# David M. Kaplan

## Assistant Professor, Economics

## University of Missouri

## KaplanDM@missouri.edu

## Office #326 Professional Bldg

# Slides from Talks

## [UConn 2017] | [Brown 2017] | [ES NASM 2017] | [Iowa 2017] | [NY Camp 2017] | [Penn State 2017] | [MEG 2016] | [KSU 2015] | [MEG 2015] | [ESWC 2015] | [NIU 2014] | [MU Stats 2014] | [MEG 2013] | [NSF/CEME 2013] | [AMES 2013] | [Defense 2013] | [UCLA 2013] | [Job Mkt 2013] | [CA E'metrics 2012] | [UCSD 2012] | [ICDM 2007]

# Publications

## Goldman, M., and Kaplan, D. M. (2017).
Nonparametric inference on (conditional) quantile differences and linear combinations, using L-statistics.
*Econometrics Journal*, XX(X):XXX–XXX.

[paper:published]
| [paper:accepted]
| [supplement]
| [replication]
| [code:unconditional]
| [code:conditional]
| [simulations:QTE]
| [simulations:CQTE]
| [simulations:CIQR]
| [conditional examples]
| [unconditional examples]
| [.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.

## Goldman, M., and Kaplan, D. M. (2017).
Fractional order statistic approximation for nonparametric conditional quantile inference.
*Journal of Econometrics*, 196(2):331–346.

[paper:published]
| [paper:accepted]
| [paper:longer draft]
| [supplement]
| [code: unconditional]
| [code: conditional]
| [simulations]
| [more sims]
| [examples]
| [.bib]
| [dissertation video]

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

## Kaplan, D. M., and Sun, Yixiao (2017). Smoothed estimating equations for instrumental variables quantile regression. *Econometric Theory*, 33(1):105–157.

Copyright Cambridge University Press; Cambridge Journals Online version.

[paper:accepted]
| [paper:working]
| [Matlab:estimator]
| [R:estimator]
| [replication files for simulations and JTPA example]
| [.bib]

IV quantile regression: smoothing improves computation and high-order properties.

## Kaplan, D. M. (2015). Improved quantile inference via fixed-smoothing asymptotics and Edgeworth expansion. *Journal of Econometrics*, 185(1):20–32.

[paper:published]
| [paper:accepted]
| [paper:draft]
| [one-sample appendix]
| [two-sample appendix]
| [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.

## Kaplan, D. M. and Blei, David M. (2007). A computational approach to style in American poetry. In *Proceedings of 7th IEEE International Conference on Data Mining (ICDM 2007)*, pp. 553–558, Omaha, NE. IEEE Computer Society. Presented at conference.

[paper]
| [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" says Wang and Yang, 2015.)

# Working Papers

## 2017, Smoothed IV quantile regression, with estimation of quantile Euler equations (with Luciano de Castro and Antonio F. Galvao; *submitted*)

[paper]
| [code]
| [.bib]

IVQR is difficult to compute, so previous estimators required iid sampling (and usually linear models). We prove consistency and asymptotic normality of the smoothed Kaplan and Sun (2017, see above) estimator with weakly dependent data (and nonlinear models). The extensive empirical analysis considers quantile Euler equations derived from quantile utility maximization.

## 2017 (first 2013), Comparing distributions by multiple testing across quantiles or CDF values (with Matt Goldman; *submitted*)

[paper]
| [code]
| [replication]
| [.bib]

### Formerly known as: Evenly sensitive KS-type inference on distributions

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.

## 2017, Bayesian and frequentist nonlinear inequality tests (with Longhao Zhuo; *submitted*)

[paper]
| [code]
| [.bib]

### Formerly known as: Bayesian and frequentist tests of sign equality and other nonlinear inequalities

Bayesian and frequentist inference on nonlinear inequality hypotheses may differ greatly, even when credible and confidence sets coincide. The shape of the hypothesis matters. Theoretical insights, practical consequences, two economic examples (stochastic dominance, translog curvature).

# Resting and Superseded Projects

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.