Michael Lechner, Gabriel Okasa
arXiv 4 Jul 2019 · Econometrics
arXiv:1907.02436 · PDF · Extracted main text
In this paper we develop a new machine learning estimator for ordered choice models based on the random forest. The proposed Ordered Forest flexibly estimates the conditional choice probabilities while taking the ordering information explicitly into account. In addition to common machine learning estimators, it enables the estimation of marginal effects as well as conducting inference and thus provides the same output as classical econometric estimators. An extensive simulation study reveals a good predictive performance, particularly in settings with non-linearities and near-multicollinearity. An empirical application contrasts the estimation of marginal effects and their standard errors with an ordered logit model. A software implementation of the Ordered Forest is provided both in R and Python in the package orf available on CRAN and PyPI, respectively.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Wager, Stefan, Athey, Susan (2018) Estimation and Inference of Heterogeneous Treatment Effects using Random Forests | 1.000 | 12 | 5 | 100% |
| 2 | Lechner, Michael (2018) Modified Causal Forests for Estimating Heterogeneous Causal Effects self | 1.000 | 11 | 4 | 100% |
| 3 | Breiman, L (2001) Random Forests | 1.000 | 7 | 4 | 100% |
| 4 | Hornung, Roman (2019) Ordinal Forests | 0.971 | 12 | 3 | 92% |
| 5 | Hothorn, Torsten, Hornik, Kurt, Zeileis, Achim (2006) Unbiased recursive partitioning: A conditional inference framework | 0.941 | 6 | 3 | 83% |
| 6 | Janitza, Silke, Tutz, Gerhard, Boulesteix, Anne Laure (2016) Random forest for ordinal responses: Prediction and variable selection | 0.928 | 10 | 4 | 80% |
| 7 | Lechner, Michael, Okasa, Gabriel (2019) orf: Ordered Random Forests self | 0.843 | 4 | 4 | 75% |
| 8 | (2021) R: A Language and Environment for Statistical Computing | 0.737 | 3 | 3 | 67% |
| 9 | Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction | 0.737 | 3 | 2 | 100% |
| 10 | Meinshausen, Nicolai (2006) Quantile Regression Forests | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 68 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Ordered Correlation Forest | 1.000 | 7 | 5 |