4,243 research outputs found
Inference for extremal conditional quantile models, with an application to market and birthweight risks
Quantile regression is an increasingly important empirical tool in economics and other sciences for analyzing the impact of a set of regressors on the conditional distribution of an outcome. Extremal quantile regression, or quantile regression applied to the tails, is of interest in many economic and financial applications, such as conditional value-at-risk, production efficiency, and adjustment bands in (S,s) models. In this paper we provide feasible inference tools for extremal conditional quantile models that rely upon extreme value approximations to the distribution of self-normalized quantile regression statistics. The methods are simple to implement and can be of independent interest even in the non-regression case. We illustrate the results with two empirical examples analyzing extreme fluctuations of a stock return and extremely low percentiles of live infants' birthweights in the range between 250 and 1500 grams.
Nonseparable Multinomial Choice Models in Cross-Section and Panel Data
Multinomial choice models are fundamental for empirical modeling of economic
choices among discrete alternatives. We analyze identification of binary and
multinomial choice models when the choice utilities are nonseparable in
observed attributes and multidimensional unobserved heterogeneity with
cross-section and panel data. We show that derivatives of choice probabilities
with respect to continuous attributes are weighted averages of utility
derivatives in cross-section models with exogenous heterogeneity. In the
special case of random coefficient models with an independent additive effect,
we further characterize that the probability derivative at zero is proportional
to the population mean of the coefficients. We extend the identification
results to models with endogenous heterogeneity using either a control function
or panel data. In time stationary panel models with two periods, we find that
differences over time of derivatives of choice probabilities identify utility
derivatives "on the diagonal," i.e. when the observed attributes take the same
values in the two periods. We also show that time stationarity does not
identify structural derivatives "off the diagonal" both in continuous and
multinomial choice panel models.Comment: 23 page
The sorted effects method: discovering heterogeneous effects beyond their averages
Supplemental Data & Programs are available here: https://hdl.handle.net/2144/34409The partial (ceteris paribus) effects of interest in nonlinear and interactive linear models are heterogeneous as they can vary dramatically with the underlying observed or unobserved covariates. Despite the apparent importance of heterogeneity, a common practice in modern empirical work is to largely ignore it by reporting average partial effects (or, at best, average effects for some groups). While average effects provide very convenient scalar summaries of typical effects, by definition they fail to reflect the entire variety of the heterogeneous effects. In order to discover these effects much more fully, we propose to estimate and report sorted effects -- a collection of estimated partial effects sorted in increasing order and indexed by percentiles. By construction the sorted effect curves completely represent and help visualize the range of the heterogeneous effects in one plot. They are as convenient and easy to report in practice as the conventional average partial effects. They also serve as a basis for classification analysis, where we divide the observational units into most or least affected groups and summarize their characteristics. We provide a quantification of uncertainty (standard errors and confidence bands) for the estimated sorted effects and related classification analysis, and provide confidence sets for the most and least affected groups. The derived statistical results rely on establishing key, new mathematical results on Hadamard differentiability of a multivariate sorting operator and a related classification operator, which are of independent interest. We apply the sorted effects method and classification analysis to demonstrate several striking patterns in the gender wage gap.https://arxiv.org/abs/1512.05635Accepted manuscrip
Distribution Regression with Sample Selection, with an Application to Wage Decompositions in the UK
We develop a distribution regression model under endogenous sample selection.
This model is a semiparametric generalization of the Heckman selection model
that accommodates much richer patterns of heterogeneity in the selection
process and effect of the covariates. The model applies to continuous, discrete
and mixed outcomes. We study the identification of the model, and develop a
computationally attractive two-step method to estimate the model parameters,
where the first step is a probit regression for the selection equation and the
second step consists of multiple distribution regressions with selection
corrections for the outcome equation. We construct estimators of functionals of
interest such as actual and counterfactual distributions of latent and observed
outcomes via plug-in rule. We derive functional central limit theorems for all
the estimators and show the validity of multiplier bootstrap to carry out
functional inference. We apply the methods to wage decompositions in the UK
using new data. Here we decompose the difference between the male and female
wage distributions into four effects: composition, wage structure, selection
structure and selection sorting. After controlling for endogenous employment
selection, we still find substantial gender wage gap -- ranging from 21% to 40%
throughout the (latent) offered wage distribution that is not explained by
observable labor market characteristics. We also uncover positive sorting for
single men and negative sorting for married women that accounts for a
substantive fraction of the gender wage gap at the top of the distribution.
