Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Wuthrich, Kaspar"'
Multiple hypothesis testing practices vary widely, without consensus on which are appropriate when. This paper provides an economic foundation for these practices designed to capture processes of scientific communication, such as regulatory approval
Externí odkaz:
http://arxiv.org/abs/2104.13367
Autor:
Potrafke, Niklas, Wuthrich, Kaspar
We examine how Green governments influence environmental, macroeconomic, and education outcomes. We exploit that the Fukushima nuclear disaster in Japan gave rise to an unanticipated change in government in the German state Baden-Wuerttemberg in 2011
Externí odkaz:
http://arxiv.org/abs/2012.09906
We study the bias of classical quantile regression and instrumental variable quantile regression estimators. While being asymptotically first-order unbiased, these estimators can have non-negligible second-order biases. We derive a higher-order stoch
Externí odkaz:
http://arxiv.org/abs/2011.03073
Publikováno v:
Chapter 9 in: Chernozhukov, V., He, X., Koenker, R., Peng, L. (Eds.), Handbook of Quantile Regression. CRC Chapman-Hall, 2017
This chapter reviews the instrumental variable quantile regression model of Chernozhukov and Hansen (2005). We discuss the key conditions used for identification of structural quantile effects within this model which include the availability of instr
Externí odkaz:
http://arxiv.org/abs/2009.00436
Publikováno v:
Econometrica, Volume 90, Issue 2 (March 2022)
We theoretically analyze the problem of testing for $p$-hacking based on distributions of $p$-values across multiple studies. We provide general results for when such distributions have testable restrictions (are non-increasing) under the null of no
Externí odkaz:
http://arxiv.org/abs/1906.06711
Autor:
Wuthrich, Kaspar, Zhu, Ying
We study the finite sample behavior of Lasso-based inference methods such as post double Lasso and debiased Lasso. We show that these methods can exhibit substantial omitted variable biases (OVBs) due to Lasso not selecting relevant controls. This ph
Externí odkaz:
http://arxiv.org/abs/1903.08704
Autor:
Kaido, Hiroaki, Wuthrich, Kaspar
Publikováno v:
Quantitative Economics, Volume 12, Issue 2 (May 2021)
The instrumental variable quantile regression (IVQR) model (Chernozhukov and Hansen, 2005) is a popular tool for estimating causal quantile effects with endogenous covariates. However, estimation is complicated by the non-smoothness and non-convexity
Externí odkaz:
http://arxiv.org/abs/1812.10925
We propose a practical and robust method for making inferences on average treatment effects estimated by synthetic controls. We develop a $K$-fold cross-fitting procedure for bias correction. To avoid the difficult estimation of the long-run variance
Externí odkaz:
http://arxiv.org/abs/1812.10820
Publikováno v:
Proceedings of COLT 2018 (PMLR 75:732-749)
We extend conformal inference to general settings that allow for time series data. Our proposal is developed as a randomization method and accounts for potential serial dependence by including block structures in the permutation scheme. As a result,
Externí odkaz:
http://arxiv.org/abs/1802.06300
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