headshot photo

David M. Kaplan


Associate Professor, Economics

University of Missouri

kaplandm@missouri.edu

Office: moving buildings soon

Google Scholar profile

Slides from Some Talks


For additional talks, browse the directory or email me.

ES meetings 2021 | UC Santa Cruz 2021 | Chicago 2020 | BU 2020 | Yale 2019 | MEG 2018 | UCONN 2018 poster .pdf .tex .sty | Duke 2018 | NASMES 2017 | MEG 2016 | KSU 2015 | ICDM 2007

Working Papers

Inference on Consensus Ranking of Distributions

2020, submitted

paper | code/tex/etc. | more examples | .bib

Learn from data about the set of utility functions for which one distribution is preferred over another (higher expected utility). More informative than all-or-nothing test of unanimous agreement (stochastic dominance); different economic interpretation than CDF-based restricted stochastic dominance.

k-Class Instrumental Variables Quantile Regression

2021, submitted
(with Xin Liu)

paper | code/tex/etc. | .bib

Applying k-class estimation to IVQR can reliably reduce median bias for certain choices of k.

sivqr: Smoothed IV quantile regression (in Stata)

2020, submitted

paper | code/tex/etc. | .bib

New Stata command; implements Kaplan and Sun (2017)

In Stata: issue command
net from https://kaplandm.github.io/stata
and follow instructions for installation (and email me if you have problems). Note: older Stata versions do not support https, in which case you can download the "code/tex/etc." .zip file above.

Comparing Latent Inequality with Ordinal Data

2020, submitted
(with Longhao Zhuo)

paper | code/tex/etc. | .bib

New methods to compare two latent distributions (better? more dispersed?) when only ordinal data are available, without unrealistic assumptions.

High-order coverage of smoothed Bayesian bootstrap intervals for population quantiles

2020, submitted
(with Lonnie Hofmann)

paper | code/tex/etc. | .bib

Motivating further study in other settings, some results for continuity-corrected Bayesian bootstrap (Banks, 1988) confidence intervals for population quantiles:
(very) high-order accurate · exact in special cases · no smoothing parameter required

Relationship Estimators

2021

paper | code/tex/etc. | .bib

Highlighting the difference between "my estimated parameter satisfies the relationship" and "I estimate that the relationship holds."

Publications


Browse the directory of all draft PDFs, replication .zip files, etc.

Frequentist properties of Bayesian inequality tests

2021, Journal of Econometrics
(with Longhao Zhuo)

published | accepted | code/tex/etc. | .bib

Characterizes Bayesian and frequentist differences on general inequality hypotheses, even when credible/confidence sets coincide.

distcomp: Comparing distributions

2019, Stata Journal

published | accepted | code/tex/etc. | .bib

In Stata: issue command
net from https://kaplandm.github.io/stata
and follow instructions for installation (and email me if you have problems). Note: older Stata versions do not support https, in which case you can download the "code/tex/etc." .zip file above.

Smoothed GMM for quantile models

2019, Journal of Econometrics
(with Luciano de Castro, Antonio Galvao, and Xin Liu)

published | accepted | code/tex/etc. | .bib

Extends smoothed IVQR estimation (Kaplan and Sun, 2017) to non-iid data, nonlinear and over-identified models. Quantile Euler equation empirical example.

Comparing distributions by multiple testing across quantiles or CDF values

2018, Journal of Econometrics
(with Matt Goldman)

published | Stata code/article | accepted | supplement | code | replication | .bib

Where do two distributions differ? A new method to strongly control FWER while distributing power more evenly than the KS. One-sample and two-sample; stepdown and pre-test refinements. Extension to conditional distributions and regression discontinuity.

Nonparametric inference on (conditional) quantile differences and linear combinations, using L-statistics

2018, Econometrics Journal
(with Matt Goldman)
2018 Denis Sargan Econometrics Prize
Virtual issue on The Econometrics of Treatment Effects

published | accepted | supplement | replication | code:unconditional | code:conditional | .bib

Nonparametric, high-order accurate CIs for: quantile differences between two populations (which are QTEs under certain assumptions); interquantile ranges; more general linear combinations of quantiles (and differences thereof); and conditional (on X) versions of each.

Fractional order statistic approximation for nonparametric conditional quantile inference

2017, Journal of Econometrics
(with Matt Goldman)

published | accepted | supplement | code:unconditional | code:conditional | simulations | more sims | examples | .bib

Nonparametric CIs for quantiles and conditional quantiles, with high-order accuracy.

Smoothed estimating equations for instrumental variables quantile regression

2017, Econometric Theory
(with Yixiao Sun)

published | accepted | R:estimator | replication | .bib

IV quantile regression: smoothing improves computation and high-order properties.
See also sivqr Stata command/paper

Improved quantile inference via fixed-smoothing asymptotics and Edgeworth expansion

2015, Journal of Econometrics

published | accepted | appendix 1 | appendix 2 | simulations | empirical | R code | R examples | MATLAB code | MATLAB examples | .bib

Studentized sample quantile: fixed-smoothing asymptotics is more accurate and suggests an "inference-optimal" bandwidth to maximize accuracy; practical advantage biggest near tails.

A computational approach to style in American poetry

2007, International Conference on Data Mining (ICDM)
(with David Blei)

published | longer draft | slides | code/app | .bib

Analyzing poetic texts: extracting orthographic, syntactic, and phonemic features. Visualizing and comparing poems in the corresponding vector space of features. ("One of the most thorough and sophisticated computing analysis of poems to date" rave Wang and Yang, 2015.)

Resting and Superseded Projects


2020, Assessing Policy Effects with Unconditional Quantile Regression
I (eventually) noticed this was essentially the same as Proposition 1 of Rothe (2010).

2019, Unbiased Estimation as a Public Good
paper | code/tex/etc. | .bib
Under certain conditions, an estimator's bias is relatively more important (compared to its variance) when contributing to the public body of scientific knowledge than for a single estimate.
(I received many helpful ideas and references from referees that I enjoyed reading and considering, but I'm not sure if/when I'll get back to this project.)

2019, Optimal Smoothing in Divide-and-Conquer for Big Data
My main point is not new; for example, see "Related work" on the second page of Xu, Shang, and Cheng (2018), "Optimal Tuning for Divide-and-conquer Kernel Ridge Regression with Massive Data".

2014, Nonparametric inference on quantile marginal effects
paper | code | simulations | example | .bib
It works well enough, but honestly I'd just bootstrap it instead of using my method.

2013, IDEAL inference on conditional quantiles: superseded by above papers "Fractional order statistic approximation for nonparametric conditional quantile inference" and "Nonparametric inference on conditional quantile treatment effects using L-statistics"

2013, IDEAL quantile inference via interpolated duals of exact analytic L-statistics: superseded by above papers "Fractional order statistic approximation for nonparametric conditional quantile inference" and "Nonparametric inference on conditional quantile treatment effects using L-statistics"

2011, Fixed-smoothing asymptotics and accurate F approximation using vector autoregressive variance matrix estimator (with Yixiao Sun)
paper | .bib

Experiencing technical difficulties (error in proofs).

For curious grad students:
work I did in grad school


2009, summer research report examining data-dependent methods for sieve size selection in nonparametric IV estimation.

2010, Natural disasters and differential household effects: evidence from the May 2006 Java earthquake
paper | slides | .bib

Were poorer households hurt more? Examining direct and indirect mechanisms.

2013, dissertation and dissertation defense