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 (eventually) noticed this was essentially the same as Proposition 1 of Rothe (2010).Ģ019, Unbiased Estimation as a Public Good Highlighting the difference between "my estimated parameter satisfies the relationship" and "I estimate that the relationship holds."Ģ020, Assessing Policy Effects with Unconditional Quantile Regression ("One of the most thorough and sophisticated computing analysis of poems to date" rave Wang and Yang, 2015.) Visualizing and comparing poems in the corresponding vector space of features. Studentized sample quantile: fixed-smoothing asymptotics is more accurate and suggests an "inference-optimal" bandwidth to maximize accuracy practical advantage biggest near tails.Ī computational approach to style in American poetryĢ007, International Conference on Data Mining (ICDM) (with David Blei)Īnalyzing poetic texts: extracting orthographic, syntactic, and phonemic features. See also sivqr Stata command/paper Improved quantile inference via fixed-smoothing asymptotics and Edgeworth expansion IV quantile regression: smoothing improves computation and high-order properties. Smoothed estimating equations for instrumental variables quantile regressionĢ017, Econometric Theory (with Yixiao Sun) Nonparametric CIs for quantiles and conditional quantiles, with high-order accuracy. 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.įractional order statistic approximation for nonparametric conditional quantile inferenceĢ017, Journal of Econometrics (with Matt Goldman) Nonparametric inference on (conditional) quantile differences and linear combinations, using L-statisticsĢ018, Econometrics Journal (with Matt Goldman)Ģ018 Denis Sargan Econometrics Prize Virtual issue on The Econometrics of Treatment Effects One-sample and two-sample stepdown and pre-test refinements.Įxtension to conditional distributions and regression discontinuity.
Quantile Euler equation empirical example.Ĭomparing distributions by multiple testing across quantiles or CDF valuesĢ018, Journal of Econometrics (with Matt Goldman)Ī new method to strongly control FWER while distributing power more evenly than the KS.
#Texlive 2018 install zip
zip files, etc.įrequentist properties of Bayesian inequality testsĢ021, Journal of Econometrics (with Longhao Zhuo)Ĭharacterizes Bayesian and frequentist differences on general inequality hypotheses, even when credible/confidence sets coincide.Ģ019, Journal of Econometrics (with Luciano de Castro, Antonio Galvao, and Xin Liu)Įxtends smoothed IVQR estimation (Kaplan and Sun, 2017) to non-iid data, nonlinear and over-identified models. Motivating further study in other settings, some results for continuity-corrected Bayesian bootstrap ( Banks, 1988) confidence intervals for population quantiles:īrowse the directory of all draft PDFs, replication. High-order coverage of smoothed Bayesian bootstrap intervals for population quantilesĢ019/2021, submitted (with Lonnie Hofmann) New methods to compare two latent distributions (better? more dispersed?) when only ordinal data are available, without unrealistic assumptions.
#Texlive 2018 install zip file
zip file above.Ĭomparing Latent Inequality with Ordinal Data
#Texlive 2018 install download
Note: older Stata versions do not support https, in which case you can download the "code/tex/etc.".
New Stata command implements Kaplan and Sun (2017)Īnd follow instructions for installation (and email me if you have problems). Sivqr: Smoothed IV quantile regression (in Stata) K-Class Instrumental Variables Quantile RegressionĪpplying k-class estimation to IVQR can reliably reduce median bias for certain choices of k. More informative than all-or-nothing test of unanimous agreement (stochastic dominance) ĭifferent economic interpretation than CDF-based restricted stochastic dominance. Learn from data about the set of utility functions for which one distribution is preferred over another (higher expected utility). Inference on Consensus Ranking of Distributions
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