Special Issue "Quantile Methods"

A special issue of Econometrics (ISSN 2225-1146).

Deadline for manuscript submissions: closed (30 April 2016)

Special Issue Editor

Guest Editor
Prof. Dr. Gabriel Montes-Rojas

Department of Economics, City University London, London EC1V 0HB, UK
Website | E-Mail
Interests: panel data; quantile regression; testing; econometric theory; development economics

Special Issue Information

Dear Colleagues,

Quantile regression is a useful tool to represent individual heterogeneity and to summarize statistical relationships among stochastic variables. This heterogeneity is analyzed using the conditional quantiles of a response variable of interest. The last decades have seen numerous developments in economics in which quantile methods played a key role, combined with important theoretical advances.

This Special Issue is open for submissions until 31 October 2015. All submitted articles will undergo rigorous peer review, and in the event of acceptance, are ensured rapid publication and high visibility.

This Special Issue within the open access journal Econometrics will cover a broad range of topics in relation to Quantile Methods, including, but not limited to:

  • treatment effects;
  • inequality and wage premia;
  • panel data quantile regression models;
  • financial econometrics applications of quantile regression (value-at-risk, asymmetry, threshold models);
  • computation issues of quantile regression;
  • endogeneity and measurement errors;
  • multivariate quantiles;
  • nonparametric quantile estimation;
  • quasi-maximum likelihood methods to estimate quantiles.

Gabriel Montes-Rojas
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Econometrics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charges (APCs) of 350 CHF (Swiss Francs) per published paper are fully funded by institutions through the Knowledge Unlatched initiative, resulting in no direct charge to authors. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


Published Papers (5 papers)

View options order results:
result details:
Displaying articles 1-5
Export citation of selected articles as:

Research

Open AccessArticle Distribution of Budget Shares for Food: An Application of Quantile Regression to Food Security 1
Econometrics 2016, 4(2), 22; https://doi.org/10.3390/econometrics4020022
Received: 1 November 2015 / Revised: 16 March 2016 / Accepted: 22 March 2016 / Published: 8 April 2016
Cited by 2 | PDF Full-text (753 KB) | HTML Full-text | XML Full-text
Abstract
This study examines, using quantile regression, the linkage between food security and efforts to enhance smallholder coffee producer incomes in Rwanda. Even though in Rwanda smallholder coffee producer incomes have increased, inhabitants these areas still experience stunting and wasting. This study examines whether
[...] Read more.
This study examines, using quantile regression, the linkage between food security and efforts to enhance smallholder coffee producer incomes in Rwanda. Even though in Rwanda smallholder coffee producer incomes have increased, inhabitants these areas still experience stunting and wasting. This study examines whether the distribution of the income elasticity for food is the same for coffee and noncoffee growing provinces. We find that that the share of expenditures on food is statistically different in coffee growing and noncoffee growing provinces. Thus, the increase in expenditure on food is smaller for coffee growing provinces than noncoffee growing provinces. Full article
(This article belongs to the Special Issue Quantile Methods)
Figures

Figure 1

Open AccessArticle Interpretation and Semiparametric Efficiency in Quantile Regression under Misspecification
Received: 4 October 2015 / Revised: 27 November 2015 / Accepted: 1 December 2015 / Published: 24 December 2015
PDF Full-text (512 KB) | HTML Full-text | XML Full-text
Abstract
Allowing for misspecification in the linear conditional quantile function, this paper provides a new interpretation and the semiparametric efficiency bound for the quantile regression parameter β ( τ ) in Koenker and Bassett (1978). The first result on interpretation shows that under a
[...] Read more.
Allowing for misspecification in the linear conditional quantile function, this paper provides a new interpretation and the semiparametric efficiency bound for the quantile regression parameter β ( τ ) in Koenker and Bassett (1978). The first result on interpretation shows that under a mean-squared loss function, the probability limit of the Koenker–Bassett estimator minimizes a weighted distribution approximation error, defined as \(F_{Y}(X'\beta(\tau)|X) - \tau\), i.e., the deviation of the conditional distribution function, evaluated at the linear quantile approximation, from the quantile level. The second result implies that the Koenker–Bassett estimator semiparametrically efficiently estimates the quantile regression parameter that produces parsimonious descriptive statistics for the conditional distribution. Therefore, quantile regression shares the attractive features of ordinary least squares: interpretability and semiparametric efficiency under misspecification. Full article
(This article belongs to the Special Issue Quantile Methods)
Figures

