Tarefa paper_repro.kiel_mcclain_1995.ex13_3_rprice.agent

Veredito

Pergunta feita ao analista

Wooldridge, Introductory Econometrics, Example 13.3 (the effect of a garbage incinerator's location on housing prices, with the data of Kiel and McClain 1995 on house sales in North Andover, Massachusetts, in 1978, before any rumours of the incinerator, and in 1981, when its construction began): pooling the two years, estimate by ordinary least squares a regression of the real house price (in 1978 dollars) on a dummy for 1981, a dummy for houses near the incinerator site, and the interaction of the two dummies. Report the coefficient on the interaction, the difference-in-differences estimate of the incinerator's effect on the price of nearby houses.

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Trajetória

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

1. 0Início

   Wooldridge, Introductory Econometrics, Example 13.3 (the effect of a garbage incinerator's location on housing prices, with the data of Kiel and McClain 1995 on house sales in North Andover, Massachusetts, in 1978, before any rumours of the incinerator, and in 1981, when its construction began): pooling the two years, estimate by ordinary least squares a regression of the real house price (in 1978 dollars) on a dummy for 1981, a dummy for houses near the incinerator site, and the interaction of the two dummies. Report the coefficient on the interaction, the difference-in-differences estimate of the incinerator's effect on the price of nearby houses.

   kielmc (agent)

   Wooldridge's KIELMC data (from Kiel and McClain 1995): sales of houses in North Andover, Massachusetts, in 1978, before any rumours of a new garbage incinerator, and in 1981, when its construction began; one row per sale. Columns: rownames, the row number; year, 1978 or 1981; y81, 1 for a 1981 sale; price, the selling price in dollars; rprice, the price in 1978 dollars (real price); lprice and lrprice, their natural logs; nearinc, 1 if the house is within 3 miles (15,840 feet) of the incinerator site; y81nrinc, y81 times nearinc; dist, distance from the incinerator site in feet, and ldist its log; y81ldist, y81 times ldist; age, the age of the house in years, and agesq its square; rooms, number of rooms; baths, number of bathrooms; area, square footage of the house, and larea its log; land, square footage of the lot, and lland its log; intst, distance from the interstate in feet, lintst its log and lintstsq the square of that log; cbd, distance from the central business district in feet; nbh, the neighbourhood (1 to 6); wind, the percentage of time the wind blows from the incinerator toward the house.

   file 1ad63b59aff51c713725147032dd5803f3dbfc97163eabcde3b5f731ebbf03e0 · frame —
2. 1Turno do modelo
   call_a6efee69gemini-3.8-flashFim: STOP9.280 ms14752 de entrada · 22 de saída · 459 de raciocínio · 0 em cache

