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ECONIS (ZBW)
81
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61
What is the value added by using causal machine learning methods in a welfare experiment evaluation?
Strittmatter, Anthony
-
2019
-
First Draft: September 17, 2018
I investigate causal machine learning (CML) methods to estimate effect heterogeneity by means of conditional average treatment effects (CATEs). In particular, I study whether the estimated effect heterogeneity can provide evidence for the theoretical labour supply predictions of Connecticut's...
Persistent link: https://www.econbiz.de/10012232107
Saved in:
62
Determinants of banks' profitability: do Basel III liquidity and capital ratios matter?
Durand, Pierre
-
2019
Persistent link: https://www.econbiz.de/10012242184
Saved in:
63
What is the value added by using causal machine learning methods in a welfare experiment evaluation?
Strittmatter, Anthony
-
2019
Recent studies have proposed causal machine learning (CML) methods to estimate conditional average treatment effects (CATEs). In this study, I investigate whether CML methods add value compared to conventional CATE estimators by re-evaluating Connecticut's Jobs First welfare experiment. This...
Persistent link: https://www.econbiz.de/10012161467
Saved in:
64
Corporate default forecasting with machine learning
Moscatelli, Mirko
;
Narizzano, Simone
;
Parlapiano, Fabio
; …
-
2019
Persistent link: https://www.econbiz.de/10012144526
Saved in:
65
Forecasting own brand sales : does incorporating competition help?
Li, Wei
;
Fok, Dennis
;
Franses, Philip Hans
-
2019
Persistent link: https://www.econbiz.de/10012149723
Saved in:
66
Machine learning, the Treasury yield curve and recession forecasting
Puglia, Michael
;
Tucker, Adam
-
2019
Persistent link: https://www.econbiz.de/10012388624
Saved in:
67
Forecasting inflation in a data-rich environment : the benefits of machine learning methods
Madeiros, Marcelo C.
;
Vasconcelos, Gabriel F. R.
; …
-
2019
Persistent link: https://www.econbiz.de/10012249628
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68
The within-job gender pay gap in Hungary
Takács, Olga
;
Vincze, János
-
2018
seems to carry definite financial advantages. Finally, we apply an alternative methodology,
Random
Forest
regression, and …
Persistent link: https://www.econbiz.de/10012012514
Saved in:
69
Machine learning estimation of heterogeneous causal effects : empirical Monte Carlo evidence
Knaus, Michael C.
;
Lechner, Michael
;
Strittmatter, Anthony
-
2018
Persistent link: https://www.econbiz.de/10012001308
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70
Pre- and within-season attendance forecasting in Major League Baseball : a
random
forest
approach
Mueller, Steffen Q.
-
2018
-
Version: June 2018
This study explores the forecasting of Major League Baseball game ticket sales and identifies important attendance predictors by means of random forests that are grown from classification and regression trees (CART) and conditional inference trees. Unlike previous studies that predict sport...
Persistent link: https://www.econbiz.de/10011866133
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