Figure 1

Open AccessArticle Counterfactual Distributions in Bivariate Models—A Conditional Quantile Approach
Econometrics 2015, 3(4), 719-732; https://doi.org/10.3390/econometrics3040719
Received: 10 September 2015 / Revised: 18 October 2015 / Accepted: 21 October 2015 / Published: 9 November 2015
PDF Full-text (472 KB) | HTML Full-text | XML Full-text
Abstract
This paper proposes a methodology to incorporate bivariate models in numerical computations of counterfactual distributions. The proposal is to extend the works of Machado and Mata (2005) and Melly (2005) using the grid method to generate pairs of random variables. This contribution allows
[...] Read more.
This paper proposes a methodology to incorporate bivariate models in numerical computations of counterfactual distributions. The proposal is to extend the works of Machado and Mata (2005) and Melly (2005) using the grid method to generate pairs of random variables. This contribution allows incorporating the effect of intra-household decision making in counterfactual decompositions of changes in income distribution. An application using data from five latin american countries shows that this approach substantially improves the goodness of fit to the empirical distribution. However, the exercise of decomposition is less conclusive about the performance of the method, which essentially depends on the sample size and the accuracy of the regression model. Full article
(This article belongs to the Special Issue Quantile Methods)
Figures

Figure 1

Open AccessArticle On Bootstrap Inference for Quantile Regression Panel Data: A Monte Carlo Study
Econometrics 2015, 3(3), 654-666; https://doi.org/10.3390/econometrics3030654
Received: 26 June 2015 / Revised: 10 August 2015 / Accepted: 19 August 2015 / Published: 10 September 2015
Cited by 3 | PDF Full-text (229 KB) | HTML Full-text | XML Full-text
Abstract
This paper evaluates bootstrap inference methods for quantile regression panel data models. We propose to construct confidence intervals for the parameters of interest using percentile bootstrap with pairwise resampling. We study three different bootstrapping procedures. First, the bootstrap samples are constructed by resampling
[...] Read more.
This paper evaluates bootstrap inference methods for quantile regression panel data models. We propose to construct confidence intervals for the parameters of interest using percentile bootstrap with pairwise resampling. We study three different bootstrapping procedures. First, the bootstrap samples are constructed by resampling only from cross-sectional units with replacement. Second, the temporal resampling is performed from the time series. Finally, a more general resampling scheme, which considers sampling from both the cross-sectional and temporal dimensions, is introduced. The bootstrap algorithms are computationally attractive and easy to use in practice. We evaluate the performance of the bootstrap confidence interval by means of Monte Carlo simulations. The results show that the bootstrap methods have good finite sample performance for both location and location-scale models. Full article
(This article belongs to the Special Issue Quantile Methods)
Open AccessArticle A New Family of Consistent and Asymptotically-Normal Estimators for the Extremal Index
Econometrics 2015, 3(3), 633-653; https://doi.org/10.3390/econometrics3030633
Received: 14 July 2015 / Revised: 3 August 2015 / Accepted: 7 August 2015 / Published: 28 August 2015
PDF Full-text (394 KB) | HTML Full-text | XML Full-text
Abstract
The extremal index (θ) is the key parameter for extending extreme value theory results from i.i.d. to stationary sequences. One important property of this parameter is that its inverse determines the degree of clustering in the extremes. This article introduces a novel interpretation
[...] Read more.
The extremal index (θ) is the key parameter for extending extreme value theory results from i.i.d. to stationary sequences. One important property of this parameter is that its inverse determines the degree of clustering in the extremes. This article introduces a novel interpretation of the extremal index as a limiting probability characterized by two Poisson processes and a simple family of estimators derived from this new characterization. Unlike most estimators for θ in the literature, this estimator is consistent, asymptotically normal and very stable across partitions of the sample. Further, we show in an extensive simulation study that this estimator outperforms in finite samples the logs, blocks and runs estimation methods. Finally, we apply this new estimator to test for clustering of extremes in monthly time series of unemployment growth and inflation rates and conclude that runs of large unemployment rates are more prolonged than periods of high inflation. Full article
(This article belongs to the Special Issue Quantile Methods)
Figures

Figure 1

Back to Top