Replication Files for Nonparametric Derivative Estimation via Local Linear Forests

Replication files for the paper 'Nonparametric Derivative Estimation via Local Linear Forests' include all code, simulation scripts, and an empirical application. The repository contains a master script (master.R) to reproduce all results, tables, and figures. The empirical dataset (disc.dta) originates from Lang and Manove (2011).

Use Cases

• Reproduce simulation results for univariate and multivariate experiments based on the provided scripts.
• Replicate the empirical application analyzing data from Lang and Manove (2011).
• Study the implementation of local linear forests and derivative estimation helper functions.
• Benchmark derivative estimation performance using the included simulation framework.

Strengths

• Includes a complete replication package with a master script to regenerate all reported results.
• Empirical application is based on a cited external dataset (Lang and Manove, 2011).
• Code organization separates simulation scripts, helper functions, and output generation.

Limitations

• Column-level documentation for the empirical dataset (disc.dta) is absent; field semantics must be inferred after download.
• Row count and scale of the empirical data are unknown, which may limit suitability assessment.

Provenance

Source

Harvard Dataverse, author Justin Dang.

Collection Method

Created as replication materials for an academic paper.

Time Range

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Freshness

Last updated 2026-04-22 20:36:20; freshness should be verified.

Geography

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