# David M. Kaplan

## Associate Professor, Economics

## University of Missouri

## kaplandm@missouri.edu

## Office #227 Professional Bldg

## Google Scholar profile

# Slides from Some Talks

Email me for slides from unlisted talks.

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

# In-page Links to Paper Details

Inference on Consensus Ranking of Distributions

*k*-class IV quantile regression

sivqr: Smoothed IV quantile regression in Stata

Comparing Latent Inequality with Ordinal Data

High-order coverage of smoothed Bayesian bootstrap intervals for population quantiles
Unbiased Estimation as a Public Good

Frequentist properties of Bayesian inequality tests (*JoE*)

Comparing distributions by multiple testing (*JoE*)

distcomp: Comparing distributions (*Stata Journal*)

Smoothed estimating equations for IV quantile regression (*ET*)

Smoothed GMM for quantile models (*JoE*)

Inference on (conditional) quantile differences and interquantile ranges (*EctJ*)

Fractional order statistic quantile inference (*JoE*)

Quantile inference by fixed-smoothing asymptotics and Edgeworth expansion (*JoE*)

A computational approach to style in American poetry (*Int'l Conf Data Mining*)

# 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 IV quantile regression

2021

(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 http://faculty.missouri.edu/kaplandm`

and follow instructions for installation (and email me if you have problems)

## 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 | replication | .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

## Unbiased Estimation as a Public Good

2019, *rejected*

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.

# Publications

## Frequentist properties of Bayesian inequality tests

2021, *Journal of Econometrics*

(with Longhao Zhuo)

published | accepted | WP | 2015 WP | code | .bib

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

## distcomp: Comparing distributions

2019, *Stata Journal*

In Stata: issue command

`net from http://faculty.missouri.edu/kaplandm`

and follow instructions for installation (and email me if you have problems)

## Smoothed GMM for quantile models

2019, *Journal of Econometrics*

(with Luciano de Castro, Antonio Galvao, and Xin Liu)

published | accepted | code | .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 | accepted | supplement | 2016 WP | 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.

In Stata: issue command

`net from http://faculty.missouri.edu/kaplandm`

and follow instructions for installation (and email me if you have problems)
*Stata Journal* draft

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

2018, *Econometrics Journal*

(with Matt Goldman)

2018 Denis Sargan Econometrics Prize

Selected for virtual issue on The Econometrics of Treatment Effects)

published | free/view-only | accepted | supplement | replication | code:unconditional | code:conditional | .bib | dissertation video

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 | 2016 WP | supplement | code:unconditional | code:conditional | simulations | more sims | examples | .bib | dissertation video

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 | Matlab:estimator | 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, Optimal Smoothing in Divide-and-Conquer for Big Data

paper
| code
| .bib

2014, Nonparametric inference on quantile marginal effects

paper
| code
| simulations
| example
| .bib

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:

projects from my 2nd year in grad school

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.

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