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American Journal of  Applied 
Statistics and Economics (AJASE)

Forecasting the Global Price of  Corn: Unveiling Insights with SARIMA Modelling 
Amidst Geopolitical Events and Market Dynamics

Raksha Khadka1*, Yeong Nain Chi1

Volume 3 Issue 1, Year 2024
ISSN: 2992-927X (Online)

DOI: https://doi.org/10.54536/ajase.v3i1.2776
https://journals.e-palli.com/home/index.php/ajase

Article Information ABSTRACT

Received: July 05, 2024

Accepted: August 07, 2024

Published: August 10, 2024

Corn is pivotal in global agriculture, serving diverse purposes in the food, feed, and biofuel 
sectors. Despite its economic significance, corn price volatility, influenced by supply-demand 
dynamics, climate variations, and geopolitical tensions, poses challenges in decision-making 
processes. This necessitates accurate price forecasting of  corn for producers and government 
alike to formulate effective policies that uphold stability and enhance efficiency within the 
corn market. Using long-term records of  the monthly global price of  corn spanning from 
January 2014 to December 2023, this study employs SARIMA modeling techniques to 
forecast the global price of  corn. To find a solution, the auto.arima() function from the 
“forecast” package in R 4.3.2 for Windows was employed to identify both the structure of  
the series (stationary or not) and type (seasonal or not) and sets the model’s parameters, 
which takes into account the AIC, AICc or BIC values generated to determine the best 
fitting seasonal ARIMA model. Following the Box–Jenkins methodology, the best-fitting 
SARIMA (0,1,1) (0,0,1) [12] model was identified, supported by the lowest AIC value. The 
Ljung–Box Q–test further validated the model’s adequacy in capturing the data’s behavior, 
with a non-significant p-value of  0.7013. This analysis uncovered valuable insights into 
the fluctuations of  corn prices, providing a comprehensive understanding of  the interplay 
between economic factors and external influences. This study underscores the practical 
utility of  SARIMA modeling for farmers and other relevant stakeholders in anticipating 
market fluctuations and devising adaptive strategies in response to evolving corn market 
dynamics.

Keywords

Corn, Price, Time Series, 
Forecasting, SARIMA

1 Department of  Agriculture, Food, & Resource Sciences, School of  Agricultural and Natural Sciences, University of  Maryland 
  Eastern Shore (UMES), Princess Anne, MD 21853, USA
* Corresponding author’s e-mail: rkhadka@umes.edu

INTRODUCTION
Corn stands as one of  the paramount grain crops 
globally, holding the prestigious rank of  third, trailing 
only behind wheat and rice. Its cultivation sprawls 
across more than 100 countries, with the United States 
spearheading production, contributing approximately 
40% of  the world’s total output. Alongside the US, other 
key corn-producing nations encompass China, Brazil, 
Mexico, Indonesia, India, France, and Argentina (Darekar 
& Reddy, 2017). Beyond its sheer volume of  production, 
corn plays a pivotal role in various sectors, including both 
food and industrial domains. Notably, corn finds its way 
into the production lines of  diverse products, prominently 
featuring in the creation of  starch (Yu & Moon, 2021). 
Corn’s multifaceted utility extends to its applications in 
livestock feed production (Fauziah et al., 2023), corn oil 
extraction (Wheals et al., 1999), and the production of  
ethanol, a renewable and widely used biofuel. Ethanol 
production from corn boasts an impressive conversion 
rate, generating 2.7 gallons of  ethanol per bushel of  corn 
(Baker & Zahniser, 2006). Given its indispensable role in 
various industries, any fluctuations in the price of  corn 
reverberate across sectors, impacting stakeholders at 
various levels.
Price volatility in commodity markets stems from a 
myriad of  factors, encompassing intricate interplays 
of  supply-demand dynamics, crop yield variations, 
geopolitical tensions, economic downturns, and even 

