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Econometrics
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Filtering
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Filtering

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

Deadline for manuscript submissions: closed (28 February 2019) | Viewed by 31520

Special Issue Editors

Prof. Dr. Christian Hafner

E-Mail Website
Guest Editor
CORE and Institute of Statistics, Biostatistics and Actuarial Sciences, Université catholique de Louvain, 1348 Ottignies-Louvain-la-Neuve, Belgium
Interests: financial econometrics
Special Issues, Collections and Topics in MDPI journals
Dr. Zhengyuan Gao

E-Mail Website
Guest Editor
CORE, Université catholique de Louvain, Louvain-la-Neuve, Beglium
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 submissions that pass pre-check are 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 1400 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

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

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Published Papers (4 papers)

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Research

24 pages, 334 KiB  
Article
Looking Backward and Looking Forward
by Zhengyuan Gao and Christian M. Hafner
Econometrics 2019, 7(2), 27; https://doi.org/10.3390/econometrics7020027 - 14 Jun 2019
Viewed by 5827
Abstract
Filtering has had a profound impact as a device of perceiving information and deriving agent expectations in dynamic economic models. For an abstract economic system, this paper shows that the foundation of applying the filtering method corresponds to the existence of a conditional [...] Read more.
Filtering has had a profound impact as a device of perceiving information and deriving agent expectations in dynamic economic models. For an abstract economic system, this paper shows that the foundation of applying the filtering method corresponds to the existence of a conditional expectation as an equilibrium process. Agent-based rational behavior of looking backward and looking forward is generalized to a conditional expectation process where the economic system is approximated by a class of models, which can be represented and estimated without information loss. The proposed framework elucidates the range of applications of a general filtering device and is not limited to a particular model class such as rational expectations. Full article
(This article belongs to the Special Issue Filtering)
22 pages, 707 KiB  
Article
State-Space Models on the Stiefel Manifold with a New Approach to Nonlinear Filtering
by Yukai Yang and Luc Bauwens
Econometrics 2018, 6(4), 48; https://doi.org/10.3390/econometrics6040048 - 12 Dec 2018
Cited by 2 | Viewed by 9198
Abstract
We develop novel multivariate state-space models wherein the latent states evolve on the Stiefel manifold and follow a conditional matrix Langevin distribution. The latent states correspond to time-varying reduced rank parameter matrices, like the loadings in dynamic factor models and the parameters of [...] Read more.
We develop novel multivariate state-space models wherein the latent states evolve on the Stiefel manifold and follow a conditional matrix Langevin distribution. The latent states correspond to time-varying reduced rank parameter matrices, like the loadings in dynamic factor models and the parameters of cointegrating relations in vector error-correction models. The corresponding nonlinear filtering algorithms are developed and evaluated by means of simulation experiments. Full article
(This article belongs to the Special Issue Filtering)
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33 pages, 431 KiB  
Article
Filters, Waves and Spectra
by D. Stephen G. Pollock
Econometrics 2018, 6(3), 35; https://doi.org/10.3390/econometrics6030035 - 27 Jul 2018
Cited by 5 | Viewed by 7844
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 [...] Read more.
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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10 pages, 288 KiB  
Article
Some Results on ℓ1 Polynomial Trend Filtering
by Hiroshi Yamada and Ruixue Du
Econometrics 2018, 6(3), 33; https://doi.org/10.3390/econometrics6030033 - 10 Jul 2018
Viewed by 7384
Abstract
ℓ1 polynomial trend filtering, which is a filtering method described as an ℓ1-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 [...] Read more.
ℓ1 polynomial trend filtering, which is a filtering method described as an ℓ1-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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