Agricultural Commodities in the Context of the Russia-Ukraine War: Evidence from Corn, Wheat, Barley, and Sunflower Oil
2. Literature Review
- Note: plots are constructed based on 391 observations and stand on monthly observations. The R studio program was used for data processing and visualization. The monthly series were obtained from St. Louis FED , indicating raw data. The figures were constructed using R studio’s “tidyverse” and “ggplot2” packages. WPW stands for the World Price of Wheat, WPB stands for the World Price of Barley, WPC stands for the World Price of Corn, and the World Price of Sunflower Oil holds the acronym WPSF.
3.2. Methods Used
4.1. VAR Estimated Results
- Note: Plots indicate the VAR (1) impulse response function with twelve combinations of WPC, WPB, WPW and WPSF. IRF results stand within a 95% confidence interval (CI) and are constrained to ten periods ahead. Red lines represent error margins, and simulations were performed with 100 trials. Because our differenced series are monthly, the IRF effects were measured for the next ten months. The variables display the period from 1 January 1990 to 1 August 2022. The figure was generated in R studio using the package “vars” and the function “irf”.
- Note: This figure highlights FEVD results based on four variables in the system for 10 months ahead. The series were differenced indicating the full period from 1 January 1990 to 1 August 2022. Plots were generated in R studio through the “vars” package and implemented with the “fevd” function. Results in a numerical format are available upon request.
4.2. Granger Causality Tests
4.3. Estimated VECM Results
4.4. Estimated Forecasts with VAR and VECM Fanchart
- Note: Plots indicate predictions with raw data through the VAR fanchart package. The estimated forecasts were conducted individually for each variable with a 95% confidence band for ten months ahead. The results cover the entire period from 1 January 1990 to 1 June 2022, based on 391 observations. The estimated forecasts start on 1 July 2022, and end on 1 April 2023. The black line represents the predictions, and the gray-shaded area indicates the error margin.
- Note: This figure presents the forecasts for the next ten months based on 391 observations of each variable in the model. The forecast starts on 1 July 2022 and ends on 1 April 2023 using raw data. Estimations were conducted through the VECM fanchart on a 95% confidence band. The black line represents estimated forecasts, and the shaded part in gray is the error margin. The figure was generated in R studio using the “forecast” package and the “fanchart” function.
5. Discussion: Price Forecasts—Presumption and Limits
Data Availability Statement
Conflicts of Interest
- Note: The figure indicates the correlation matrix based on the raw data of individual variables. The series represents the full-time period from 1 January 1990 to 1 August 2022. However, the boxplots were also based on raw data, and the plots were generated using the package “tidyverse” in R studio.
- Note: The plot was completed in R studio using the “vars” package and was generated through the “stability” function. The monthly series cover the entire period from 1 January 1990 to 1 August 2022. The red lines show each variable’s 95% confidence band within the system. The series is within the 95% confidence band, indicating a stable system.
- Note: Plots were built in R studio using the functions “acf” and “pacf” through the “vars” package. The series represents the full time period from 1 January 1990 to 1 August 2022, using the first difference. The first differencing was used because the data did not pass the stationarity tests in their raw form. The blue line stands for the 95% confidence band, and the black bars highlight the number of autoregressive lags in the system.
|Time||fcst (WPC)||fcst (WPW)||fcst (WPB)||fcst (WPSF)|
|1 July 2022||297.32||302.31||223.73||1666.86|
|1 August 2022||288.83||297.71||214.47||1587.24|
|1 September 2022||283.95||295.76||209.53||1555.62|
|1 October 2022||280.65||293.64||206.52||1545.06|
|1 November 2022||278.01||291.11||204.26||1542.13|
|1 December 2022||275.60||288.38||202.27||1540.85|
|1 January 2023||273.31||285.62||200.39||1538.83|
|1 February 2023||271.07||282.93||198.56||1535.34|
|1 March 2023||268.87||280.33||196.76||1530.28|
|1 April 2023||266.68||277.82||194.98||1523.82|
|Horizon||fcst (WPC)||fcst (WPW)||fcst (WPB)||fcst (WPSF)|
|1 July 2022||295.36||297.31||218.98||1693.19|
|1 August 2022||285.69||290.54||206.28||1637.49|
|1 September 2022||279.14||287.66||202.09||1600.58|
|1 October 2022||275.18||285.65||200.88||1569.88|
|1 November 2022||272.97||283.52||200.08||1546.78|
|1 December 2022||271.46||281.53||199.32||1530.63|
|1 January 2023||270.25||279.98||198.69||1519.40|
|1 February 2023||269.27||278.81||198.25||1510.98|
|1 March 2023||268.47||277.91||197.99||1504.00|
|1 April 2023||267.78||277.16||197.84||1497.89|
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|Variables||Model (1)||Model (2)||Model (3)||Model (4)|
|WPC (L1)||0.172 ***|
|WPW (L1)||0.091 **|
|WPSF (L1)||0.016 **|
|Residual Std. Error (df = 384)||10.438||14.741||7.372||82.645|
|F Statistic (df = 10; 384)||10.438 ***||4.875 ***||23.498 ***||19.685 ***|
|Granger causality H0: WPC does not Granger-cause|
WPW, WPB and WPSF
|Granger causality H0: WPW does not Granger-cause|
WPC, WPB and WPSF
|Granger causality H0: WPB does not Granger-cause|
WPC, WPW and WPSF
|Granger causality H0: WPSF does not Granger-cause|
WPC, WPW and WPB
|Test type: trace statistic, without a linear trend and constant cointegration|
|Values of the test statistic and critical values of the test:|
|r ≤ 3||74.25||7.52||9.24||12.97|
|r ≤ 2||186.83||17.85||19.96||24.60|
|r ≤ 1||318.08||32.00||34.91||41.07|
|r = 0||459.28||49.65||53.12||60.16|
|Test type: maximal eigenvalue statistic (lambda max), without a linear trend and constant cointegration|
|Values of the test statistic and critical values of the test:|
|r ≤ 3||74.25||7.52||9.24||12.97|
|r ≤ 2||112.59||13.75||15.67||20.21|
|r ≤ 1||131.24||19.77||22.00||26.81|
|r = 0||141.21||25.56||28.14||33.24|
|WPW||0.0230(0.0358)||−0.0913(0.0289) ***||−0.1096(0.0386) **||3.2415(1.2652) *|
|WPC||0.1057(0.0455) *||−0.0258(0.0859)||0.0062(0.0068)||0.1729(0.0668) **|
|WPW||0.3208(0.0643) ***||−0.1583(0.1212)||−0.0121(0.0096)||0.0039(0.0942) *|
|WPB||0.0844(0.1972) **||0.1786(0.0476) **||0.0034(0.0048)||0.1552(0.0476) **|
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Aliu, F.; Kučera, J.; Hašková, S. Agricultural Commodities in the Context of the Russia-Ukraine War: Evidence from Corn, Wheat, Barley, and Sunflower Oil. Forecasting 2023, 5, 351-373. https://doi.org/10.3390/forecast5010019
Aliu F, Kučera J, Hašková S. Agricultural Commodities in the Context of the Russia-Ukraine War: Evidence from Corn, Wheat, Barley, and Sunflower Oil. Forecasting. 2023; 5(1):351-373. https://doi.org/10.3390/forecast5010019Chicago/Turabian Style
Aliu, Florin, Jiří Kučera, and Simona Hašková. 2023. "Agricultural Commodities in the Context of the Russia-Ukraine War: Evidence from Corn, Wheat, Barley, and Sunflower Oil" Forecasting 5, no. 1: 351-373. https://doi.org/10.3390/forecast5010019