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process on Replicating Wooldridge's Difference-in-Differences Example** Right, so the core task here is to replicate Example 13.3 from Wooldridge's *Introductory Econometrics*. The goal is to estimate the causal effect of an incinerator on real house prices using a difference-in-differences (DiD) approach. Specifically, I need to: 1. **Pool the data** from the two years available. 2. **Estimate a regression** using Ordinary Least Squares (OLS). 3. The regression model should include: * A **dummy variable for the year 1981** (`y81`). * A **dummy variable for houses located near the incinerator site** (`nearinc`). * The **interaction term** between the year dummy and the proximity dummy (`y81nrinc`). * The dependent variable is the **real house price in 1978 dollars** (`rprice`). 4. **Report the coefficient on the interaction term**, which represents the difference-in-differences estimate of the incinerator's effect on nearby houses. First, I need to confirm the available dataset and its relevant variables. The prompt mentions the `kielmc` dataset, and I've identified the following columns as crucial: * `rprice`: The real house price (in 1978 dollars) – this will be my dependent variable. * `y81`: A dummy variable indicating if the sale occurred in 1981 (the "post" period). * `nearinc`: A dummy variable indicating if the house is within 3 miles of the incinerator site (the "treated" group). * `y81nrinc`: This is explicitly the interaction term (`y81 * nearinc`), which is exactly what I need for the DiD model. Now, I need to figure out the best tool within the `infer` library to perform this analysis. I'm considering a couple of options: * **`infer.did_2x2`**: This function seems purpose-built for a standard 2x2 DiD setup. I'd likely call it like `infer.did_2x2(dataset='kielmc', y='rprice', treated='nearinc', post='y81')`. I need to be sure this function uses the correct estimation method and reports the interaction term coefficient as the DiD estimate. * **`infer.ols`**: This is a more general OLS regression function. I can certainly use it to estimate the full model as specified by Wooldridge, including the interaction term: `rprice = beta_0 + delta_0*y81 + beta_1*nearinc + delta_1*(y81*nearinc)`. A key consideration is the standard errors. Wooldridge's example likely uses classical standard errors. I'll need to check if `infer.ols` supports `se='classical'` (or a similar option) to ensure comparability. My initial thought was to go straight for `infer.did_2x2` because it's specialized. However, I need to scrutinize what `infer.did_2x2` *actually* computes internally. Does it simply estimate the OLS model with the interaction term, or does it use a different estimation framework (like group-period means, which would be a bit different)? I should probably inspect the documentation or even the source code of `infer.did_2x2` to be absolutely certain it aligns with the OLS regression Wooldridge presents. On the other hand, `infer.ols` provides more granular control. By explicitly specifying all the regressors (including the interaction `y81nrinc`), I can be sure I'm directly replicating the OLS equation. The ability to specify `se='classical'` in `infer.ols` is particularly important if Wooldridge's example relies on those specific standard errors. I'm also pondering if there's a `skill` function related to "differences-in-differences" that might offer another perspective or confirm the results. It's good practice to cross-reference if available. Ultimately, my decision hinges on: 1. **The exact estimation method Wooldridge uses:** Is it purely OLS of the specified regression, or something else? My initial reading suggests it's the OLS regression with the interaction. 2. **The reporting of the interaction coefficient:** Both `infer.did_2x2` and `infer.ols` should ideally provide this. 3. **Standard error type:** If Wooldridge uses classical SEs, `infer.ols` with `se='classical'` seems like the safest bet for a direct replication. For now, I'm leaning towards using `infer.ols` because it offers explicit control over the model specification and standard errors, making it easier to verify that I'm precisely matching Wooldridge's approach. I'll then confirm that the coefficient on the interaction term (`y81nrinc`) is indeed the difference-in-differences estimate of the incinerator's impact.

   Chamadas pedidas

   • load_skill (meta-ferramenta)

     {
       "name": "differences-in-differences"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
3. 2Habilidade
   differences-in-differencesTexto da habilidade
4. 3Turno do modelo
   call_ffb2ec7fgemini-3.8-flashFim: STOP6.857 ms15792 de entrada · 99 de saída · 315 de raciocínio · 0 em cache

   Texto visível

   I will estimate the regression of real house prices on the 1981 indicator, the indicator for proximity to the incinerator, and their interaction using OLS to replicate Wooldridge's Example 13.3.

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process on Estimating the Incinerator's Effect on House Prices** Alright, the task at hand is clear: I need to estimate the causal effect of the incinerator on the price of nearby houses. The user has specified a particular methodology, and I need to follow it precisely. First, they've asked me to pool the two years of data. Then, they want me to perform an Ordinary Least Squares (OLS) regression. The dependent variable is the real house price, adjusted to 1978 dollars. The independent variables are quite specific: a dummy variable for the year 1981, another dummy variable for houses located near the incinerator site, and crucially, the interaction term between these two dummies. The ultimate goal here is to report the coefficient on this interaction term. This coefficient, as the user correctly points out, will represent the difference-in-differences (DID) estimate of the incinerator's effect on the price of houses situated near it. This is a standard approach for estimating the impact of a policy or event when we have data before and after the event, and a control group. To accomplish this, I'll need to utilize specific econometric tools. I'll start by running an OLS regression using the `infer.ols` function. The dataset is `kielmc`, and the variables are `rprice` as the outcome, and `y81`, `nearinc`, and `y81nrinc` (which I understand to be the interaction term `y81 * nearinc`) as regressors. However, before I just blindly run the code, a little detail catches my attention. I need to recall what standard errors Wooldridge reports in Example 13.3 of his "Introductory Econometrics." The typical textbook presentation, especially in that example, uses classical standard errors. Given that this is a standard reference point, it's important for me to align my output with that expectation. So, when I run `infer.ols`, I should ensure I'm generating classical standard errors. After running the OLS, I also need to specifically use the `infer.did_2x2` function. This function is designed to directly compute the DID estimator, which should yield the same result as the interaction coefficient from the pooled OLS, but it's good practice to run both to confirm and to directly address the user's request for the DID estimate. So, I'll execute `infer.did_2x2` on the `kielmc` data with the appropriate treatment and time variables. In summary, my plan is to: 1. Pool the data. 2. Perform an OLS regression with `rprice` as the dependent variable and `y81`, `nearinc`, and their interaction (`y81nrinc`) as independent variables, using classical standard errors. 3. Use the `infer.did_2x2` function for a direct DID estimation. 4. Report the coefficient on the interaction term from the OLS, which is my difference-in-differences estimate.

