explicit: "You never compute a number. Every number in your answer must have been stated by a tool call in this run... No mental arithmetic: for a ratio, difference, sum, product, percent change or share of figures already computed, call calc.combine; to restate a log-point coefficient as a percent or an effect in standard deviations, call core.scale_effect." Looking at the offending output, "25.2%", I can see exactly where I went wrong. I presented this percentage without an accompanying, explicit tool call to justify its origin. My internal process must have been to take a previously generated coefficient, presumably `coef.grant` given the context, and directly convert it to a percentage myself. This is precisely what Rule 1 forbids – it prohibits any form of mental arithmetic or direct conversion of raw data into a numerical output without the express instruction of a designated tool. The correct procedure, as dictated by the rule, is clear: if I need to restate a log-point coefficient as a percentage, I *must* invoke `core.scale_effect`. Therefore, my immediate next step, and the correction to this error, is to ensure that the transformation of `coef.grant` into its percentage representation is handled by calling `core.scale_effect`. This ensures that all numerical figures in my output are verifiably sourced and accounted for by a specific tool execution.
Chamadas pedidascore.scale_effect{ "coefficient": "call_63519219:coef.grant", "transform": "log_points_to_percent" }
- 12Ferramenta
core.scale_effectv0.1.0call_3b83ae6fdecidido pelo modeloreexecutávelArgumentos
{ "baseline": null, "coefficient": "call_63519219:coef.grant", "sd": null, "transform": "log_points_to_percent" }Figuras call_3b83ae6f Chave Valor Valor bruto Erro-padrão IC effect Effect as a percent change (from log points): Coefficient on grant in the regression of lscrap-22,3 % -22.299995742063256— -42,3637 – 4,74808 Diagnósticostransform="log_points_to_percent"
- the standard error is not carried: a percent effect is a nonlinear function of b, so only the interval, transformed endpoint by endpoint, keeps its meaning
- Digests dos dados
- sha256 do resultado
8d469698639f0cb397bcf27ac04edbc444c8473c55d4baea970967b17709df1a
- 13Turno do modelo
call_a4a34511gemini-3.8-flashFim:STOP11.359 ms20529 de entrada · 617 de saída · 835 de raciocínio · 16231 em cacheResumo do raciocínio, como declarado pelo modelo**My Thought Process in Analyzing the Impact of Grant Funding** I've been tasked with evaluating the percentage effect of grant funding, and the initial result I've arrived at is a -22.3% effect, with a confidence interval spanning from -42.3637% to 4.74808%. My next critical step is to rigorously verify every single numerical output by ensuring it's directly attributable to a tool call. I need to be absolutely certain that no extraneous or unbacked numbers have crept into my analysis. Here's how I'm breaking down the verification process: First, I'm cross-referencing the coefficients for "grant." I see `-0.252315` appearing twice, associated with `call_63519219:coef.grant` and `call_b65e9e83:coef.grant`. This consistency is good. I also have `0.150629` from the `call_63519219` table, and `-1.67508` with its associated standard error of `0.0969239`, both from the `call_63519219` table. Then there's the confidence interval `[-0.551018, 0.0463881]` linked to `call_63519219:coef.grant` CI. Moving on to the second grant coefficient, I have `0.176239` as the standard error for `call_b65e9e83:coef.grant`, and its confidence interval is `[-0.605806, 0.101177]`. I'm also noting the number of observations, `162`, which is common to both `call_63519219` and `call_b65e9e83` for `n_obs`. For `call_b65e9e83`, I see `54` for `n_units`. The years `1987, 1988, 1989` are clearly from the original question context. Further, I've identified `-0.42159` from `call_b65e9e83:coef.grant_1`. From the `call_63519219` table, I have `0.2102`, and from `call_b65e9e83:coef.grant_1` SE, I have `0.347049`. The core reported effect, `-22.3%`, is directly from `call_3b83ae6f:effect`. Its confidence interval, `[-42.3637%, 4.74808%]`, is also from `call_3b83ae6f:effect` CI. Finally, I've captured `0.0543534` from the `call_b65e9e83` pooled table. My comprehensive review confirms that all these numbers are traceable to specific tool calls or the question's context, leaving me confident in the integrity of the data used for the final submission. With this rigorous double-check complete, I am ready to proceed with `submit_answer`.
