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Kernel density estimation for undirected dyadic data

Graham, Bryan S.; Niu, Fengshi; Powell, James - 2019 - This Draft: July 2019

We study nonparametric estimation of density functions for undirected dyadic random variables (i.e., random variables de?ned for all unordered pairs of agents/nodes in a weighted network of order N). These random variables satisfy a local dependence property: any random variables in the network...

Persistent link: https://www.econbiz.de/10012053034

A Method of Moments Estimator of Tail Dependence

Einmahl, John H. J.; Krajina, Andrea; Segers, Johan - 2007

In the world of multivariate extremes, estimation of the dependence structure still presents a challenge and an interesting problem. A procedure for the bivariate case is presented that opens the road to a similar way of handling the problem in a truly multivariate setting. We consider a...

Persistent link: https://www.econbiz.de/10014223096

Nonparametric Estimation of Risk-Neutral Densities

Grith, Maria; Härdle, Wolfgang; Schienle, Melanie - 2017

This chapter deals with nonparametric estimation of the risk neutral density. We present three different approaches which do not require parametric functional assumptions on the underlying asset price dynamics nor on the distributional form of the risk neutral density. The first estimator is a...

Persistent link: https://www.econbiz.de/10014123485

Local Multiplicative Bias Correction for Asymmetric Kernel Density Estimators

Hagmann-von Arx, Matthias; Scaillet, O.; Baker … - 2004

We consider semiparametric asymmetric kernel density estimators when the unknown density has support on [0, ∞). We provide a unifying framework which contains asymmetric kernel versions of several semiparametric density estimators considered previously in the literature. This framework allows...

Persistent link: https://www.econbiz.de/10014074330

Bayesian Nonparametric Calibration and Combination of Predictive Distributions

Bassetti, Federico - 2015

We introduce a Bayesian approach to predictive density calibration and combination that accounts for parameter uncertainty and model set incompleteness through the use of random calibration functionals and random combination weights. Building on the work of Ranjan and Gneiting (2010) and...

Persistent link: https://www.econbiz.de/10013027970

A Note on the Relation of Weighting and Matching Estimators

Lechner, Michael - 2007

This paper compares the inverse-probability-of-selection-weighting estimation principle with the matching principle and … derives conditions for weighting and matching to identify the same and the true distribution, respectively. This comparison …

Persistent link: https://www.econbiz.de/10012728898

Programme Evaluation with Multiple Treatments

Frölich, Markus - 2002

This paper reviews the main identification and estimation strategies for microeconomic policy evaluation. Particular … nonparametric matching and weighting estimators of the average treatment effects and their properties are examined. …

Persistent link: https://www.econbiz.de/10010262703

Programme evaluation with multiple treatments

Frölich, Markus - 2002

Persistent link: https://www.econbiz.de/10011412411

Nonparametric IV estimation of local average treatment effects with covariates

Froelich, Markus - School of Economics and Political Science, Universität … - 2002

In this paper nonparametric instrumental variable estimation of local average treatment effects (LATE) is extended to incorporate confounding covariates. Estimation of local average treatment effects is appealing since their identification relies on much weaker assumptions than the...

Persistent link: https://www.econbiz.de/10005453938

A generalization of the balancing property of the propensity score

Froelich, Markus - School of Economics and Political Science, Universität … - 2002

Propensity score matching is a nonparametric technique frequently used for estimating average treatment effects. Yet … its applicability is not confined to treatment evaluation. In this paper the propensity score property is generalized to … the setting of selection on unobservables. It is shown that propensity score matching can be used to decompose effects due …

Persistent link: https://www.econbiz.de/10005453940