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Special Issue on Economic Forecasting
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Special Issue on Economic Forecasting

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 15979

Special Issue Editors

Prof. Dr. Martin Wagner

E-Mail Website
Guest Editor
Department of Economics, University of Klagenfurt, 9020 Klagenfurt, Austria
Interests: econometrics; quantitative economics; transition economics; environmental economics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. 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 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

  • forecasting
  • forecast evaluation
  • theory
  • application

Published Papers (4 papers)

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Research

34 pages, 779 KiB  
Article
Forecasting Industrial Production Using Its Aggregated and Disaggregated Series or a Combination of Both: Evidence from One Emerging Market Economy
by Diogo de Prince, Emerson Fernandes Marçal and Pedro L. Valls Pereira
Econometrics 2022, 10(2), 27; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics10020027 - 15 Jun 2022
Cited by 1 | Viewed by 3279
Abstract
In this paper, we address whether using a disaggregated series or combining an aggregated and disaggregated series improves the forecasting of the aggregated series compared to using the aggregated series alone. We used econometric techniques, such as the weighted lag adaptive least absolute [...] Read more.
In this paper, we address whether using a disaggregated series or combining an aggregated and disaggregated series improves the forecasting of the aggregated series compared to using the aggregated series alone. We used econometric techniques, such as the weighted lag adaptive least absolute shrinkage and selection operator, and Exponential Triple Smoothing (ETS), as well as the Autometrics algorithm to forecast industrial production in Brazil one to twelve months ahead. This is the novelty of the work, as is the use of the average multi-horizon Superior Predictive Ability (aSPA) and uniform multi-horizon Superior Predictive Ability (uSPA) tests, used to select the best forecasting model by combining different horizons. Our sample covers the period from January 2002 to February 2020. The disaggregated ETS has a better forecast performance when forecasting horizons that are more than one month ahead using the mean square error, and the aggregated ETS has better forecasting ability for horizons equal to 1 and 2. The aggregated ETS forecast does not contain information that is useful for forecasting industrial production in Brazil beyond the information already found in the disaggregated ETS forecast between two and twelve months ahead. Full article
(This article belongs to the Special Issue Special Issue on Economic Forecasting)
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16 pages, 1805 KiB  
Article
Forecasting Real GDP Growth for Africa
by Philip Hans Franses and Max Welz
Econometrics 2022, 10(1), 3; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics10010003 - 05 Jan 2022
Viewed by 4054
Abstract
We propose a simple and reproducible methodology to create a single equation forecasting model (SEFM) for low-frequency macroeconomic variables. Our methodology is illustrated by forecasting annual real GDP growth rates for 52 African countries, where the data are obtained from the World Bank [...] Read more.
We propose a simple and reproducible methodology to create a single equation forecasting model (SEFM) for low-frequency macroeconomic variables. Our methodology is illustrated by forecasting annual real GDP growth rates for 52 African countries, where the data are obtained from the World Bank and start in 1960. The models include lagged growth rates of other countries, as well as a cointegration relationship to capture potential common stochastic trends. With a few selection steps, our methodology quickly arrives at a reasonably small forecasting model per country. Compared with benchmark models, the single equation forecasting models seem to perform quite well. Full article
(This article belongs to the Special Issue Special Issue on Economic Forecasting)
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21 pages, 2242 KiB  
Article
Forecasting Facing Economic Shifts, Climate Change and Evolving Pandemics
by Jennifer L. Castle, Jurgen A. Doornik and David F. Hendry
Econometrics 2022, 10(1), 2; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics10010002 - 22 Dec 2021
Cited by 2 | Viewed by 4266
Abstract
By its emissions of greenhouse gases, economic activity is the source of climate change which affects pandemics that in turn can impact badly on economies. Across the three highly interacting disciplines in our title, time-series observations are measured at vastly different data frequencies: [...] Read more.
By its emissions of greenhouse gases, economic activity is the source of climate change which affects pandemics that in turn can impact badly on economies. Across the three highly interacting disciplines in our title, time-series observations are measured at vastly different data frequencies: very low frequency at 1000-year intervals for paleoclimate, through annual, monthly to intra-daily for current climate; weekly and daily for pandemic data; annual, quarterly and monthly for economic data, and seconds or nano-seconds in finance. Nevertheless, there are important commonalities to economic, climate and pandemic time series. First, time series in all three disciplines are subject to non-stationarities from evolving stochastic trends and sudden distributional shifts, as well as data revisions and changes to data measurement systems. Next, all three have imperfect and incomplete knowledge of their data generating processes from changing human behaviour, so must search for reasonable empirical modeling approximations. Finally, all three need forecasts of likely future outcomes to plan and adapt as events unfold, albeit again over very different horizons. We consider how these features shape the formulation and selection of forecasting models to tackle their common data features yet distinct problems. Full article
(This article belongs to the Special Issue Special Issue on Economic Forecasting)
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25 pages, 2960 KiB  
Article
New York FED Staff Nowcasts and Reality: What Can We Learn about the Future, the Present, and the Past?
by Boriss Siliverstovs
Econometrics 2021, 9(1), 11; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9010011 - 06 Mar 2021
Cited by 1 | Viewed by 2854
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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