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1. Asymptotic inference for the constrained quantile regression process

Author:
Parker, Thomas
Source:
Journal of econometrics 2019 v.213 no.1 pp. 174-189
ISSN:
0304-4076
Subject:
econometric models; economic analysis; economic theory; regression analysis; stochastic processes
Abstract:
... I investigate the asymptotic distribution of linear quantile regression coefficient estimates when the parameter lies on the boundary of the parameter space. In order to allow for inferences made across many conditional quantiles, I provide a uniform characterization of constrained quantile regression estimates as a stochastic process over an interval of quantile levels. To do this I pose the proc ...
DOI:
10.1016/j.jeconom.2019.04.010

2. Conditional quantile processes based on series or many regressors

Author:
Belloni, Alexandre; Chernozhukov, Victor; Chetverikov, Denis; Fernández-Val, Iván
Source:
Journal of econometrics 2019 v.213 no.1 pp. 4-29
ISSN:
0304-4076
Subject:
byproducts; demand elasticities; econometric models; economic theory; energy use and consumption; gasoline; mathematical theory; normal distribution; regression analysis
Abstract:
... Quantile regression (QR) is a principal regression method for analyzing the impact of covariates on outcomes. The impact is described by the conditional quantile function and its functionals. In this paper we develop the nonparametric QR-series framework, covering many regressors as a special case, for performing inference on the entire conditional quantile function and its linear functionals. In ...
DOI:
10.1016/j.jeconom.2019.04.003

3. Edgeworth’s time series model: Not AR(1) but same covariance structure

Author:
Portnoy, Stephen
Source:
Journal of econometrics 2019 v.213 no.1 pp. 281-288
ISSN:
0304-4076
Subject:
covariance; econometric models; economic analysis; economic theory; time series analysis
Abstract:
... In an 1886 paper, Edgeworth developed a method for simulating time series processes with substantial dependence. A version of this process with normal errors has the same means and covariance structure as an AR(1) process, but is actually a mixture of a very large number of processes, some of which are not stationary. That is, joint distributions of lag 3 or greater are not normal but are mixtures ...
DOI:
10.1016/j.jeconom.2019.04.015

4. On the predictive risk in misspecified quantile regression

Author:
Giessing, Alexander; He, Xuming
Source:
Journal of econometrics 2019 v.213 no.1 pp. 235-260
ISSN:
0304-4076
Subject:
econometric models; economic analysis; economic theory; regression analysis; risk
Abstract:
... In the present paper we investigate the predictive risk of possibly misspecified quantile regression functions. The in-sample risk is well-known to be an overly optimistic estimate of the predictive risk and we provide two relatively simple (asymptotic) characterizations of the associated bias, also called expected optimism. We propose estimates for the expected optimism and the predictive risk, a ...
DOI:
10.1016/j.jeconom.2019.04.013

5. Panel data quantile regression with grouped fixed effects

Author:
Gu, Jiaying; Volgushev, Stanislav
Source:
Journal of econometrics 2019 v.213 no.1 pp. 68-91
ISSN:
0304-4076
Subject:
econometric models; economic analysis; economic theory; regression analysis; system optimization
Abstract:
... This paper introduces estimation methods for grouped latent heterogeneity in panel data quantile regression. We assume that the observed individuals come from a heterogeneous population with a finite number of types. The number of types and group membership is not assumed to be known in advance and is estimated by means of a convex optimization problem. We provide conditions under which group memb ...
DOI:
10.1016/j.jeconom.2019.04.006

6. Partial identification of the treatment effect distribution and its functionals

Author:
Firpo, Sergio; Ridder, Geert
Source:
Journal of econometrics 2019 v.213 no.1 pp. 210-234
ISSN:
0304-4076
Subject:
cumulative distribution; econometric models; economic analysis; economic theory
Abstract:
... In the treatment effect problem, the available information is on the marginal distributions of potential outcomes, but not on their joint distribution. The only point identified functional of the treatment effect distribution is its average, the average treatment effect (ATE). Quantiles and other functionals of the distribution of treatment effect are only partially identified. Bounds on a single ...
DOI:
10.1016/j.jeconom.2019.04.012

7. Penalized sieve GEL for weighted average derivatives of nonparametric quantile IV regressions

Author:
Chen, Xiaohong; Pouzo, Demian; Powell, James L.
Source:
Journal of econometrics 2019 v.213 no.1 pp. 30-53
ISSN:
0304-4076
Subject:
econometric models; economic analysis; economic theory
Abstract:
... This paper considers estimation and inference for a weighted average derivative (WAD) of a nonparametric quantile instrumental variables regression (NPQIV). NPQIV is a non-separable and nonlinear ill-posed inverse problem, which might be why there is no published work on the asymptotic properties of any estimator of its WAD. We first characterize the semiparametric efficiency bound for a WAD of a ...
DOI:
10.1016/j.jeconom.2019.04.004

