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- University of Applied Sciences St. GallenCH12 papers
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10 papers · 1 filter
Efficient Sampling for Realized Variance Estimation in Time-Changed Diffusion Models
Timo Dimitriadis, Roxana Halbleib, Jeannine Polivka +3
This paper analyzes the benefits of sampling intraday returns in intrinsic time for the realized variance (RV) estimator. We theoretically show in finite samples that depending on…
Reservoir Computing for Macroeconomic Forecasting with Mixed Frequency Data
Giovanni Ballarin, Petros Dellaportas, Lyudmila Grigoryeva +3
Macroeconomic forecasting has recently started embracing techniques that can deal with large-scale datasets and series with unequal release periods. MIxed-DAta Sampling (MIDAS) and…
Analysing a built-in advantage in asymmetric darts contests using causal machine learning
Daniel Goller
We analyse a sequential contest with two players in darts where one of the contestants enjoys a technical advantage. Using methods from the causal machine learning literature, we a…
Double Machine Learning based Program Evaluation under Unconfoundedness
Michael C. Knaus
This paper reviews, applies and extends recently proposed methods based on Double Machine Learning (DML) with a focus on program evaluation under unconfoundedness. DML based method…
Direct and Indirect Effects based on Changes-in-Changes
Martin Huber, Mark Schelker, Anthony Strittmatter
We propose a novel approach for causal mediation analysis based on changes-in-changes assumptions restricting unobserved heterogeneity over time. This allows disentangling the caus…
Nonparametric estimation of causal heterogeneity under high-dimensional confounding
Michael Zimmert, Michael Lechner
This paper considers the practically important case of nonparametrically estimating heterogeneous average treatment effects that vary with a limited number of discrete and continuo…