The Finite Sample Performance of Semi- and Nonparametric Estimators for Treatment Effects and Policy Evaluation

In this paper, we propose nonparametric estimators of sharp bounds on the distribution of treatment effects of a binary treatment and establish their asymptotic distributions. We note the possible failure of the standard bootstrap with the same sample size and apply the fewer-than-nbootstrap to making inferences on these bounds. The finite sample performances of the confidence intervals for the bounds based on normal critical values, the standard bootstrap, and the fewer-than-nbootstrap are investigated via a simulation study. Finally we establish sharp bounds on the treatment effect distribution when covariates are available.

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Estimation of the tail-index in a conditional location-scale family of heavy-tailed distributions

Dependence Modeling ◽

10.1515/demo-2019-0021 ◽

2019 ◽

Vol 7 (1) ◽

pp. 394-417

Author(s):

Aboubacrène Ag Ahmad ◽

El Hadji Deme ◽

Aliou Diop ◽

Stéphane Girard

Keyword(s):

Asymptotic Properties ◽

Real Data ◽

Scale Model ◽

Extreme Value Index ◽

Finite Sample ◽

Scale Functions ◽

Nonparametric Estimators ◽

Heavy Tailed Distributions ◽

Finite Sample Properties ◽

Heavy Tailed

AbstractWe introduce a location-scale model for conditional heavy-tailed distributions when the covariate is deterministic. First, nonparametric estimators of the location and scale functions are introduced. Second, an estimator of the conditional extreme-value index is derived. The asymptotic properties of the estimators are established under mild assumptions and their finite sample properties are illustrated both on simulated and real data.

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Estimation of Treatment Effects in Repeated Public Goods Experiments

Econometrics ◽

10.3390/econometrics6040043 ◽

2018 ◽

Vol 6 (4) ◽

pp. 43

Author(s):

Jianning Kong ◽

Donggyu Sul

Keyword(s):

Public Goods ◽

Treatment Effects ◽

Time Varying ◽

Initial Condition ◽

Finite Sample ◽

Cross Sectional ◽

Public Goods Experiments ◽

Voluntary Contribution Mechanism ◽

Average Contribution ◽

Cross Sectional Average

This paper provides a new statistical model for repeated voluntary contribution mechanism games. In a repeated public goods experiment, contributions in the first round are cross-sectionally independent simply because subjects are randomly selected. Meanwhile, contributions to a public account over rounds are serially and cross-sectionally correlated. Furthermore, the cross-sectional average of the contributions across subjects usually decreases over rounds. By considering this non-stationary initial condition—the initial contribution has a different distribution from the rest of the contributions—we model statistically the time varying patterns of the average contribution in repeated public goods experiments and then propose a simple but efficient method to test for treatment effects. The suggested method has good finite sample performance and works well in practice.

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Structural Equations, Treatment Effects and Econometric Policy Evaluation

10.3386/t0306 ◽

2005 ◽

Cited By ~ 1

Author(s):

James Heckman ◽

Edward Vytlacil

Keyword(s):

Policy Evaluation ◽

Treatment Effects ◽

Structural Equations

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NONPARAMETRIC ESTIMATION OF CONDITIONAL VALUE-AT-RISK AND EXPECTED SHORTFALL BASED ON EXTREME VALUE THEORY

Econometric Theory ◽

10.1017/s0266466616000517 ◽

2016 ◽

Vol 34 (1) ◽

pp. 23-67 ◽

Cited By ~ 15

Author(s):

Carlos Martins-Filho ◽

Feng Yao ◽

Maximo Torero

Keyword(s):

At Risk ◽

Value At Risk ◽

Monte Carlo Study ◽

Value Theory ◽

Expected Shortfall ◽

Scale Model ◽

Conditional Value At Risk ◽

Strong Mixing ◽

Finite Sample ◽

Nonparametric Estimators

We propose nonparametric estimators for conditional value-at-risk (CVaR) and conditional expected shortfall (CES) associated with conditional distributions of a series of returns on a financial asset. The return series and the conditioning covariates, which may include lagged returns and other exogenous variables, are assumed to be strong mixing and follow a nonparametric conditional location-scale model. First stage nonparametric estimators for location and scale are combined with a generalized Pareto approximation for distribution tails proposed by Pickands (1975, Annals of Statistics 3, 119–131) to give final estimators for CVaR and CES. We provide consistency and asymptotic normality of the proposed estimators under suitable normalization. We also present the results of a Monte Carlo study that sheds light on their finite sample performance. Empirical viability of the model and estimators is investigated through a backtesting exercise using returns on future contracts for five agricultural commodities.

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A COMPARISON OF NONPARAMETRIC ESTIMATORS FOR LENGTH DISTRIBUTION IN LINE SEGMENT PROCESSES

Image Analysis & Stereology ◽

10.5566/ias.1889 ◽

2019 ◽

Vol 38 (2) ◽

pp. 121

Author(s):

Zbynek Pawlas ◽

Marketa Zikmundova

Keyword(s):

Line Segment ◽

Length Distribution ◽

Hard Core ◽

Dimensional Euclidean Space ◽

Finite Sample ◽

Likelihood Estimator ◽

Nonparametric Estimators ◽

Nonparametric Maximum Likelihood Estimator ◽

Nonparametric Likelihood ◽

Kaplan Meier

We study nonparametric estimation of the length distribution for stationary line segment processes in the d-dimensional Euclidean space. Several methods have been proposed in the literature. We review different approaches (Horvitz-Thompson type estimator, reduced-sample estimator, Kaplan-Meier estimator, nonparametric maximum likelihood estimator, stochastic restoration estimation) and compare the finite sample behaviour by means of a simulation study for stationary line segment processes in 2D and 3D. Several data generating processes (Poisson point process, Matérn cluster process and Matérn hard-core process II) are considered with both independent and dependent segments. Our finite sample comparison reveals that the nonparametric likelihood estimator provides the most preferable method which works reasonably also if its assumptions are not satisfied.

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Variance reduction estimation for return models with jumps using gamma asymmetric kernels

Studies in Nonlinear Dynamics & Econometrics ◽

10.1515/snde-2018-0001 ◽

2019 ◽

Vol 23 (5) ◽

Cited By ~ 1

Author(s):

Yuping Song ◽

Weijie Hou ◽

Shengyi Zhou

Keyword(s):

Simulation Study ◽

Bias Correction ◽

High Frequency ◽

Variance Reduction ◽

Stock Exchange ◽

High Frequency Data ◽

Weak Consistency ◽

Frequency Data ◽

Finite Sample ◽

Nonparametric Estimators

Abstract This paper discusses Nadaraya-Watson estimators for the unknown coefficients in second-order diffusion model with jumps constructed with Gamma asymmetric kernels. Compared with existing nonparametric estimators constructed with Gaussian symmetric kernels, local constant smoothing using Gamma asymmetric kernels possesses some extra advantages such as boundary bias correction, variance reduction and resistance to sparse design points, which is validated through theoretical details and finite sample simulation study. Under the regular conditions, the weak consistency and the asymptotic normality of these estimators are presented. Finally, the statistical advantages of the nonparametric estimators are depicted through 5-minute high-frequency data from Shenzhen Stock Exchange in China.

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