8. Placebo inference on treatment effects when the number of clusters is small

Author:
Hagemann, Andreas
Source:
Journal of econometrics 2019 v.213 no.1 pp. 190-209
ISSN:
0304-4076
Subject:
econometric models; economic analysis; economic theory; least squares; placebos
Abstract:
... I introduce a general, Fisher-style randomization testing framework to conduct nearly exact inference about the lack of effect of a binary treatment in the presence of very few, large clusters when the treatment effect is identified across clusters. The proposed randomization test formalizes and extends the intuitive notion of generating null distributions by assigning placebo treatments to untrea ...
DOI:
10.1016/j.jeconom.2019.04.011

9. Predictive quantile regressions under persistence and conditional heteroskedasticity

Author:
Fan, Rui; Lee, Ji Hyung
Source:
Journal of econometrics 2019 v.213 no.1 pp. 261-280
ISSN:
0304-4076
Subject:
Monte Carlo method; confidence interval; econometric models; economic analysis; economic theory; heteroskedasticity; regression analysis
Abstract:
... This paper provides an improved inference for predictive quantile regressions with persistent predictors and conditionally heteroskedastic errors. The confidence intervals based on conventional quantile regression techniques are not valid when predictors are highly persistent. Moreover, the conditional heteroskedasticity introduces rather complicated nuisance parameters in the limit theory, whose ...
DOI:
10.1016/j.jeconom.2019.04.014

10. Quantile-regression-based clustering for panel data

Author:
Zhang, Yingying; Wang, Huixia Judy; Zhu, Zhongyi
Source:
Journal of econometrics 2019 v.213 no.1 pp. 54-67
ISSN:
0304-4076
Subject:
algorithms; econometric models; economic analysis; economic development; economic theory
Abstract:
... In panel data analysis, it is important to identify subgroups of units with heterogeneous parameters. This can not only increase the model flexibility but also produce more efficient estimation by pooling information across units within the same group. In this paper, we propose a new quantile-regression-based clustering method for panel data. We develop an iterative algorithm using a similar idea ...
DOI:
10.1016/j.jeconom.2019.04.005

11. Quantiles via moments

Author:
Machado, José A.F.; Santos Silva, J.M.C.
Source:
Journal of econometrics 2019 v.213 no.1 pp. 145-173
ISSN:
0304-4076
Subject:
econometric models; economic analysis; economic theory; regression analysis
Abstract:
... We study the conditions under which it is possible to estimate regression quantiles by estimating conditional means. The advantage of this approach is that it allows the use of methods that are only valid in the estimation of conditional means, while still providing information on how the regressors affect the entire conditional distribution. The methods we propose are not meant to replace the wel ...
DOI:
10.1016/j.jeconom.2019.04.009

12. Review of median stable distributions and Schröder’s equation

Author:
Bassett, Gib
Source:
Journal of econometrics 2019 v.213 no.1 pp. 289-295
ISSN:
0304-4076
Subject:
econometric models; economic analysis; economic theory; equations; systems analysis
Abstract:
... Median stable distributions are an extension of traditional (mean) stable distributions. The traditional definition of stability (in terms of sums of iid random variables) is recast as a condition on the sampling distribution of an estimator. For the traditional (mean) stable distribution, the sample mean’s (rescaled) sampling distribution is identical to the distribution of the iid data. Median s ...
DOI:
10.1016/j.jeconom.2019.04.016

13. Smoothed GMM for quantile models

Author:
de Castro, Luciano; Galvao, Antonio F.; Kaplan, David M.; Liu, Xin
Source:
Journal of econometrics 2019 v.213 no.1 pp. 121-144
ISSN:
0304-4076
Subject:
econometric models; economic analysis; economic theory; equations; regression analysis; risk
Abstract:
... We consider estimation of finite-dimensional parameters identified by general conditional quantile restrictions, including instrumental variables quantile regression. Within a generalized method of moments framework, moment functions are smoothed to aid both computation and precision. Consistency and asymptotic normality are established under weaker assumptions than previously seen in the literatu ...
DOI:
10.1016/j.jeconom.2019.04.008

