Bootstrap confidence sets under model misspecification [PDF]
Annals of Statistics 2015, Vol. 43, No. 6, 2653-2675, 2014 A multiplier bootstrap procedure for construction of likelihood-based confidence sets is considered for finite samples and a possible model misspecification. Theoretical results justify the bootstrap validity for a small or moderate sample size and allow to control the impact of the parameter dimension $p$: the bootstrap approximation works if $p^3/n ...
Spokoiny, Vladimir, Zhilova, Mayya
arxiv +8 more sources
Weak-instrument-robust subvector inference in instrumental variables regression: A subvector Lagrange multiplier test and properties of subvector Anderson-Rubin confidence sets [PDF]
arXivWe propose a weak-instrument-robust subvector Lagrange multiplier test for instrumental variables regression. We show that it is asymptotically size-correct under a technical condition. This is the first weak-instrument-robust subvector test for instrumental variables regression to recover the degrees of freedom of the commonly used non-weak-instrument-
Londschien, Malte, Bühlmann, Peter
arxiv +6 more sources
Multiply Connected Topological Economics, Confidence Relation and Political Economy [PDF]
arXiv, 2007 Using the similar formulas of the preference relation and the utility function, we propose the confidence relations and the corresponding influence functions that represent various interacting strengths of different families, cliques and systems of organization.
Yi‐Fang Chang
arxiv +5 more sources
Simultaneous likelihood-based bootstrap confidence sets for a large number of models [PDF]
arXiv, 2015 The paper studies a problem of constructing simultaneous likelihood-based confidence sets. We consider a simultaneous multiplier bootstrap procedure for estimating the quantiles of the joint distribution of the likelihood ratio statistics, and for adjusting the confidence level for multiplicity.
Zhilova, Mayya
arxiv +7 more sources
Wrapping input–output multipliers in confidence intervals
Journal of Economic StructuresAbstractInput–output multipliers are typically calculated as point estimates of the Leontief quantity model. From the previous literature, they can also be estimated (and their confidence intervals) directly from establishments’/industries’ inputs and outputs data by running an appropriate econometric regression.
Robert Stehrer +3 more
openaire +3 more sources
Uncertainty and Public Investment Multipliers: The Role of Economic Confidence
IMF Working Papers, 2021 William Gbohoui
openaire +3 more sources
Robust Inference via Multiplier Bootstrap [PDF]
arXiv, 2019 This paper investigates the theoretical underpinnings of two fundamental statistical inference problems, the construction of confidence sets and large-scale simultaneous hypothesis testing, in the presence of heavy-tailed data. With heavy-tailed observation noise, finite sample properties of the least squares-based methods, typified by the sample mean,
Xi Chen, Wen-Xin Zhou
arxiv +3 more sources
Towards Special Daemon-Sensitive Electron Multiplier: Positive Outcome of March 2009 Experiment [PDF]
Mod.Phys.Lett.A25:489-499,2010, 2009 Results of the experiments on daemon detection performed in St-Petersburg in March 2009 are presented. Adding the data obtained with the daemon-sensitive FEU-167-1 PM tubes to the data amassed in our previous measurements (starting from 2000) raises the confidence level of existence of the spring maximum in NEACHO (near-Earth almost circular ...
E. Drobyshevski, M. Drobyshevski
arxiv +3 more sources
Long Run Confidence: Estimating Confidence Intervals when using Long Run Multipliers [PDF]
, 2019 The recent exchange on Error Correction Models in Political Analysis and elsewhere dealt with several important issues involved in time series analysis. While there was much disagreement in the symposium, one common theme was the lack of power due to the
Mark David Nieman, David A. Peterson
openalex +3 more sources
Confidence Bands for Coefficients in High Dimensional Linear Models with Error-in-variables [PDF]
arXiv, 2017 We study high-dimensional linear models with error-in-variables. Such models are motivated by various applications in econometrics, finance and genetics. These models are challenging because of the need to account for measurement errors to avoid non-vanishing biases in addition to handle the high dimensionality of the parameters.
A. Belloni, V. Chernozhukov, A. Kaul
arxiv +3 more sources