   Chamadas pedidas

   • infer.ols

     {
       "dataset": "kielmc",
       "se": "classical",
       "x": [
         "y81",
         "nearinc",
         "y81nrinc"
       ],
       "y": "rprice"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
5. 4Ferramenta
   infer.olsv0.1.0call_f4e0a289decidido pelo modeloreexecutável

   Motivo: I will estimate the regression of real house prices on the 1981 indicator, the indicator for proximity to the incinerator, and their interaction using OLS to replicate Wooldridge's Example 13.3.

   Argumentos

   {
     "cluster": null,
     "controls": [],
     "dataset": "kielmc",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "classical",
     "subset": null,
     "weights": null,
     "x": [
       "y81",
       "nearinc",
       "y81nrinc"
     ],
     "y": "rprice"
   }

   Figuras call_f4e0a289

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.y81 Coefficient on y81 in the regression of rprice	18.790,3	18790.28595300544	4.050,064050.0649593130915	10.821,9 – 26.758,7
   coef.nearinc Coefficient on nearinc in the regression of rprice	-18.824,4	-18824.37049941928	4.875,324875.32214561876	-28.416,4 – -9.232,29
   coef.y81nrinc Coefficient on y81nrinc in the regression of rprice	-11.863,9	-11863.903252112583	7.456,657456.646173258857	-26.534,7 – 2.806,87

   Diagnósticos

   • n_obs = 321321
   • k = 44
   • r2 = 0,1739480.1739483015349974
   • df_resid = 317317
   • se_type = "classical"
   • n_clusters = —

   Tabelas (1)

   {
     "coefficients": [
       {
         "ci_high": 87882.356827585,
         "ci_low": 77152.09845696785,
         "coef": 82517.22764227643,
         "p": 1.709245828665835e-95,
         "se": 2726.9100704846505,
         "t": 30.260340645414367,
         "term": "Intercept"
       },
       {
         "ci_high": 26758.69017388404,
         "ci_low": 10821.881732126845,
         "coef": 18790.28595300544,
         "p": 0.000005116892165044555,
         "se": 4050.0649593130915,
         "t": 4.639502363979949,
         "term": "y81"
       },
       {
         "ci_high": -9232.292852872026,
         "ci_low": -28416.448145966537,
         "coef": -18824.37049941928,
         "p": 0.00013680172198592228,
         "se": 4875.32214561876,
         "t": -3.861154183695517,
         "term": "nearinc"
       },
       {
         "ci_high": 2806.8665842980354,
         "ci_low": -26534.673088523203,
         "coef": -11863.903252112583,
         "p": 0.11259484069905487,
         "se": 7456.646173258857,
         "t": -1.5910508526821483,
         "term": "y81nrinc"
       }
     ]
   }

   Digests dos dados

   kielmc 6d002733bb822a80945b4cbdca611d8de467352d3edc1966d8ac266fca8a1d94

   sha256 do resultado

   f155c65d1a5ad3cd71594c695baa5ef32826a6e599c549ebb9b83694d7720628

   Resultado mostrado ao modelo
6. 5Turno do modelo
   call_12069e34gemini-3.8-flashFim: STOP3.094 ms16697 de entrada · 79 de saída · 37 de raciocínio · 12766 em cache

   Texto visível

   I will estimate the difference-in-differences using infer.did_2x2 to verify the 2x2 group-period means and the DiD estimate.

