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Author: Cerulli, Giovanni
Resulting in 2 citations.
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Cerulli, Giovanni |
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Data-driven Sensitivity Analysis for Matching Estimators Economics Letters 185 (December 2019): 108749. Also: https://www.sciencedirect.com/science/article/pii/S0165176519303763 Cohort(s): Young Women Publisher: Elsevier Keyword(s): Modeling; Statistical Analysis; Unions; Wages This paper proposes a sensitivity analysis test of unobservable selection for matching estimators based on a "leave-one-covariate-out" (LOCO) algorithm. Rooted in the machine learning literature, this sensitivity test performs a bootstrap over different subsets of covariates, and simulates various estimation scenarios to be compared with the baseline matching results. We provide an empirical application, comparing results with more traditional sensitivity tests. |
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Bibliography Citation
Cerulli, Giovanni. "Data-driven Sensitivity Analysis for Matching Estimators." Economics Letters 185 (December 2019): 108749.
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| 2. |
Cerulli, Giovanni |
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Improving Econometric Prediction by Machine Learning Applied Economics Letters published online (14 September 2020): DOI: 10.1080/13504851.2020.1820939. Also: https://www.tandfonline.com/doi/full/10.1080/13504851.2020.1820939 Cohort(s): Young Women Publisher: Routledge ==> Taylor & Francis (1998) Keyword(s): Modeling; Statistical Analysis; Wages, Women We present a Machine Learning (ML) toolbox to predict targeted econometric outcomes improving prediction in two directions: (i) by cross–validated optimal tuning, (ii) by comparing/combining results from different learners (meta-learning). In predicting woman wage class based on her characteristics, we show that all our ML methods' predictions highly outperform standard multinomial logit ones, both in terms of mean accuracy and its standard deviation. In particular, we set out that a regularized multinomial regression obtains an average prediction accuracy almost 60% larger than that of an unregularized one. Finally, as different learners may behave differently, we show that combining them into one ensemble learner proves to preserve good predictive accuracy lowering the variance more than stand-alone approaches. |
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Bibliography Citation
Cerulli, Giovanni. "Improving Econometric Prediction by Machine Learning." Applied Economics Letters published online (14 September 2020): DOI: 10.1080/13504851.2020.1820939.
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