Dr Daniel Wilhelm: all content

    Showing 1 – 20 of 31 results

    Working paper graphic

    An Adaptive Test of Stochastic Monotonicity

    Working Paper

    We propose a new nonparametric test of stochastic monotonicity which adapts to the unknown smoothness of the conditional distribution of interest, possesses desirable asymptotic properties, is conceptually easy to implement, and computationally attractive.

    4 May 2020

    Working paper graphic

    An adaptive test of stochastic monotonicity

    Working Paper

    We propose a new nonparametric test of stochastic monotonicity which adapts to the unknown smoothness of the conditional distribution of interest, possesses desirable asymptotic properties, is conceptually easy to implement, and computationally attractive.

    15 October 2019

    Working paper graphic

    Testing for the presence of measurement error

    Working Paper

    This paper proposes a simple nonparametric test of the hypothesis of no measurement error in explanatory variables and of the hypothesis that measurement error, if there is any, does not distort a given object of interest.

    23 September 2019

    Working paper graphic

    Optimal Data Collection for Randomized Control Trials

    Working Paper

    In a randomized control trial, the precision of an average treatment effect estimator and the power of the corresponding t-test can be improved either by collecting data on additional individuals, or by collecting additional covariates that predict the outcome variable. To design the experiment, a researcher needs to solve this tradeoff subject to her budget constraint. We show that this optimization problem is equivalent to optimally predicting outcomes by the covariates, which in turn can be solved using existing machine learning techniques using pre-experimental data such as other similar studies, a census, or a household survey. In two empirical applications, we show that our procedure can lead to reductions of up to 58% in the costs of data collection, or improvements of the same magnitude in the precision of the treatment effect estimator.

    2 May 2019

    Working paper graphic

    Testing for the presence of measurement error in Stata

    Working Paper

    In this paper, we describe how to test for the presence of measurement error in explanatory variables. First, we discuss the test of such hypotheses in parametric models such as linear regressions and then introduce a new Stata command [R] dgmtest for a nonparametric test proposed in Wilhelm (2018b).

    28 August 2018

    Working paper graphic

    Testing for the presence of measurement error

    Working Paper

    This paper proposes a simple nonparametric test of the hypothesis of no measurement error in explanatory variables and of the hypothesis that measurement error, if there is any, does not distort a given object of interest. We show that, under weak assumptions, both of these hypotheses are equivalent to certain restrictions on the joint distribution of an observable outcome and two observable variables that are related to the latent explanatory variable.

    19 July 2018

    Working paper graphic

    An adaptive test of stochastic monotonicity

    Working Paper

    We propose a new nonparametric test of stochastic monotonicity which adapts to the unknown smoothness of the conditional distribution of interest, possesses desirable asymptotic properties, is conceptually easy to implement, and computationally attractive.

    3 April 2018

    Working paper graphic

    Nonparametric instrumental variable estimation

    Working Paper

    This paper introduces Stata commands [R] npivreg and [R] npivregcv, which implement nonparametric instrumental variable (NPIV) estimation methods without and with a cross-validated choice of tuning parameters, respectively.

    30 October 2017

    Working paper graphic

    Optimal data collection for randomized control trials

    Working Paper

    In a randomized control trial, the precision of an average treatment effect estimator and the power of the corresponding t-test can be improved either by collecting data on additional individuals, or by collecting additional covariates that predict the outcome variable. We propose the use of pre-experimental data such as other similar studies, a census, or a household survey, to inform the choice of both the sample size and the covariates to be collected.

    23 October 2017