Abdul-Nasah Soale, Emmanuel Selorm Tsyawo
arXiv 18 Feb 2023 · Econometrics
arXiv:2302.09255 · PDF · Extracted main text
High covariate dimensionality is increasingly occurrent in model estimation, and existing techniques to address this issue typically require sparsity or discrete heterogeneity of the unobservable parameter vector. However, neither restriction may be supported by economic theory in some empirical contexts, leading to severe bias and misleading inference. The clustering-based grouped parameter estimator (GPE) introduced in this paper drops both restrictions and maintains the natural one that the parameter support be bounded. GPE exhibits robust large sample properties under standard conditions and accommodates both sparse and non-sparse parameters whose support can be bounded away from zero. Extensive Monte Carlo simulations demonstrate the excellent performance of GPE in terms of bias reduction and size control compared to competing estimators. An empirical application of GPE to estimating price and income elasticities of demand for gasoline highlights its practical utility.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Belloni, Alexandre, Chernozhukov, Victor, Hansen, Christian (2014) Inference on treatment effects after selection among high-dimensional controls | 1.000 | 9 | 4 | 100% |
| 2 | Bonhomme, Stéphane, Lamadon, Thibaut, Manresa, Elena (2022) Discretizing unobserved heterogeneity | 1.000 | 5 | 3 | 100% |
| 3 | Ke, Zheng Tracy, Fan, Jianqing, Wu, Yichao (2015) Homogeneity pursuit | 1.000 | 5 | 3 | 100% |
| 4 | Chernozhukov, Victor, Newey, Whitney K, Singh, Rahul (2022) De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers | 0.956 | 8 | 4 | 88% |
| 5 | Bonhomme, Stéphane, Manresa, Elena (2015) Grouped patterns of heterogeneity in panel data | 0.950 | 7 | 4 | 86% |
| 6 | Belloni, Alexandre, Chen, Daniel, Chernozhukov, Victor, Hansen, Chri… (2012) Sparse models and methods for optimal instruments with an application to eminent domain | 0.941 | 6 | 4 | 83% |
| 7 | Chernozhukov, Victor, Hansen, Christian, Liao, Yuan, Zhu, Yinchu (2023) Inference for low-rank models | 0.874 | 6 | 2 | 100% |
| 8 | Semenova, Vira, Chernozhukov, Victor (2021) Debiased machine learning of conditional average treatment effects and other causal functions | 0.737 | 4 | 2 | 75% |
| 9 | Cheng, Xu, Schorfheide, Frank, Shao, Peng (2021) Clustering for multi-dimensional heterogeneity | 0.737 | 3 | 2 | 100% |
| 10 | Belloni, Alexandre, Chernozhukov, Victor, Chetverikov, Denis, Kato,… (2015) Some new asymptotic theory for least squares series: Pointwise and uniform results | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 41 scored citations.