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process** Right, so, my next step in this analysis is to run `infer.did_2x2`. I'm doing this to specifically see what that particular function, `did_2x2`, outputs. My expectation is that it will provide me with a set of 2x2 means, and likely other related summary statistics, which I'll then need to interpret in the context of the larger investigation. It's a targeted extraction of specific data points that I anticipate will be crucial for understanding the relationships or effects I'm examining at a pairwise level.

   Chamadas pedidas

   • infer.did_2x2

     {
       "dataset": "kielmc",
       "post": "y81",
       "treated": "nearinc",
       "y": "rprice"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
7. 6Ferramenta
   infer.did_2x2v0.1.0call_051d96fedecidido pelo modeloreexecutável

   Motivo: I will estimate the difference-in-differences using infer.did_2x2 to verify the 2x2 group-period means and the DiD estimate.

   Argumentos

   {
     "cluster": null,
     "controls": [],
     "dataset": "kielmc",
     "level": 0.95,
     "post": "y81",
     "treated": "nearinc",
     "y": "rprice"
   }

   Figuras call_051d96fe

   Chave	Valor	Valor bruto	Erro-padrão	IC
   did Difference-in-differences effect on rprice	-11.863,9	-11863.903252112583	8.635,598635.585271746188	-28.854,2 – 5.126,4

   Diagnósticos

   • design = "stacked"
   • n_obs = 321321
   • se_type = "HC1"
   • parallel_trends = "untestable"
   • n_clusters = —

   Tabelas (1)

   {
     "means": [
       {
         "group": "treated",
         "mean": 63692.857142857145,
         "n": 56,
         "period": "before"
       },
       {
         "group": "treated",
         "mean": 70619.23984375,
         "n": 40,
         "period": "after"
       },
       {
         "group": "comparison",
         "mean": 82517.22764227643,
         "n": 123,
         "period": "before"
       },
       {
         "group": "comparison",
         "mean": 101307.51359528187,
         "n": 102,
         "period": "after"
       }
     ]
   }

   • with two periods parallel trends cannot be tested

   Digests dos dados

   kielmc 6d002733bb822a80945b4cbdca611d8de467352d3edc1966d8ac266fca8a1d94

   sha256 do resultado

   e9b75a5ed896dfe381c9756004c925d6e39e9f4d764ef873a75b9dd06cdc44da

   Resultado mostrado ao modelo
8. 7Turno do modelo
   call_b92af761gemini-3.8-flashFim: STOP18.000 ms17028 de entrada · 577 de saída · 1359 de raciocínio · 12720 em cache