14. What do mean impacts miss? Distributional effects of corporate diversification

Author:
Xiao, Zhijie; Xu, Lan
Source:
Journal of econometrics 2019 v.213 no.1 pp. 92-120
ISSN:
0304-4076
Subject:
econometric models; economic analysis; economic theory; empirical research; finance; regression analysis; stock exchange; United States
Abstract:
... Corporate diversification is one of the most debated topics in finance over the past two decades. While it is widely believed that there exists a discount in the stock market valuation of conglomerate firms, the extant research based on least squares methods points to different directions. We argue that the existing empirical analyses ignore some important data features, especially cross sectional ...
DOI:
10.1016/j.jeconom.2019.04.007

15. Accelerating score-driven time series models

Author:
Blasques, F.; Gorgi, P.; Koopman, S.J.
Source:
Journal of econometrics 2019 v.212 no.2 pp. 359-376
ISSN:
0304-4076
Subject:
autocorrelation; econometric models; economic analysis; economic theory; empirical research; equations; heteroskedasticity; inflation; time series analysis; United States
Abstract:
... We propose a new class of score-driven time series models that allows for a more flexible weighting of score innovations for the filtering of time varying parameters. The parameter for the score innovation is made time-varying by means of an updating equation that accounts for the autocorrelations of past innovations. We provide the theoretical foundations for this acceleration method by showing o ...
DOI:
10.1016/j.jeconom.2019.03.005

16. A diagnostic criterion for approximate factor structure

Author:
Gagliardini, Patrick; Ossola, Elisa; Scaillet, Olivier
Source:
Journal of econometrics 2019 v.212 no.2 pp. 503-521
ISSN:
0304-4076
Subject:
data collection; econometric models; economic analysis; economic theory; empirical research; macroeconomics; markets; United States
Abstract:
... We build a simple diagnostic criterion for approximate factor structure in large panel datasets. Given observable factors, the criterion checks whether the errors are weakly cross-sectionally correlated or share at least one unobservable common factor (interactive effects). A general version allows to determine the number of omitted common factors also for time-varying structures. The empirical an ...
DOI:
10.1016/j.jeconom.2019.06.001

17. A moment-based notion of time dependence for functional time series

Author:
Salish, Nazarii; Gleim, Alexander
Source:
Journal of econometrics 2019 v.212 no.2 pp. 377-392
ISSN:
0304-4076
Subject:
covariance; economic analysis; economic theory; time series analysis
Abstract:
... This paper addresses the fundamental topic of time dependence for time series when data points are given as functions. We construct a notion of time dependence through the projections on the basis system extracted from the principal components of normalized sums. This allows us to adapt various scalar time series techniques to the functional data context. In particular, we define dependence based ...
DOI:
10.1016/j.jeconom.2019.03.007

18. Asymptotic theory and wild bootstrap inference with clustered errors

Author:
Djogbenou, Antoine A.; MacKinnon, James G.; Nielsen, Morten Ørregaard
Source:
Journal of econometrics 2019 v.212 no.2 pp. 393-412
ISSN:
0304-4076
Subject:
confidence interval; econometric models; economic analysis; economic theory; mathematical theory; regression analysis; variance
Abstract:
... We study inference based on cluster-robust variance estimators for regression models with clustered errors, focusing on the wild cluster bootstrap. We state conditions under which asymptotic and bootstrap tests and confidence intervals are asymptotically valid. These conditions put limits on the rates at which the cluster sizes can increase as the number of clusters tends to infinity. We also deri ...
DOI:
10.1016/j.jeconom.2019.04.035

19. Identification and wavelet estimation of weighted ATE under discontinuous and kink incentive assignment mechanisms

Author:
Chen, Heng; Fan, Yanqin
Source:
Journal of econometrics 2019 v.212 no.2 pp. 476-502
ISSN:
0304-4076
Subject:
econometric models; economic analysis; economic theory; wavelet
Abstract:
... This paper studies identification, estimation, and inference of a weighted average treatment effect (W-ATE) parameter in a class of switching regime models, where the agent’s selection of treatment is affected by either a discontinuous or kink incentive assignment mechanism and some unobservable characteristic. For each assignment mechanism, we (i) establish identification and propose a local wave ...
DOI:
10.1016/j.jeconom.2019.05.015

20. Instrumental variables and the sign of the average treatment effect

Author:
Machado, Cecilia; Shaikh, Azeem M.; Vytlacil, Edward J.
Source:
Journal of econometrics 2019 v.212 no.2 pp. 522-555
ISSN:
0304-4076
Subject:
econometric models; economic analysis; economic theory; equations
Abstract:
... This paper considers identification and inference about the sign of the average effect of a binary endogenous regressor (or treatment) on a binary outcome of interest when a binary instrument is available. In this setting, the average effect of the endogenous regressor on the outcome is sometimes referred to as the average treatment effect (ATE). We consider four different sets of assumptions: ins ...
DOI:
10.1016/j.jeconom.2018.04.007