Special Issue "Filtering"

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

Deadline for manuscript submissions: 28 February 2019

Special Issue Editors

Guest Editor
Prof. Christian Hafner

CORE and Institute of statistics, biostatistics and actuarial sciences, Université catholique de Louvain, Louvain-la-neuve, Belgium
Website | E-Mail
Interests: financial econometrics
Guest Editor
Dr. Zhengyuan Gao

CORE, Université catholique de Louvain, Louvain-la-Neuve, Beglium
Website | E-Mail
Interests: likelihood-based estimation; structural econometrics; representation theory in econometrics; stochastic differential equations in econometrics; machine learning

Special Issue Information

Dear Colleagues,

In the big data era, the amount of information exceeds traditional cognitive and computational capacities. Also, many phenomena that are critical to our lives cannot be directly measured. Filtering allows us to infer underlying laws and provides us with a vista of the world. Hence, filtering is a fundamental concept not only in economics and econometrics, but also in adjacent disciplines such as machine learning, applied mathematics, complex systems, psychology, physics, etc. Filtering as a machine learning tool is used in imitating human's reasoning process in robotic systems and artificial intelligence. In brain- and neuroscience, filtering is understood as a synthesis simulating cognitions and perceptions. Recent developments in stochastic systems and stochastic computations advance the theory of filters. They provide opportunities to better integrate and interpret complex dynamics of natural and social phenomena.

Due to this recent progress in many areas, it is desirable to reconnect the various sources of filtering problems to those in economics. As econometrics considers filtering information generated by economic entities, this reconnection is pertinent for both econometric theory and applications. This special issue will collect research papers on filtering in many areas, with an emphasis on their potential impact in economics and econometrics.

Prof. Christian Hafner
Dr. Zhengyuan Gao
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 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.

Keywords

  • information
  • big data
  • machine learning
  • perception
  • cognition
  • econometrics

Published Papers (2 papers)

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Research

Open AccessArticle Filters, Waves and Spectra
Econometrics 2018, 6(3), 35; https://doi.org/10.3390/econometrics6030035
Received: 17 March 2018 / Revised: 15 July 2018 / Accepted: 17 July 2018 / Published: 27 July 2018
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Abstract
Econometric analysis requires filtering techniques that are adapted to cater to data sequences that are short and that have strong trends. Whereas the economists have tended to conduct their analyses in the time domain, the engineers have emphasised the frequency domain. This paper
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Econometric analysis requires filtering techniques that are adapted to cater to data sequences that are short and that have strong trends. Whereas the economists have tended to conduct their analyses in the time domain, the engineers have emphasised the frequency domain. This paper places its emphasis in the frequency domain; and it shows how the frequency-domain methods can be adapted to cater to short trended sequences. Working in the frequency domain allows an unrestricted choice to be made of the frequency response of a filter. It also requires that the data should be free of trends. Methods for extracting the trends prior to filtering and for restoring them thereafter are described. Full article
(This article belongs to the Special Issue Filtering)
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Open AccessArticle Some Results on 1 Polynomial Trend Filtering
Econometrics 2018, 6(3), 33; https://doi.org/10.3390/econometrics6030033
Received: 22 May 2018 / Revised: 30 June 2018 / Accepted: 4 July 2018 / Published: 10 July 2018
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Abstract
1 polynomial trend filtering, which is a filtering method described as an 1-norm penalized least-squares problem, is promising because it enables the estimation of a piecewise polynomial trend in a univariate economic time series without prespecifying the number and location
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1 polynomial trend filtering, which is a filtering method described as an 1-norm penalized least-squares problem, is promising because it enables the estimation of a piecewise polynomial trend in a univariate economic time series without prespecifying the number and location of knots. This paper shows some theoretical results on the filtering, one of which is that a small modification of the filtering provides not only identical trend estimates as the filtering but also extrapolations of the trend beyond both sample limits. Full article
(This article belongs to the Special Issue Filtering)
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