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process on Analyzing the Incinerator's Effect on Housing Prices** My primary goal here is to analyze the impact of a garbage incinerator on housing prices, specifically focusing on the difference-in-differences (DID) estimate, as requested by Wooldridge's Example 13.3. I'm working with data from North Andover, Massachusetts, comparing house prices in 1978 (before any rumors) and 1981 (when construction began). First, I need to run an Ordinary Least Squares (OLS) regression to estimate the model, which includes a dummy for the year 1981 (`y81`), a dummy for houses located near the incinerator site (`nearinc`), and their interaction term (`y81*nearinc`). The dependent variable is the real house price in 1978 dollars. I also want to consider different types of standard errors. The prompt specifically mentions checking `infer.ols` with `se='HC1'` to compare it with the classical standard errors. This is important because robust standard errors can offer a more reliable inference when there might be heteroskedasticity in the error terms. I'll compare the results for the interaction term's standard error using both classical and HC1 methods. Upon reviewing the output from `infer.ols` (`call_f4e0a289`), I see the following coefficients: * `rprice = 82,517.23 - 18,824.37 * nearinc + 18,790.29 * y81 - 11,863.90 * y81*nearinc` The prompt explicitly asks for two things: 1. The coefficient on the interaction term (`y81*nearinc`). 2. The difference-in-differences (DID) estimate of the incinerator's effect on the price of nearby houses. In this particular 2x2 DID setup, the coefficient on the interaction term *is* the DID estimate. This is because the interaction term captures the change in the difference in housing prices for the "nearinc" group after the incinerator's construction began, relative to the change for the "not nearinc" group. Looking at the specific figures from the `infer.ols` call (`call_f4e0a289`): * The coefficient on the interaction term (`y81nrinc`) is -11,863.90. * The classical standard error for this interaction term is 7,456.65. * The corresponding t-statistic is -1.59. Now, let's check the robust standard errors (HC1) from the `infer.ols` call (`call_051d96fe`). This result directly gives the DID estimate with robust standard errors: * The DID estimate is -11,863.90. * The robust standard error (HC1) is 8,635.59. The prompt also requires me to adhere to specific reporting guidelines, including writing the answer in English (as the prompt is in English), being short and direct, and including the estimate with its uncertainty. I also need to note potential limitations or caveats. From the prompt's Rule 7, I must include caveats that limit the conclusion. I've identified several: * **Parallel Pre-Trends:** With only two time periods (1978 and 1981), it's impossible to test whether the housing prices in the treated and control groups would have followed parallel trends in the absence of the incinerator. This is a critical assumption for DID. * **Statistical Significance:** The DID estimate of -11,863.90 is not statistically significant at conventional levels (e.g., 5% or 10%). The p-value for the interaction term (using classical SE) is 0.1126. This means the observed effect could reasonably be due to random chance. * **Model Specification:** This is a simplified 2x2 model. It doesn't control for other house-specific characteristics (like age, size, number of rooms) that could influence prices and might differ between the "near" and "not near" groups, or change over time. Wooldridge addresses this in later examples by including these controls. Finally, I need to ensure I quote the correct figures and their references. The key figures to report are the DID estimate and its standard error. I will use `call_f4e0a289:coef.y81nrinc` for the interaction coefficient and its classical standard error, and `call_051d96fe:did` for the DID estimate with robust (HC1) standard errors, as this is a direct output for that calculation. I should also consider including the other coefficients if they are directly used or implied in the final reporting, but the prompt specifically focuses on the interaction/DID. I'll use the numbers exactly as the tools stated them. Everything seems in order, so I'm ready to formulate the final answer.

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "In the pooled ordinary least squares regression of real house prices (`rprice`, in 1978 dollars) on the 1981 dummy (`y81`), the proximity dummy (`nearinc`), and their interaction (`y81nrinc`), the estimated difference-in-differences coefficient on the interaction term (`y81nrinc`) is -11863.9 (SE = 7456.65 with classical homoskedastic standard errors; 95% CI: [-26534.7, 2806.87]). Under heteroskedasticity-robust standard errors (HC1), the standard error is 8635.59 (95% CI: [-28854.2, 5126.4]).\n\nFor completeness, the other estimated coefficients in this specification are:\n- `y81`: 18790.3 (SE = 4050.06; 95% CI: [10821.9, 26758.7])\n- `nearinc`: -18824.4 (SE = 4875.32; 95% CI: [-28416.4, -9232.29])\n\nThis indicates that, relative to houses further away, real prices of houses located near the incinerator site fell by approximately $11,863.90 between 1978 and 1981. However, this estimate is not statistically significant at the 5% or 10% levels (t = -1.59, p = 0.113).",
       "caveats": [
         "With only two time periods (1978 and 1981), the parallel trends assumption cannot be tested.",
         "The estimate is not statistically significant at conventional levels (p = 0.113 under homoskedastic errors; standard error increases to 8635.59 under robust errors).",
         "This simple specification does not control for other housing characteristics (e.g., age, size, land area, number of rooms) that may differ across locations and over time."
       ],
       "figure_refs": [
         "call_f4e0a289:coef.y81nrinc",
         "call_f4e0a289:coef.y81",
         "call_f4e0a289:coef.nearinc",
         "call_051d96fe:did"
       ]
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
9. 8Guarda
   resposta recusada

   Números encontrados

   1978 · 1981 · -11863.9 · 7456.65 · 95% · -26534.7 · 2806.87 · 8635.59 · 95% · -28854.2 · 5126.4 · 18790.3 · 4050.06 · 95% · 10821.9 · 26758.7 · -18824.4 · 4875.32 · 95% · -28416.4 · -9232.29 · 11,863.90 · 1978 · 1981 · 5% · 10% · -1.59 · 0.113 · 1978 · 1981 · 0.113 · 8635.59

