The Oracle Local Polynomial Estimator
Códigos JEL: C14, C46, C52
- Autores:
-
Torres, Santiago
- Tipo de recurso:
- Work document
- Fecha de publicación:
- 2023
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/70960
- Acceso en línea:
- https://hdl.handle.net/1992/70960
- Palabra clave:
- Regression discontinuity designs
Non-parametric estimation
Local polynomial sstimators
Causal inference
Mean-squared error
Economía
- Rights
- openAccess
- License
- https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf
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The Oracle Local Polynomial Estimator |
| title |
The Oracle Local Polynomial Estimator |
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The Oracle Local Polynomial Estimator Regression discontinuity designs Non-parametric estimation Local polynomial sstimators Causal inference Mean-squared error Economía |
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The Oracle Local Polynomial Estimator |
| title_full |
The Oracle Local Polynomial Estimator |
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The Oracle Local Polynomial Estimator |
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The Oracle Local Polynomial Estimator |
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The Oracle Local Polynomial Estimator |
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Torres, Santiago |
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Torres, Santiago |
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Regression discontinuity designs Non-parametric estimation Local polynomial sstimators Causal inference Mean-squared error |
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Regression discontinuity designs Non-parametric estimation Local polynomial sstimators Causal inference Mean-squared error Economía |
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Economía |
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Códigos JEL: C14, C46, C52 |
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54 páginas |
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Torres, Santiago2023-10-25T20:18:41Z2023-10-25T20:18:41Z2023-101657-7191https://hdl.handle.net/1992/7096010.57784/1992/70960Códigos JEL: C14, C46, C52This paper introduces a new estimator for continuity-based Regression Discontinuity (RD) designs named the estimated Oracle Local Polynomial Estimator (OLPE). The OLPE is a weighted average of a collection of local polynomial estimators, each of which is characterized by a unique bandwidth sequence, polynomial order, and kernel weighting schemes, and whose weights are chosen to minimize the Mean-Squared Error (MSE) of the combination. This procedure yields a new consistent estimator of the target causal effect exhibiting lower bias and/or variance than its components. The precision gains stem from two factors. First, the method allocates more weight to estimators with lower asymptotic mean squared error, allowing it to select the specifications that are best suited to the specific estimation problem. Second, even if the individual estimators are not optimal, averaging mechanically leads to bias reduction and variance shrinkage. Although the OLPE weights are unknown, an “estimated” OLPE can be constructed by replacing unobserved MSE-optimal weights with those derived from a consistent estimator. Monte Carlo simulations indicate that the estimated OLPE can significantly enhance precision compared to conventional local polynomial methods, even in small sample sizes. The estimated OLPE remains consistent and asymptotically normal without imposing additional assumptions beyond those required for local polynomial estimators. Moreover, this approach applies to sharp, fuzzy, and kink RD designs, with or without covariates.54 páginasapplication/pdfengUniversidad de los AndesFacultad de EconomíaDocumentos CEDE; 2023-33https://ideas.repec.org/p/col/000089/020937.htmlhttps://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdfinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2The Oracle Local Polynomial EstimatorDocumento de trabajoinfo:eu-repo/semantics/workingPaperinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_8042http://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/WPRegression discontinuity designsNon-parametric estimationLocal polynomial sstimatorsCausal inferenceMean-squared errorEconomíaPublicationLICENSElicense.txtlicense.txttext/plain; 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