global health crises. Corn prices are inherently susceptible 
to these forces and have witnessed significant oscillations 
over time. In this context, harnessing the power of  time 
series analysis emerges as a potent tool for navigating the 
complexities of  pricing decisions.
Time series analysis entails the systematic examination 
of  data points recorded over sequential time intervals. 
These data, arranged chronologically, offer insights into 
the temporal evolution of  a specific variable. Represented 
mathematically as y(t), where ‘y’ denotes the variable 
of  interest and ‘t’ denotes time, time series data enable 
analysts to discern patterns, trends, and anomalies, thereby 
empowering informed decision-making (Montgomery 
et al., 2015). By leveraging historical trends, forecasting 
techniques embedded within time series analysis equip 
market participants with valuable foresight, facilitating 
proactive adjustments to pricing strategies in anticipation 
of  future market dynamics.
Moreover, the significance of  corn transcends mere 
economic considerations. It is deeply intertwined with 
agricultural practices, environmental sustainability, and 
food security on a global scale. As populations burgeon and 
climates fluctuate, the resilience and adaptability of  corn 
cultivation become increasingly pivotal. Understanding 
the intricate dynamics governing corn prices not only 
informs commercial decisions but also holds implications 
for broader socio-economic and environmental contexts.
Furthermore, the evolution of  technology, particularly 



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in data analytics and computational methods, has 
revolutionized the landscape of  market analysis. Advanced 
statistical models and machine learning algorithms offer 
unprecedented capabilities in extracting actionable 
insights from voluminous datasets. Integrating these 
technological advancements with traditional economic 
principles enhances the efficacy of  pricing strategies, 
positioning market participants to navigate the intricacies 
of  the corn market with greater precision and agility.
In light of  these considerations, this paper embarks on 
a comprehensive exploration of  corn price dynamics, 
employing a multifaceted approach that melds economic 
theory with cutting-edge analytical methodologies. 
Through a nuanced examination of  historical trends, 
statistical modeling, and forecast projections, this study 
endeavors to shed light on the underlying drivers of  corn 
price fluctuations, thereby empowering stakeholders with 
actionable intelligence to optimize pricing strategies and 
mitigate risks in the volatile landscape of  commodity 
markets.

LITERATURE REVIEW
The fluctuations in global corn prices over the years have 
been influenced by a myriad of  interconnected factors, 
resulting in a complex and dynamic market landscape. 
These fluctuations can be attributed to shifts in global 
demand, disruptions in supply chains, geopolitical events, 
disease outbreaks, sudden climate changes, increased 
demand for corn-based products (such as biofuels), and 
speculation in commodity markets. The COVID-19 
pandemic in 2020 exemplified how external shocks 
can significantly impact corn prices and production, 
particularly in the United States. Disruptions in ethanol, 
gasoline, and oil markets led to a notable decrease in corn 
prices, as highlighted by Schmitz et al. (2020). Beghin & 
Timalsina (2020) observed a decrease in corn prices from 
$3.74 per bushel in December 2019 to $2.94 per bushel in 

May 2020, while Liu et al. (2024) noted that the pandemic 
influenced subsequent increases in global corn prices.
Following the economic slowdown as a result of  
COVID-19, biofuel prices experienced a significant 
decline in 2020, followed by their main feedstocks, maize, 
and oilseeds (Elleby et al., 2020). Additionally, the Russia-
Ukraine conflict, which began in 2022, exacerbated 
the situation by causing an energy crisis and disrupting 
food production and commodity markets, including 
corn prices, as both countries are major exporters of  
staple crops (Avalos & Huang, 2022). The conflict also 
highlighted the paradoxical potential for increased biofuel 
usage to moderate rising oil prices, consequently boosting 
demand and prices for corn, a crucial feedstock for 
ethanol production. Furthermore, fluctuations in both 
supply and demand for corn, driven by factors such as 
natural conditions, imports, changing needs in animal 
feed, food production, and alternative energy sources, 
have contributed to consumer-level price volatility 
(Baladina et al., 2021).
The seasonal fluctuations in crop production further 
exacerbate the challenge of  market strategy and 
investment planning due to price uncertainty (Brandt & 
Bessler, 1983). Consequently, accurate price forecasting 
becomes essential to assist farmers, stakeholders, 
consumers, policymakers, and investors in making 
informed decisions. Numerous studies have focused on 
forecasting commodity prices, including corn, utilizing 
various time series forecasting models.
Table 1 presents some of  the identified models used 
for forecasting corn prices, along with the evaluation 
tools and performance metrics employed by different 
researchers. Similarly, Table 2 illustrates the diversity of  
applications of  time series forecasting models in value 
forecasting across different fields, showcasing the range 
of  models utilized and the performance metrics evaluated 
by various authors.