   Sem lastro

   11,863.90

   Não citados

   —

   0 nova(s) tentativa(s) restante(s)
10. 9Turno do modelo
    call_9bbbb039gemini-3.8-flashFim: STOP10.868 ms19055 de entrada · 588 de saída · 640 de raciocínio · 16249 em cache

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process on Number Accuracy** I'm reviewing my output and noticed a discrepancy. I wrote "11,863.90" with two decimal places, but the tool reported "-11863.9". This is a critical point because of Rule 1: "Every number in your answer must have been stated by a tool call in this run (a figure, its standard error or interval, a diagnostic, a table cell) or written in the question." I need to be extremely precise and only use the exact numbers provided by the tools. So, instead of "11,863.90", I must use "-11863.9". I also saw numbers like "113" and "1.59" in my draft, which are not directly from tool outputs. Let's cross-reference with `tables.coefficients` from `call_f4e0a289`. The tool output for `coef` is indeed `-11863.9`, `se` is `7456.65`, `t` is `-1.59105`, and `p` is `0.112595`. These are the exact figures I should be using. I've also compiled a list of other exact numbers from the tools and the question that I need to adhere to: * `-11863.9` * `7456.65` * `-26534.7` * `2806.87` * `8635.59` * `-28854.2` * `5126.4` * `18790.3` * `4050.06` * `10821.9` * `26758.7` * `-18824.4` * `4875.32` * `-28416.4` * `-9232.29` * `82517.2` * `2726.91` Numbers from the question are: `1978`, `1981`. The number of observations (`n_obs`) is `321`. The R-squared value (`r2`) is `0.173948301534997`. Crucially, Rule 7 states: "Write numbers as the tools stated them; do not quote rule-of-thumb thresholds as numbers." This means I can't use approximations like "5%" or "10%" unless they are directly provided by a tool. I need to stick strictly to the exact numerical outputs. Therefore, my goal now is to reconstruct the answer, ensuring that every single number I include is an exact match to what the tools or the original question provided. No rounding, no estimations, no rule-of-thumb thresholds.

    Chamadas pedidas

    • submit_answer (meta-ferramenta)

      {
        "answer": "In the pooled ordinary least squares regression of real house prices (rprice, in 1978 dollars) on the 1981 dummy (y81), the proximity dummy (nearinc), and their interaction (y81nrinc), the difference-in-differences estimate of the incinerator's effect—given by the coefficient on the interaction term y81nrinc—is -11863.9.\n\nSummary of the estimated coefficients:\n- Interaction term (y81nrinc): -11863.9 (classical SE = 7456.65; 95% CI: [-26534.7, 2806.87]; robust SE = 8635.59; 95% CI: [-28854.2, 5126.4])\n- Year 1981 dummy (y81): 18790.3 (classical SE = 4050.06; 95% CI: [10821.9, 26758.7])\n- Proximity dummy (nearinc): -18824.4 (classical SE = 4875.32; 95% CI: [-28416.4, -9232.29])\n\nThe point estimate indicates that real prices of houses near the incinerator site fell by 11863.9 relative to houses farther away between 1978 and 1981, though the coefficient is not statistically significant at conventional significance levels (p = 0.112595; t = -1.59105).",
        "caveats": [
          "With only two periods (1978 and 1981), the parallel trends assumption cannot be tested.",
          "The estimate is imprecise and not statistically distinguishable from zero at conventional levels (classical SE = 7456.65, robust SE = 8635.59, p = 0.112595).",
          "The simple 2x2 specification does not condition on other housing characteristics such as age, lot size, living area, and number of rooms."
        ],
        "figure_refs": [
          "call_f4e0a289:coef.y81nrinc",
          "call_f4e0a289:coef.y81",
          "call_f4e0a289:coef.nearinc",
          "call_051d96fe:did"
        ]
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
11. 10Guarda
    resposta aceita

    Números encontrados

    1978 · 1981 · -11863.9 · -11863.9 · 7456.65 · 95% · -26534.7 · 2806.87 · 8635.59 · 95% · -28854.2 · 5126.4 · 1981 · 18790.3 · 4050.06 · 95% · 10821.9 · 26758.7 · -18824.4 · 4875.32 · 95% · -28416.4 · -9232.29 · 11863.9 · 1978 · 1981 · 0.112595 · -1.59105 · 1978 · 1981 · 7456.65 · 8635.59 · 0.112595

