Special Issue "Special Issue on Economic Forecasting"

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

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Martin Wagner
E-Mail Website
Guest Editor
1. Department of Economics, University of Klagenfurt, Austria
2. Bank of Slovenia, Ljubljana, Slovenia
3. Institute for Advanced Studies, Vienna, Austria
Interests: econometrics; time series; panel data; quantitative economics; transition; environment
Robert Kunst
E-Mail Website
Guest Editor
1. Department of Economics, University of Vienna, Austria
2. Institute for Advanced Studies, Vienna, Austria
Interests: time-series econometrics (in particular, seasonality, cointegration, forecasting, model selection); applied econometrics

Special Issue Information

Dear Colleagues,

The special volume is dedicated—first and foremost, but not exclusively—to providing a publication outlet for papers presented at the 2nd Vienna Workshop on Economic Forecasting. This workshop and a fortiori the Special Issue aim at providing a forum for exchanging ideas and discussing recent results and developments in forecasting. Both theoretical as well as applied contributions are welcome, as are contributions utilizing different approaches to economic forecasting, including forecasts based on time series models, DSGE models, large scale structural models or forecasting using microeconomic data. Forecast evaluation is another topic of interest.

Prof. Dr. Martin Wagner
Prof. Dr. Robert Kunst
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 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

  • forecasting
  • forecast evaluation
  • theory
  • application

Published Papers (1 paper)

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Research

Article
New York FED Staff Nowcasts and Reality: What Can We Learn about the Future, the Present, and the Past?
Econometrics 2021, 9(1), 11; https://doi.org/10.3390/econometrics9010011 - 06 Mar 2021
Viewed by 886
Abstract
We assess the forecasting performance of the nowcasting model developed at the New York FED. We show that the observation regarding a striking difference in the model’s predictive ability across business cycle phases made earlier in the literature also applies here. During expansions, [...] Read more.
We assess the forecasting performance of the nowcasting model developed at the New York FED. We show that the observation regarding a striking difference in the model’s predictive ability across business cycle phases made earlier in the literature also applies here. During expansions, the nowcasting model forecasts at best are at least as good as the historical mean model, whereas during the recessionary periods, there are very substantial gains corresponding in the reduction in MSFE of about 90% relative to the benchmark model. We show how the asymmetry in the relative forecasting performance can be verified by the use of such recursive measures of relative forecast accuracy as Cumulated Sum of Squared Forecast Error Difference (CSSFED) and Recursive Relative Mean Squared Forecast Error (based on Rearranged observations) (R2MSFE(+R)). Ignoring these asymmetries results in a biased judgement of the relative forecasting performance of the competing models over a sample as a whole, as well as during economic expansions, when the forecasting accuracy of a more sophisticated model relative to naive benchmark models tends to be overstated. Hence, care needs to be exercised when ranking several models by their forecasting performance without taking into consideration various states of the economy. Full article
(This article belongs to the Special Issue Special Issue on Economic Forecasting)
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