Table 1: SARIMA models identified by authors for commodity price forecasting
Commodities Models Identified Evaluation Tools/Performance Metrics Contributors
Corn  (2,1,0)(3,1,1)12 MSE, RMSE, Ljung-Box Q (Lv & Wu, 2022) 
Soybean   (0,1,3)(0,0,2)12 MSE (Chi, 2021)
Red Lentil  (2,1,2)(0,1,1)52 AIC, Ljung-Box Q (Divisekara et al., 2020)
Potato (1,1,1)(1,0,0)12 SBC, AIC (Chandran & Pandey, 2007)
Tomato (2,1,1)(1,0,1)12 AIC (Mutwiri, 2019)
Bajra (0,1,1)(0,1,1)12 AIC, SBC, MAD, MAPE, MSE (Sharma & Burark, 2015)

Table 2: SARIMA models identified by various authors for value forecasting
Forecasts Models Identified Evaluation Tools/Performance Metrics Contributors
Monthly mean surface 
air temperature 

(2,1,1)(1,1,2)12 ME, RMSE, MAE, MPE, MAPE, MASE, 
BIC

(Asamoah-Boaheng, 
2014)

Frequency of  monthly 
rainfall 

 (1,0,1)(1,1,1)12 S.E, Variance, AIC (Adams et al., 2019)

Temperatures (1,1,1)(1,0,1,)12 AIC (Chen et al., 2018)



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Corn plays a crucial role in ethanol production, a biofuel 
often blended with gasoline. Over the last two decades, 
ethanol production has been the only use of  corn in the 
United States that has seen a notable increase, consuming 
about 40% of  the U.S. harvest on average over the past 
five years (Avalos & Huang, 2022). Higher oil prices 
incentivize gasoline blenders to increase the ethanol 
content in their products, potentially mitigating oil price 
spikes but simultaneously boosting corn demand and 
prices (Avalos & Huang, 2022). This inflation was fueled 
by post-pandemic economic adjustments since mid-
2020, and persistent constraints on aggregate supply due 
to disruptions in global supply chains (Goryunov et al., 
2023). 
Economic challenges, including accelerating inflation 
since 2021, further compounded the situation, impacting 
countries worldwide. In the USA, inflation reached 
close to 10%, while it was even higher in the euro area 
(Goryunov et al., 2023). Although inflation rates began to 
decrease by 2023, they remained elevated, reflecting the 
enduring impact of  the factors influencing corn prices 
on the global economy. Dohlman et al. (2024) forecasts 
suggest that there will likely be a decrease or stability 
in crop prices from 2024 to 2033. The United States 
Department of  Agriculture (USDA) also anticipates a 
decline in corn prices to $4.50 per bushel, followed by 
a period of  stabilization around the 2025/26 timeframe 
(Dohlman et al., 2024).  Due to the abundant supply 
of  corn in the United States, it is anticipated that corn 
prices will experience a decline throughout 2024 (UGA 
Cooperative Extension, 2024).
In addition to understanding the factors influencing price 
fluctuations, forecasting models play a crucial role in 
providing valuable insights for decision-making processes 
across various sectors, ranging from agriculture to climate 
science. By utilizing sophisticated modeling techniques 
and evaluating performance metrics, researchers strive to 
enhance the accuracy and reliability of  forecasting models 
to better navigate the complexities of  global markets and 
environmental systems. 

MATERIALS AND METHODS
The main purpose of  this study was to demonstrate the 
role of  the time series model in predicting processes 
and to pursue the analysis of  time series data using 
long-term records of  the monthly global price of  corn 
from January 2014 to December 2023. The monthly 
global price of  corn, (units:  U.S. dollars per metric 
ton, monthly, not seasonally adjusted) from January 
2014 to December 2023, is available to the public from 
International Monetary Fund, Global price of  Corn 
[PMAIZMTUSDM], retrieved from FRED, Federal 
Reserve Bank of  St. Louis; https://fred.stlouisfed.org/
series/PMAIZMTUSDM, March 10, 2024. The average 
monthly global price of  corn from January 2014 to 
December 2023 was $200.6 U.S. dollars per metric ton 
with a standard deviation of  $84.35 (Minimum: $144.0, 
Maximum: $348.5, and Median: $171.9).