These findings can be interpreted as evidence of assortative matching in the
marriage market and glass-ceiling in the labor market.Comment: 72 pages, 4 tables, 39 figures, includes supplement with additional
empirical result
PVT-Robust CMOS Programmable Chaotic Oscillator: Synchronization of Two 7-Scroll Attractors
Designing chaotic oscillators using complementary metal-oxide-semiconductor (CMOS) integrated circuit technology for generating multi-scroll attractors has been a challenge. That way, we introduce a current-mode piecewise-linear (PWL) function based on CMOS cells that allow programmable generation of 2–7-scroll chaotic attractors. The mathematical model of the chaotic oscillator designed herein has four coefficients and a PWL function, which can be varied to provide a high value of the maximum Lyapunov exponent. The coefficients are implemented electronically by designing operational transconductance amplifiers that allow programmability of their transconductances. Design simulations of the chaotic oscillator are provided for the 0.35μ m CMOS technology. Post-layout and process–voltage–temperature (PVT) variation simulations demonstrate robustness of the multi-scroll chaotic attractors. Finally, we highlight the synchronization of two seven-scroll attractors in a master–slave topology by generalized Hamiltonian forms and observer approach. Simulation results show that the synchronized CMOS chaotic oscillators are robust to PVT variations and are suitable for chaotic secure communication applications.Universidad Autónoma de Tlaxcala CACyPI-UATx-2017Program to Strengthen Quality in Educational Institutions C/PFCE-2016-29MSU0013Y-07-23National Council for Science and Technology 237991 22284
quantreg. nonpar: An R Package for performing nonparametric series quantile regression
The R package quantreg.nonpar implements nonparametric quantile regression methods to estimate and make inference on partially linear quantile models. quantreg.nonpar obtains point estimates of the conditional quantile function and its derivatives based on series approximations to the nonparametric part of the model. It also provides pointwise and uniform confidence intervals over a region of covariate values and/or quantile indices for the same functions using analytical and resampling methods. This paper serves as an introduction to the package and displays basic functionality of the functions contained within.https://arxiv.org/abs/1610.08329Published and Accepted manuscript versions
Generic inference on quantile and quantile effect functions for discrete outcomes
Quantile and quantile effect functions are important tools for descriptive and inferential analysis due to their natural and intuitive interpretation. Existing inference methods for these functions do not apply to discrete random variables. This paper offers a simple, practical construction of simultaneous confidence bands for quantile and quantile effect functions of possibly discrete random variables. It is based on a natural transformation of simultaneous confidence bands for distribution functions, which are readily available for many problems. The construction is generic and does not depend on the nature of the underlying problem. It works in conjunction with parametric, semiparametric, and nonparametric modeling strategies and does not depend on the sampling scheme. We apply our method to characterize the distributional impact of insurance coverage on health care utilization and obtain the distributional decomposition of the racial test score gap. Our analysis generates new, interesting empirical findings, and complements previous analyses that focused on mean effects only. In both applications, the outcomes of interest are discrete rendering existing inference methods invalid for obtaining uniform confidence bands for quantile and quantile effects functions.https://arxiv.org/abs/1608.05142First author draf
quantreg. nonpar: An R Package for performing nonparametric series quantile regression
The R package quantreg.nonpar implements nonparametric quantile regression methods to estimate and make inference on partially linear quantile models. quantreg.nonpar obtains point estimates of the conditional quantile function and its derivatives based on series approximations to the nonparametric part of the model. It also provides pointwise and uniform confidence intervals over a region of covariate values and/or quantile indices for the same functions using analytical and resampling methods. This paper serves as an introduction to the package and displays basic functionality of the functions contained within.https://arxiv.org/abs/1610.08329Published and Accepted manuscript versions
Program Evaluation and Causal Inference with High-Dimensional Data
In this paper, we provide efficient estimators and honest confidence bands
for a variety of treatment effects including local average (LATE) and local
quantile treatment effects (LQTE) in data-rich environments. We can handle very
many control variables, endogenous receipt of treatment, heterogeneous
treatment effects, and function-valued outcomes. Our framework covers the
special case of exogenous receipt of treatment, either conditional on controls
or unconditionally as in randomized control trials. In the latter case, our
approach produces efficient estimators and honest bands for (functional)
average treatment effects (ATE) and quantile treatment effects (QTE). To make
informative inference possible, we assume that key reduced form predictive
relationships are approximately sparse. This assumption allows the use of
regularization and selection methods to estimate those relations, and we
provide methods for post-regularization and post-selection inference that are
uniformly valid (honest) across a wide-range of models. We show that a key
ingredient enabling honest inference is the use of orthogonal or doubly robust
moment conditions in estimating certain reduced form functional parameters. We
illustrate the use of the proposed methods with an application to estimating
the effect of 401(k) eligibility and participation on accumulated assets.Comment: 118 pages, 3 tables, 11 figures, includes supplementary appendix.
This version corrects some typos in Example 2 of the published versio
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