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Forecasting
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Economic Forecasting in Agriculture
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Economic Forecasting in Agriculture

A special issue of Forecasting (ISSN 2571-9394). This special issue belongs to the section "Forecasting in Economics and Management".

Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 15114

Special Issue Editor

Prof. Dr. Marek Vochozka

E-Mail Website
Guest Editor
Institute of Technology and Business in České Budějovice, Research Department of Economics and Natural Resources Management, Okružní 517/10, 370 01 České Budějovice, Czech Republic
Interests: forecasting; timeseries; business economics; commodities; financial markets; capital markets
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

You are invited to join this Special Issue of Forecasting. The issue is focused on Economic Forecasting in Agriculture. The topic is wide from one point of view. On the other hand, it is very special.

Agriculture is a strategic sector for every country all over the world. The products of the sector satisfy the essential needs of every man and woman. So, agriculture and its performance, structure, prices, and others are controlled and protected by every government. On the other hand, there are many risks that can influence farmers’ yields. Especially the weather, input prices, output prices, yield estimations, and others. This Special Issue provides an opportunity to publish the results of our research focused on the future of agriculture.

The topic is very important due to the fact that agriculture is a strategic sector of every economy of the world. It is very sensitive to every input. There are many risks. However, the outputs must be guaranteed to people (citizens, voters). However, our planet is changing. There are many crises. The most serious we can await. This aim can help us to understand the future of this strategic sector. The Special Issue will provide space to discuss partial results of our research and combine them into a complex view of the coming years.

  • Commodities yields forecasting;
  • Commodities prices forecasting;
  • Agricultural enterprises planning;
  • Financial plans of agricultural enterprises;
  • Weather derivative estimating.

We look forward to your contributions and rich discussion.

Dr. Marek Vochozka
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. Forecasting 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 1800 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

  • commodities
  • yields
  • prices
  • timeseries
  • weather derivatives
  • agricultural enterprises

Published Papers (3 papers)

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Research

23 pages, 3803 KiB  
Article
Agricultural Commodities in the Context of the Russia-Ukraine War: Evidence from Corn, Wheat, Barley, and Sunflower Oil
by Florin Aliu, Jiří Kučera and Simona Hašková
Forecasting 2023, 5(1), 351-373; https://doi.org/10.3390/forecast5010019 - 22 Mar 2023
Cited by 5 | Viewed by 6925
Abstract
The Russian invasion of Ukraine on 24 February 2022 accelerated agricultural commodity prices and raised food insecurities worldwide. Ukraine and Russia are the leading global suppliers of wheat, corn, barley and sunflower oil. For this purpose, we investigated the relationship among these four [...] Read more.
The Russian invasion of Ukraine on 24 February 2022 accelerated agricultural commodity prices and raised food insecurities worldwide. Ukraine and Russia are the leading global suppliers of wheat, corn, barley and sunflower oil. For this purpose, we investigated the relationship among these four agricultural commodities and, at the same time, predicted their future performance. The series covers the period from 1 January 1990 to 1 August 2022, based on monthly frequencies. The VAR impulse response function, variance decomposition, Granger Causality Test and vector error correction model were used to analyze relationships between variables. The results indicate that corn prices are an integral part of price changes in wheat, barley and sunflower oil. Wheat prices are also essential but with a weaker influence than that of corn. The additional purpose of this study was to forecast their price changes ten months ahead. The Vector Autoregressive (VAR) and Vector Error Correction (VECM) fanchart estimates an average price decline in corn, wheat, barley and sunflower oil in the range of 10%. From a policy perspective, the findings provide reliable signals for countries exposed to food insecurities and inflationary risk. Recognizing the limitations that predictions maintain, the results provide modest signals for relevant agencies, international regulatory authorities, retailers and low-income countries. Moreover, stakeholders can become informed about their price behavior and the causal relationship they hold with each other. Full article
(This article belongs to the Special Issue Economic Forecasting in Agriculture)
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17 pages, 4643 KiB  
Article
Coffee as an Identifier of Inflation in Selected US Agglomerations
by Marek Vochozka, Svatopluk Janek and Zuzana Rowland
Forecasting 2023, 5(1), 153-169; https://doi.org/10.3390/forecast5010007 - 13 Jan 2023
Cited by 2 | Viewed by 2167
Abstract
The research goal presented in this paper was to determine the strength of the relationship between the price of coffee traded on ICE Futures US and Consumer Price Indices in the major urban agglomerations of the United States—New York, Chicago, and Los Angeles—and [...] Read more.
The research goal presented in this paper was to determine the strength of the relationship between the price of coffee traded on ICE Futures US and Consumer Price Indices in the major urban agglomerations of the United States—New York, Chicago, and Los Angeles—and to predict the future development. The results obtained using the Pearson correlation coefficient confirmed a very close direct correlation (r = 0.61 for New York and Chicago; r = 0.57 for Los Angeles) between the price of coffee and inflation. The prediction made using the SARIMA model disrupted the mutual correlation. The price of coffee is likely to anchor at a new level where it will fluctuate; on the other hand, the CPIs showed strong unilateral pro-growth trends. The results could be beneficial for the analysis and creation of policies and further analyses of market structures at the technical level. Full article
(This article belongs to the Special Issue Economic Forecasting in Agriculture)
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13 pages, 1726 KiB  
Article
Modeling and Forecasting Somali Economic Growth Using ARIMA Models
by Abas Omar Mohamed
Forecasting 2022, 4(4), 1038-1050; https://doi.org/10.3390/forecast4040056 - 30 Nov 2022
Cited by 2 | Viewed by 5275
Abstract
The study investigated the empirical role of past values of Somalia’s GDP growth rates in its future realizations. Using the Box–Jenkins modeling method, the study utilized 250 in-sample quarterly time series data to forecast out-of-the-sample Somali GDP growth rates for fourteen quarters. Balancing [...] Read more.
The study investigated the empirical role of past values of Somalia’s GDP growth rates in its future realizations. Using the Box–Jenkins modeling method, the study utilized 250 in-sample quarterly time series data to forecast out-of-the-sample Somali GDP growth rates for fourteen quarters. Balancing between parsimony and fitness criteria of model selection, the study found Autoregressive Integrated Moving Average ARIMA (5,1,2) to be the most appropriate model to estimate and forecast the trajectory of Somali economic growth. The study sourced the GDP growth data from World Bank World Development Indicators (WDI) for the period between 1960 to 2022. The study results predict that Somalia’s GDP will, on average, experience 4 percent quarterly growth rates for the coming three and half years. To solidify the validity of the forecasting results, the study conducted several ARIMA and rolling window diagnostic tests. The model errors proved to be white noise, the moving average (MA) and Autoregressive (AR) components are covariances stationary, and the rolling window test shows model stability within a 95% confidence interval. These optimistic economic growth forecasts represent a policy dividend for the government of Somalia after almost a decade-long stick-and-carrot economic policies between strict IMF fiscal disciplinary measures and World Bank development investments on target projects. The study, however, acknowledges that the developments of current severe droughts, locust infestations, COVID-19 pandemic, internal political, and security stability, and that the active involvement of international development partners will play a crucial role in the realization of these promising growth projections. Full article
(This article belongs to the Special Issue Economic Forecasting in Agriculture)
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