Special Issue "Filtering"
A special issue of Econometrics (ISSN 2225-1146).
Deadline for manuscript submissions: 28 February 2019
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
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.
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- big data
- machine learning