Small Bandwidth Asymptotics for Density-Weighted Average Derivatives

Cattaneo, Matias D.; Crump, Richard K.; Jansson, Michael

doi:10.1017/s0266466613000169

Cited by 41 publications

(19 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the context of U-statistics it goes back to Hoeffding (1948). It is implicit in Holland and Leinhardt (1976) in their work on subgraph counts; see also the recent work on dyadic regression by Fafchamps and Gubert (2007), Cameron and Miller (2014) and Aronow et al (2017), as well as that on density weighted average derivatives by Cattaneo et al (2014). However, the small amount of extant formal limit theory for dyadic regression (cited earlier) suggests different approaches to variance estimation.…”

Section: Areas For Additional Researchmentioning

confidence: 99%

Sparse network asymptotics for logistic regression

Graham¹

2020

View full text Add to dashboard Cite

, web: http : //bryangraham.github.io/ econometrics/. Financial support from NSF Grant SES #1851647 is gratefully acknowledged. Some of the results contained in this paper were presented, albeit in more basic and preliminary forms, at an invited session of the 2018 Latin American Meetings of the Econometric Society, and at a plenary lecture of the 2019 meetings of the International Association of of Applied Econometrics. I am thankful to Michael Jansson for several very helpful conversations and to Konrad Menzel for feedback on the initial draft. All the usual disclaimers apply.

show abstract

Section: Areas For Additional Researchmentioning

confidence: 99%

Sparse network asymptotics for logistic regression

Graham¹

2020

View full text Add to dashboard Cite

show abstract

“…Although asymptotic linearity is satisfied by many econometric estimators, it can fail in certain semi-parametric problems (e.g., Cattaneo et al, 2014) and in problems involving model selection or shrinkage (e.g., Liao, 2013, Cheng andLiao, 2015), for example.…”

Section: Setupmentioning

confidence: 99%

“…Here H π is the usual parametric (projected) Hessian matrix, since Ω is the identity. Now, for given α and p values, let µ(α, p) be such that15 Pr (Z[µ(α, p)] > c α ) = p.…”

mentioning

confidence: 99%

Minimizing Sensitivity to Model Misspecification

Bonhomme¹,

Weidner²

2020

View full text Add to dashboard Cite

We propose a framework for estimation and inference when the model may be misspecified. We rely on a local asymptotic approach where the degree of misspecification is indexed by the sample size. We construct estimators whose mean squared error is minimax in a neighborhood of the reference model, based on simple one-step adjustments. In addition, we provide confidence intervals that contain the true parameter under local misspecification. To interpret the degree of misspecification, we map it to the local power of a specification test of the reference model. Our approach allows for systematic sensitivity analysis when the parameter of interest may be partially or irregularly identified. As illustrations, we study two binary choice models: a cross-sectional model where the error distribution is misspecified, and a dynamic panel data model where the number of time periods is small and the distribution of individual effects is misspecified.

show abstract

“…If, however, the bandwidth sequence h has Nh → C < ∞ (a "knife-edge" undersmoothing condition similar to one considered by Cattaneo et al (2014) in a different context), then both T 1 and T 3 will be asymptotically normal when normalized by √ N .…”

Section: Asymptotic Distribution Theorymentioning

confidence: 99%

Kernel density estimation for undirected dyadic data

Graham

Niu

Powell

2019

View full text Add to dashboard Cite

We study nonparametric estimation of density functions for undirected dyadic random variables (i.e., random variables defined for all n def ≡ N 2 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 that share one or two indices may be dependent, while those sharing no indices in common are independent. In this setting, we show that density functions may be estimated by an application of the kernel estimation method of Rosenblatt (1956) and Parzen (1962). We suggest an estimate of their asymptotic variances inspired by a combination of (i) Newey's (1994) method of variance estimation for kernel estimators in the "monadic" setting and (ii) a variance estimator for the (estimated) density of a simple network first suggested by Holland & Leinhardt (1976). More unusual are the rates of convergence and asymptotic (normal) distributions of our dyadic density estimates. Specifically, we show that they converge at the same rate as the (unconditional) dyadic sample mean: the square root of the number, N, of nodes. This differs from the results for nonparametric estimation of densities and regression functions for monadic data, which generally have a slower rate of convergence than their corresponding sample mean.

show abstract

Small Bandwidth Asymptotics for Density-Weighted Average Derivatives

Cited by 41 publications

References 42 publications

Sparse network asymptotics for logistic regression

Sparse network asymptotics for logistic regression

Minimizing Sensitivity to Model Misspecification

Kernel density estimation for undirected dyadic data

Contact Info

Product

Resources

About