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    11863.9

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12. 11Resposta

    In the pooled ordinary least squares regression of real house prices (rprice, in 1978 dollars) on the 1981 dummy (y81), the proximity dummy (nearinc), and their interaction (y81nrinc), the difference-in-differences estimate of the incinerator's effect—given by the coefficient on the interaction term y81nrinc—is -11863.9. Summary of the estimated coefficients: - Interaction term (y81nrinc): -11863.9 (classical SE = 7456.65; 95% CI: [-26534.7, 2806.87]; robust SE = 8635.59; 95% CI: [-28854.2, 5126.4]) - Year 1981 dummy (y81): 18790.3 (classical SE = 4050.06; 95% CI: [10821.9, 26758.7]) - Proximity dummy (nearinc): -18824.4 (classical SE = 4875.32; 95% CI: [-28416.4, -9232.29]) The point estimate indicates that real prices of houses near the incinerator site fell by 11863.9 relative to houses farther away between 1978 and 1981, though the coefficient is not statistically significant at conventional significance levels (p = 0.112595; t = -1.59105). Ressalvas: With only two periods (1978 and 1981), the parallel trends assumption cannot be tested. The estimate is imprecise and not statistically distinguishable from zero at conventional levels (classical SE = 7456.65, robust SE = 8635.59, p = 0.112595). The simple 2x2 specification does not condition on other housing characteristics such as age, lot size, living area, and number of rooms.

    • With only two periods (1978 and 1981), the parallel trends assumption cannot be tested.
    • The estimate is imprecise and not statistically distinguishable from zero at conventional levels (classical SE = 7456.65, robust SE = 8635.59, p = 0.112595).
    • The simple 2x2 specification does not condition on other housing characteristics such as age, lot size, living area, and number of rooms.

    Figuras citadas: call_f4e0a289:coef.y81nrinc, call_f4e0a289:coef.y81, call_f4e0a289:coef.nearinc, call_051d96fe:did · Parada: answered

    Figura da resposta: call_f4e0a289:coef.y81 = 18.790,318790.28595300544 (se 4.050,064050.0649593130915)
13. 12Pontuação
    #0resposta com lastrofalhouobrigatóriaunbacked: ['11863.9']
14. 13Pontuação
    #1figura da resposta escolhidapassouobrigatóriaanswer figure call_f4e0a289:coef.y81 (infer.ols)
15. 14Pontuação
    #2uma entrepassouobrigatóriatool_called(infer.ols): ok | tool_called(infer.did_2x2): answer call is infer.ols
16. 15Pontuação
    #3argumento igualpassouobrigatóriainfer.ols.y = 'rprice'
17. 16Pontuação
    #4argumento igualfalhouobrigatóriainfer.ols.x = ['y81', 'nearinc', 'y81nrinc']
18. 17Pontuação
    #5argumento igualfalhouobrigatóriainfer.ols.controls = []
19. 18Pontuação
    #6argumento igualpassouobrigatórianot applicable: the answer call is infer.ols, not infer.did_2x2
20. 19Pontuação
    #7argumento igualpassouobrigatórianot applicable: the answer call is infer.ols, not infer.did_2x2
21. 20Pontuação
    #8argumento igualpassouobrigatórianot applicable: the answer call is infer.ols, not infer.did_2x2
22. 21Pontuação
    #9diagnósticopassouobrigatória321 vs 321
23. 22Pontuação
    #10diagnósticopassouobrigatórianot applicable: the answer call is infer.ols, not infer.did_2x2
24. 23Pontuação
    #11figura da resposta próximafalhouobrigatória18790.28595 vs -11863.90325211261
25. 24Pontuação
    #12figura da resposta próximafalhouregistrada4050.064959 vs 7456.646173258854
26. 25Pontuação
    #13figura da resposta próximafalhouregistrada18790.28595 vs -11863.9
27. 26Pontuação
    #14figura da resposta próximafalhouregistrada4050.064959 vs 7456.6

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