Chamadas pedidassubmit_answer(meta-ferramenta){ "answer": "The estimated fixed effects regression of the log scrap rate (`lscrap`) on the current job training grant indicator (`grant`), the lagged grant indicator (`grant_1`), and year indicators for 1988 and 1989 yields a coefficient on the current-year grant indicator of -0.252315.\n\nKey estimation details:\n- Point estimate: -0.252315\n- Classical standard error (as reported in Wooldridge Example 14.1): 0.150629 (t = -1.67508, p = 0.0969239, 95% CI: [-0.551018, 0.0463881])\n- Firm-clustered robust standard error: 0.176239 (95% CI: [-0.605806, 0.101177])\n- Transformed percent effect: -22.3% (95% CI: [-42.3637%, 4.74808%])\n- Sample size: 162 observations across 54 firms reporting scrap rates in all three years (1987–1989).\n\nFor comparison, the coefficient on the lagged grant indicator (`grant_1`) is -0.42159 (classical se = 0.2102; cluster-robust se = 0.347049). The current-year grant effect is negative but not statistically significant at conventional levels (p = 0.0969239).", "caveats": [ "The estimation uses a balanced subpanel of 54 firms (162 observations) that reported scrap rates in all three years (1987-1989).", "A sign flip was observed between the pooled OLS slope (0.0543534) and the within-firm slope (-0.252315), demonstrating between-firm confounding (firms receiving grants had higher baseline scrap rates).", "Fixed effects control for time-invariant unobserved firm heterogeneity, but do not protect against time-varying unobservables correlated with both grant assignment and scrap rates.", "The effect of the current-year grant is not statistically significant at the 5% level under either classical or cluster-robust standard errors." ], "figure_refs": [ "call_3b83ae6f:effect", "call_63519219:coef.grant", "call_b65e9e83:coef.grant", "call_b65e9e83:coef.grant_1" ] }
- 14Guardaresposta aceita
- Números encontrados
- 1988 · 1989 · -0.252315 · -0.252315 · 14.1 · 0.150629 · -1.67508 · 0.0969239 · 95% · -0.551018 · 0.0463881 · 0.176239 · 95% · -0.605806 · 0.101177 · -22.3% · 95% · -42.3637% · 4.74808% · 162 · 54 · 1987 · 1989 · -0.42159 · 0.2102 · 0.347049 · 0.0969239 · 54 · 162 · 1987 · 1989 · 0.0543534 · -0.252315 · 5%
- Sem lastro
- —
- Não citados
- —
0 nova(s) tentativa(s) restante(s) - 15Resposta
The estimated fixed effects regression of the log scrap rate (`lscrap`) on the current job training grant indicator (`grant`), the lagged grant indicator (`grant_1`), and year indicators for 1988 and 1989 yields a coefficient on the current-year grant indicator of -0.252315. Key estimation details: - Point estimate: -0.252315 - Classical standard error (as reported in Wooldridge Example 14.1): 0.150629 (t = -1.67508, p = 0.0969239, 95% CI: [-0.551018, 0.0463881]) - Firm-clustered robust standard error: 0.176239 (95% CI: [-0.605806, 0.101177]) - Transformed percent effect: -22.3% (95% CI: [-42.3637%, 4.74808%]) - Sample size: 162 observations across 54 firms reporting scrap rates in all three years (1987–1989). For comparison, the coefficient on the lagged grant indicator (`grant_1`) is -0.42159 (classical se = 0.2102; cluster-robust se = 0.347049). The current-year grant effect is negative but not statistically significant at conventional levels (p = 0.0969239). Ressalvas: The estimation uses a balanced subpanel of 54 firms (162 observations) that reported scrap rates in all three years (1987-1989). A sign flip was observed between the pooled OLS slope (0.0543534) and the within-firm slope (-0.252315), demonstrating between-firm confounding (firms receiving grants had higher baseline scrap rates). Fixed effects control for time-invariant unobserved firm heterogeneity, but do not protect against time-varying unobservables correlated with both grant assignment and scrap rates. The effect of the current-year grant is not statistically significant at the 5% level under either classical or cluster-robust standard errors.
- The estimation uses a balanced subpanel of 54 firms (162 observations) that reported scrap rates in all three years (1987-1989).
- A sign flip was observed between the pooled OLS slope (0.0543534) and the within-firm slope (-0.252315), demonstrating between-firm confounding (firms receiving grants had higher baseline scrap rates).
- Fixed effects control for time-invariant unobserved firm heterogeneity, but do not protect against time-varying unobservables correlated with both grant assignment and scrap rates.
- The effect of the current-year grant is not statistically significant at the 5% level under either classical or cluster-robust standard errors.
Figuras citadas:call_3b83ae6f:effect, call_63519219:coef.grant, call_b65e9e83:coef.grant, call_b65e9e83:coef.grant_1· Parada:answeredFigura da resposta:call_63519219:coef.grant= -0,252315-0.2523148738147904(se 0,1506290.15062899435450128) - 16Pontuação#0resposta com lastropassouobrigatória
every number backed - 17Pontuação#1figura da resposta escolhidapassouobrigatória
answer figure call_63519219:coef.grant (infer.ols) - 18Pontuação#2uma entrepassouobrigatória
tool_called(infer.ols): ok | tool_called(infer.fe): answer call is infer.ols - 19Pontuação#3argumento igualpassouobrigatória
infer.ols.y = 'lscrap' - 20Pontuação#4argumento igualpassouobrigatória
infer.ols.fe = ['fcode'] - 21Pontuação#5argumento igualpassouobrigatória
not applicable: the answer call is infer.ols, not infer.fe - 22Pontuação#6argumento igualpassouobrigatória
not applicable: the answer call is infer.ols, not infer.fe - 23Pontuação#7diagnósticopassouobrigatória
162 vs 162 - 24Pontuação#8diagnósticopassouobrigatória
not applicable: the answer call is infer.ols, not infer.fe - 25Pontuação#9figura da resposta próximapassouobrigatória
-0.2523148738 vs -0.25231487381477113 - 26Pontuação#10figura da resposta próximapassouregistrada
0.1506289944 vs 0.15062899435450078 - 27Pontuação#11figura da resposta próximapassouregistrada
-0.2523148738 vs -0.252 - 28Pontuação#12figura da resposta próximapassouregistrada
0.1506289944 vs 0.151
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Downloads desta tarefa
63a9a06e437733aaea2da0f5d88530716585dfbf5d0240b8b4192b818f140d08O hash de um zip é só de transporte: o que ancora a integridade é o RUN_SHA256SUMS que vai dentro dele, cópia exata do SHA256SUMS da execução.