Special Issue "Topics in Computational Econometrics and Finance: Theory and Applications"

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

Deadline for manuscript submissions: 31 December 2022.

Special Issue Editor

Fredj Jawadi
E-Mail Website
Guest Editor
Institut d'Administration des Entreprises de Lille (IAE), University of Lille Nord de France, Lille, France
Interests: applied econometrics; empirical finance; empirical macroeconomics

Special Issue Information

Dear Colleagues,

Since the aftermath of the global financial crisis (2008–2009), various macroeconomic and financial models have been questioned, in particular because of the inability of these models to forecast the financial crisis. This has yielded an ongoing challenge to improve these models in order to better reproduce the properties of macro and financial data and to improve their modeling and forecasting. Accordingly, these models have been the subject of different empirical investigations. To this end, some econometric models have been extended. These extensions have implied the development/extension of parametric tests (time series modeling, high frequency modeling, panel data, multivariate analysis, etc.) as well as nonparametric tests (wavelet, spectral analysis, etc.). The application of new tests has improved the modeling of macroeconomic and financial data in different ways, and the related simulations of these methods have shown their viability. Consequently, a growing research agenda has been defined to tackle these questions with these new econometric methods.

This Special Issue aims to focus on the recent topics on empirical macroeconomic and financial modeling as well as on these extended econometric techniques that were introduced to extend the original framework of macro-econometrics. Accordingly, this Special Issue will present theoretical, methodological, as well as empirical, research in empirical macroeconomics and finance, with a focus on the econometrics modeling steps.

This Special Issue is associated with the 6th International Symposium in Computational Economics and Finance (ISCEF2020, www.iscef.com). The conference will be held from 29 to 31 October 2020, in Paris.

Prof. Dr. Fredj Jawadi
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 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 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

  • empirical finance
  • empirical macroeconomics
  • computational finance
  • time series
  • nonlinearity
  • nonparametric econometrics
  • forecasting

Published Papers (2 papers)

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Research

Open AccessArticle
Structural Panel Bayesian VAR with Multivariate Time-Varying Volatility to Jointly Deal with Structural Changes, Policy Regime Shifts, and Endogeneity Issues
Econometrics 2021, 9(2), 20; https://doi.org/10.3390/econometrics9020020 - 02 May 2021
Viewed by 288
Abstract
This paper improves a standard Structural Panel Bayesian Vector Autoregression model in order to jointly deal with issues of endogeneity, because of omitted factors and unobserved heterogeneity, and volatility, because of policy regime shifts and structural changes. Bayesian methods are used to select [...] Read more.
This paper improves a standard Structural Panel Bayesian Vector Autoregression model in order to jointly deal with issues of endogeneity, because of omitted factors and unobserved heterogeneity, and volatility, because of policy regime shifts and structural changes. Bayesian methods are used to select the best model solution for examining if international spillovers come from multivariate volatility, time variation, or contemporaneous relationship. An empirical application among Central-Eastern and Western Europe economies is conducted to describe the performance of the methodology, with particular emphasis on the Great Recession and post-crisis periods. A simulated example is also addressed to highlight the performance of the estimating procedure. Findings from evidence-based forecasting are also addressed to evaluate the impact of an ongoing pandemic crisis on the global economy. Full article
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Open AccessFeature PaperArticle
Enhanced Methods of Seasonal Adjustment
Econometrics 2021, 9(1), 3; https://doi.org/10.3390/econometrics9010003 - 05 Jan 2021
Viewed by 749
Abstract
The effect of the conventional model-based methods of seasonal adjustment is to nullify the elements of the data that reside at the seasonal frequencies and to attenuate the elements at the adjacent frequencies. It may be desirable to nullify some of the adjacent [...] Read more.
The effect of the conventional model-based methods of seasonal adjustment is to nullify the elements of the data that reside at the seasonal frequencies and to attenuate the elements at the adjacent frequencies. It may be desirable to nullify some of the adjacent elements instead of merely attenuating them. For this purpose, two alternative sets of procedures are presented that have been implemented in a computer program named SEASCAPE. In the first set of procedures, a basic seasonal adjustment filter is augmented by additional filters that are targeted at the adjacent frequencies. In the second set of procedures, a Fourier transform of the data is exploited to allow the elements in the vicinities of the seasonal frequencies to be eliminated or attenuated at will. The question is raised of whether an estimated trend-cycle trajectory that is devoid of high-frequency noise can serve in place of the seasonally adjusted data. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: ENHANCED METHODS OF SEASONAL ADJUSTMENT
Authors: D.S.G. Pollock
Affiliation: University of Leicester; Email: stephen [email protected]
Abstract: The effect of the conventional model-based methods of seasonal adjustment is to nullify the elements of the data that reside at the seasonal frequencies and to attenuate the elements at the adjacent frequencies. It may be desirable to nullify some of the adjacent elements instead of merely attenuating them. For this purpose, two alternative procedures are presented that have been implemented in a computer program. In the first procedure, the seasonal-adjustment filter is augmented by additional filters that are targeted at the adjacent frequencies. In the second procedure, a Fourier transform is deployed to reveal the elements of the data at all the frequencies. This allows the elements in the vicinities of the seasonal frequencies to be eliminated or attenuated at will. In spite of the success of these procedures, the question is raised of whether the estimated trend-cycle trajectory should serve in place of the seasonally adjusted data.

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