Special Issue "Discrete-Valued Time Series: Modelling, Estimation and Forecasting"

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

Deadline for manuscript submissions: closed (31 August 2019).

Special Issue Editors

Brendan McCabe
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Guest Editor
University of Liverpool
Interests: time series modelling and estimation; forecasting count data; Bayesian analysis
Andrew Tremayne
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Guest Editor
University of Liverpool
Interests: time series econometrics; modelling count data; inference

Special Issue Information

Dear Colleagues,

This Special Issue is concerned with publishing a range of new contributions to the field of Discrete-Valued Time Series. Both methodological advances and applications are encouraged; papers which combine the two are particularly sought. Contributions may involve univariate and, particularly, multivariate time series models; these may be either observation- or parameter-driven. Topics include specification and estimation, as well as inference methods.

Count time series are usually non-negative integers, but papers dealing with binary and categorical variables are also welcome. Methodology may be classical or Bayesian in nature. There is, as of yet, a limited literature on goodness-of-fit methods in this area of modelling and so we would welcome contributions in this field. Other ripe topics for advancement would include forecasting and its applications, change-point detection and diagnostic and model testing methods. General dynamic analysis including impulse response analysis would also be of interest.

The Special Issue seeks to bring together a burgeoning stream of literature across a range of fields including, but not limited to, medicine; epidemiology; finance; and economics, discussing advances.

Overall, the main thrust of the Special Issue is to develop and refine extant methods for analysis of count time series data and to advance knowledge and applicability in novel and exciting directions.

Prof. Brendan McCabe
Prof. Andrew R. Tremayne
Guest Editors

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 Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). 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.

Keywords

  • Count data
  • Time series
  • Estimation
  • Testing
  • Forecasting
  • Model validation

Published Papers (3 papers)

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Research

Open AccessFeature PaperArticle
Likelihood Inference for Generalized Integer Autoregressive Time Series Models
Econometrics 2019, 7(4), 43; https://doi.org/10.3390/econometrics7040043 - 11 Oct 2019
Abstract
For modeling count time series data, one class of models is generalized integer autoregressive of order p based on thinning operators. It is shown how numerical maximum likelihood estimation is possible by inverting the probability generating function of the conditional distribution of an [...] Read more.
For modeling count time series data, one class of models is generalized integer autoregressive of order p based on thinning operators. It is shown how numerical maximum likelihood estimation is possible by inverting the probability generating function of the conditional distribution of an observation given the past p observations. Two data examples are included and show that thinning operators based on compounding can substantially improve the model fit compared with the commonly used binomial thinning operator. Full article
(This article belongs to the Special Issue Discrete-Valued Time Series: Modelling, Estimation and Forecasting)
Open AccessArticle
Evaluating Approximate Point Forecasting of Count Processes
Econometrics 2019, 7(3), 30; https://doi.org/10.3390/econometrics7030030 - 06 Jul 2019
Abstract
In forecasting count processes, practitioners often ignore the discreteness of counts and compute forecasts based on Gaussian approximations instead. For both central and non-central point forecasts, and for various types of count processes, the performance of such approximate point forecasts is analyzed. The [...] Read more.
In forecasting count processes, practitioners often ignore the discreteness of counts and compute forecasts based on Gaussian approximations instead. For both central and non-central point forecasts, and for various types of count processes, the performance of such approximate point forecasts is analyzed. The considered data-generating processes include different autoregressive schemes with varying model orders, count models with overdispersion or zero inflation, counts with a bounded range, and counts exhibiting trend or seasonality. We conclude that Gaussian forecast approximations should be avoided. Full article
(This article belongs to the Special Issue Discrete-Valued Time Series: Modelling, Estimation and Forecasting)
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Open AccessArticle
Measures of Dispersion and Serial Dependence in Categorical Time Series
Econometrics 2019, 7(2), 17; https://doi.org/10.3390/econometrics7020017 - 22 Apr 2019
Abstract
The analysis and modeling of categorical time series requires quantifying the extent of dispersion and serial dependence. The dispersion of categorical data is commonly measured by Gini index or entropy, but also the recently proposed extropy measure can be used for this purpose. [...] Read more.
The analysis and modeling of categorical time series requires quantifying the extent of dispersion and serial dependence. The dispersion of categorical data is commonly measured by Gini index or entropy, but also the recently proposed extropy measure can be used for this purpose. Regarding signed serial dependence in categorical time series, we consider three types of κ -measures. By analyzing bias properties, it is shown that always one of the κ -measures is related to one of the above-mentioned dispersion measures. For doing statistical inference based on the sample versions of these dispersion and dependence measures, knowledge on their distribution is required. Therefore, we study the asymptotic distributions and bias corrections of the considered dispersion and dependence measures, and we investigate the finite-sample performance of the resulting asymptotic approximations with simulations. The application of the measures is illustrated with real-data examples from politics, economics and biology. Full article
(This article belongs to the Special Issue Discrete-Valued Time Series: Modelling, Estimation and Forecasting)
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