Humboldt-Universität zu Berlin - Mathematisch-Naturwissenschaftliche Fakultät - Institut für Mathematik

Forschungsseminar Mathematische Statistik

Für den Bereich Statistik

A. Carpentier, S. Greven, W. Härdle, M. Reiß, V. Spokoiny



Weierstrass-Institut für Angewandte Analysis und Stochastik
Mohrenstrasse 39
10117 Berlin



mittwochs, 10.00 - 12.00 Uhr


19. April 2023
Chen Huang (University of Aarhus)
Arellano-Bond LASSO Estimator for Long Panel Dynamic Linear Models
Abstract: We consider the estimation and inference for dynamic linear panel models with both large cross-sectional dimension $N$ and long time dimension $T$. The Arellano-Bond (AB) estimator (Arellano and Bond, 1991) is a popular method for dynamic panels. However, it has large bias when $T$ is large. We present a simple least absolute shrinkage and selection operator (LASSO) estimator for the AB method. In particular, we first use LASSO to fit the optimal instrument variables (IV) based on a large group of lags, and then implement the linear IV estimator in the second stage. To further reduce the bias of the estimator, we propose a sample-splitting procedure. Simulations show that the sample-splitting AB-LASSO provides more accuracy in estimation and inference compared to the AB method. Finally, we apply the approach to evaluate the long run effect of democracy on the economic growth.
26. April 2023
03. Mai 2023
10. Mai 2023
17. Mai 2023
Enno Mammen (Mathematicon Universität Heidelberg) 
Random Planted Forest: a directly interpretable tree ensemble
Abstract: We introduce a novel interpretable, tree based algorithm for prediction in a regression setting in which each tree in a classical random forest is replaced by a family of planted trees that grow simultaneously. The motivation for our algorithm is to estimate the unknown regression function from a functional ANOVA decomposition perspective, where each tree corresponds to a function within that decomposition. Therefore, planted trees are limited in the number of interaction terms. The maximal order of approximation in the ANOVA decomposition can be specified or left unlimited. If a first order approximation is chosen, the result is an additive model. In the other extreme case, if the order of approximation is not limited, the resulting model places no restrictions on the form of the regression function. The talk reports on joint work with Munir Hiabu and Joseph T. Meyer.
24. Mai 2023
Jonathan Niles-Weed (Courant Institute)
31. Mai 2023    
07. Juni 2023
14. Juni 2023
21. Juni 2023
28. Juni 2023
Alessia Caponera (Universität Milano-Bicocca)
Kartik Waghmare (EPFL Lausanne)
05. Juli 2023
21. Juli 2023
19. Juli 2023



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Frau Andrea Fiebig

Telefon: +49-30-2093-45460
Fax:        +49-30-2093-45451
Humboldt-Universität zu Berlin
Institut für Mathematik
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10099 Berlin, Germany