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Latent heterogeneity in the marginal propensity to consume
Lewis, Daniel J.; Melcangi, Davide; Pilossoph, Laura - 2019
. We apply the estimator to the 2008 tax rebate and household consumption data, exploiting the randomized timing of … disbursements. We find a considerable degree of heterogeneity in MPCs, which varies by consumption good, and provide evidence on … results suggest that the partial equilibrium consumption response to the stimulus was twice as large as what is implied by …
Persistent link: https://www.econbiz.de/10012123509
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Machine learning treasury yields
Kakushadze, Zura; Yu, Willie - In: Bulletin of applied economics 7 (2020) 1, pp. 1-65
Persistent link: https://www.econbiz.de/10012807008
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Machine learning risk models
Kakushadze, Zura; Yu, Willie - In: Journal of risk & control 6 (2019) 1, pp. 37-64
Persistent link: https://www.econbiz.de/10012237599
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Exploring an LSTM-SARIMA routine for core inflation forecasting
Krukovets, Dmytro - In: Technology audit and production reserves 2 (2024) 2/76, pp. 6-12
Persistent link: https://www.econbiz.de/10014548215
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Statistical industry classification
Kakushadze, Zura; Yu, Willie - In: Journal of risk & control 3 (2016) 1, pp. 17-65
Persistent link: https://www.econbiz.de/10012229122
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A smart architecture design for health remote monitoring systems and heterogeneous wireless sensor network technologies : a machine learning breathlessness prediction prototype
Eddabbah, Mohamed; Moussaoui, Mohamed; Laaziz, Yassin - In: International journal of intelligent enterprise : IJIE 6 (2019) 2/3/4, pp. 293-310
Persistent link: https://www.econbiz.de/10012138929
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Who benefits from health insurance? : uncovering heterogeneous policy impacts using causal machine learning
Kreif, Noemi; Mirelman, Andrew; Moreno-Serra, Rodrigo; … - 2020
Persistent link: https://www.econbiz.de/10013168836
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Finite sample evaluation of causal machine learning methods : guidelines for the applied researcher
Naghi, Andrea A.; Wirths, Christian P. - 2021
The econometrics literature proposed several new causal machine learning methods (CML) in the past few years. These methods harness the strength of machine learning methods to flexibly model the relationship between the treatment, outcome and confounders, while providing valid inferential...
Persistent link: https://www.econbiz.de/10012650104
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An instrumental variable forest approach for detecting heterogeneous treatment effects in observational studies
Wang, Guihua; Li, Jun; Hopp, Wallace J. - In: Management science : journal of the Institute for … 68 (2022) 5, pp. 3399-3418
Persistent link: https://www.econbiz.de/10013368663
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The value added of machine learning to causal inference : evidence from revisited studies
Baiardi, Anna; Naghi, Andrea A. - In: The econometrics journal 27 (2024) 2, pp. 213-234
Persistent link: https://www.econbiz.de/10015046371