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JRFM
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Advances in Econometric Analysis and Its Applications
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Advances in Econometric Analysis and Its Applications

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Mathematics and Finance".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 5362

Special Issue Editor

Prof. Dr. Mike Tsionas

E-Mail Website
Guest Editor
Lancaster University Management School, Lancaster LA1 4YX, UK
Interests: econometrics; applied econometrics; bayesian techniques in time series and panel data; efficiency and productivity and banking models

Special Issue Information

Dear Colleagues,

The purpose of the Special Issue of JRFM is to provide new perspectives, models, and applications in econometrics. Summaries of relatively recent econometric techniques, useful for practitioners, are also welcome. Theoretical and empirical contributions are also welcome, provided there is a novel element or a review of novel techniques that have been published in major journals but whose application is still limited in practice.

You are more than welcome to send your contribution for consideration.

Prof. Dr. Mike Tsionas
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 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. Journal of Risk and Financial Management is an international peer-reviewed open access monthly 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

  • Econometrics
  • Applied econometrics
  • Time series analysis
  • Panel data
  • Econometric theory

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Published Papers (2 papers)

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Research

8 pages, 224 KiB  
Article
Does More Expert Adjustment Associate with Less Accurate Professional Forecasts?
by Philip Hans Franses and Max Welz
J. Risk Financial Manag. 2020, 13(3), 44; https://doi.org/10.3390/jrfm13030044 - 2 Mar 2020
Viewed by 2391
Abstract
Professional forecasters can rely on an econometric model to create their forecasts. It is usually unknown to what extent they adjust an econometric model-based forecast. In this paper we show, while making just two simple assumptions, that it is possible to estimate the [...] Read more.
Professional forecasters can rely on an econometric model to create their forecasts. It is usually unknown to what extent they adjust an econometric model-based forecast. In this paper we show, while making just two simple assumptions, that it is possible to estimate the persistence and variance of the deviation of their forecasts from forecasts from an econometric model. A key feature of the data that facilitates our estimates is that we have forecast updates for the same forecast target. An illustration to consensus forecasters who give forecasts for GDP growth, inflation and unemployment for a range of countries and years suggests that the more a forecaster deviates from a prediction from an econometric model, the less accurate are the forecasts. Full article
(This article belongs to the Special Issue Advances in Econometric Analysis and Its Applications)
9 pages, 2139 KiB  
Article
Robust Bayesian Inference in Stochastic Frontier Models
by Mike G. Tsionas
J. Risk Financial Manag. 2019, 12(4), 183; https://doi.org/10.3390/jrfm12040183 - 4 Dec 2019
Viewed by 2451
Abstract
We use the concept of coarsened posteriors to provide robust Bayesian inference via coarsening in order to robustify posteriors arising from stochastic frontier models. These posteriors arise from tempered versions of the likelihood when at most a pre-specified amount of data is used, [...] Read more.
We use the concept of coarsened posteriors to provide robust Bayesian inference via coarsening in order to robustify posteriors arising from stochastic frontier models. These posteriors arise from tempered versions of the likelihood when at most a pre-specified amount of data is used, and are robust to changes in the model. Specifically, we examine robustness to changes in the distribution of the composed error in the stochastic frontier model (SFM). Moreover, coarsening is a form of regularization, reduces overfitting and makes inferences less sensitive to model choice. The new techniques are illustrated using artificial data as well as in a substantive application to large U.S. banks. Full article
(This article belongs to the Special Issue Advances in Econometric Analysis and Its Applications)
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