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~person:"Lechner, Michael"
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Lechner, Michael
Fernandez, Pablo
339
Franses, Philip Hans
103
Levy, Daniel
97
Ha Nam Khanh, Giao
73
Velez-Pareja, Ignacio
72
Volle, Pierre
72
Budzinski, Oliver
71
Klotz, Michael
56
Parguel, Béatrice
56
Desmet, Pierre
54
Acemoglu, Daron
53
Friedman, Hershey H.
52
Sethi, Suresh
52
Hildebrandt, Lutz
51
Darpy, Denis
46
Tham, Joseph
45
Bergen, Mark
44
Naudé, Wim
44
Camilleri, Mark Anthony
43
Restrepo, Pascual
43
Carabias, Jose M.
41
Goldfarb, Avi
41
Guiot, Denis
41
Larceneux, Fabrice
38
Magni, Carlo Alberto
38
Oxelheim, Lars
36
Snir, Avichai
35
Benoît-Moreau, Florence
34
Gans, Joshua
34
Michelberger, Pál
34
Dilger, Alexander
33
Dutta, Shantanu
33
Luo, Xueming
33
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32
Sidorchuk, Сидорчук Роман Роальдович
32
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31
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ECONIS (ZBW)
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Payment evasion
Bühler, Stefan
;
Halbheer, Daniel
;
Lechner, Michael
-
2015
imposed on detected payment evaders. For a given price and fine, the
consumers
purchase, evade payment, or choose the outside …
Persistent link: https://www.econbiz.de/10010518819
Saved in:
2
Payment evasion
Bühler, Stefan
;
Halbheer, Daniel
;
Lechner, Michael
-
2015
Persistent link: https://www.econbiz.de/10011290824
Saved in:
3
Payment evasion
Bühler, Stefan
;
Halbheer, Daniel
;
Lechner, Michael
- In:
The journal of industrial economics
65
(
2017
)
4
,
pp. 804-832
Persistent link: https://www.econbiz.de/10011788696
Saved in:
4
Payment evasion
Bühler, Stefan
;
Halbheer, Daniel
;
Lechner, Michael
-
2014
Persistent link: https://www.econbiz.de/10011288849
Saved in:
5
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
Saved in:
6
Does the estimation of the propensity score by machine learning improve matching estimation? : the case of Germany's programmes for long term unemployed
Goller, Daniel
;
Lechner, Michael
;
Moczall, Andreas
; …
-
2019
Matching-type estimators using the propensity score are the major workhorse in active labour market policy evaluation. This work investigates if machine learning algorithms for estimating the propensity score lead to more credible estimation of average treatment effects on the treated using a...
Persistent link: https://www.econbiz.de/10012060603
Saved in:
7
Does the estimation of the propensity score by machine learning improve matching estimation? : the case of Germany's programmes for long term unemployed
Goller, Daniel
;
Lechner, Michael
;
Moczall, Andreas
; …
-
2019
Persistent link: https://www.econbiz.de/10012098947
Saved in:
8
Priority to unemployed immigrants? : a causal machine learning evaluation of training in Belgium
Cockx, Bart
;
Lechner, Michael
;
Bollens, Joost
-
2020
Persistent link: https://www.econbiz.de/10012216299
Saved in:
9
Priority to unemployed immigrants : a Causal machine learning evaluation of training in Belgium
Cockx, Bart
;
Lechner, Michael
;
Bollens, Joost
-
2020
Based on administrative data of unemployed in Belgium, we estimate the labour market effects of three training programmes at various aggregation levels using Modified Causal Forests, a causal machine learning estimator. While all programmes have positive effects after the lock-in period, we find...
Persistent link: https://www.econbiz.de/10012219355
Saved in:
10
Priority to unemployed immigrants? : a causal machine learning evaluation of training in Belgium
Cockx, Bart
;
Lechner, Michael
;
Bollens, Joost
-
2019
We investigate heterogenous employment effects of Flemish training programmes. Based on administrative individual data, we analyse programme effects at various aggregation levels using Modified Causal Forests (MCF), a causal machine learning estimator for multiple programmes. While all...
Persistent link: https://www.econbiz.de/10012153340
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