Time series analysis is based on the underlying assumption 
that the data is stationary. Thus, it is crucial to identify 
whether time series data is stationary or non-stationary. 
Data is considered stationary if  its mean and variance 
do not change over time. Conversely, non-stationary 
data exhibit a long-term increase or decrease over time, 
along with periodic fluctuations and changes in variance. 
A series is strictly stationary if  the marginal distribution 
of  Y at time t [P(Yt)] is the same as at any other point in 
time. P(Yt) = P(Yt+k) and P(Yt, Yt+k) does not depend 
on t (t ≥ 1 and k is any integer). This implies that the 
mean, variance, and covariance of  the series Yt are time-
invariant. However, a series is said to be weakly stationary 
if  the following conditions are met:
E(Y1) = E(Y2) = …..= E(Yt) = µ              (1)
Var(Y1 )=Var(Y2 )=.....=Var (Yt )= γ0  (a constant)         (2)
Cov(Y1,Y(1+k)= Cov(Y2,Y(2+k)) = ….Cov(Yt,Y(t+k)) = γk  
(depends only on lag k)               (3)
Because the statistical properties of  time series data 
change over time in non-stationary data, it is difficult 
to make predictions and draw conclusions. We cannot 
determine the appropriate model. Non-stationary data 
may sometimes result in false regressions, meaning the 
regression equation shows a significant relationship 
between two variables when there wasn’t any.
The Dickey-Fuller Test was developed by David Dickey 
and Wayne Fuller in 1979. It tests for the presence 
of  trends and seasonality in data. This test tests the 
presence of  unit roots (Phillips & Perron, 1988) in an 
autoregressive model. If  a unit root is present, it means 
the data is non-stationary suggesting that it is difficult 
to model and forecast the future values. Consider the 
following hypothesis: 
Null Hypothesis (H0) = The model is non-stationary (the 
time series has a unit root)
Alternate Hypothesis (H1) = The model is stationary (the 
time series does not have a unit root).
If  the test statistic produced by the Dickey-Fuller test 
is at the significance level of  0.05, the null hypothesis is 
rejected and the data set suggests stationarity. Again, if  
the test statistics are greater than the significance level 
i.e., >0.05, the time series suggests the non-stationarity 
of  data which implies there is a need for differencing to 
make the series stationary before applying time series 
models such as ARIMA.
It is a method of  transforming a non-stationary time 
series into a stationary time series. It is used in removing 
the trend in the time series (McGonigle et al., 2022). This 
is an important step in preparing data to be used in an 
ARIMA model. When the difference between the current 
period and the previous period is made, it is called first-
order differencing and can be denoted as I(1). It can be 
expressed as:
Δyt  = yt - y(t-1)                (4)
Where yt represents time series data at time t and the 
current value we are trying to model. If  the values exhibit 
non-stationarity properties, the process is repeated twice 
and thus called second-order differencing and can be 



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denoted as I(2). It can be expressed as:
Δ2 yt = (yt  – y(t-1) ) - (y(t-1) – y(t-2))              (5)
Where yt represents time series data at time t and the 
current value we are trying to model. This process is 
continued until the values show stationary properties 
(constant mean and variance). A series that is stationary 
after being differentiated d times is said to be integrated 
of  order d, denoted by I(d). However, when a series is 
stationary without differencing is said to be I(0).

Autoregressive (AR) Model
AR models are used to forecast future values only based 
on their previous values, typically called lags. Thus, the 
forecasted value ‘Y’ and time ‘t’ in AR is the function of  
its past values Yt-1, Yt-2, Yt-3, Yt-4, ………. Thus,
Yt  = f  (Y(t-1), Y(t-2), Y(t-3), Y(t-4),…., εt)              (6)
The model can be mathematically expressed as: 
yt  =β0 +β1 y(t-n) + εt                 (7)
where: yt represents the value of  the variable at time t, β0 
is the constant term, β1 is the coefficient of  the lagged 
variable, yt−n represents the effect of  the nth period’s 
value on the current period, εt is the error term at time t 
and represents the deviation of  the actual value from the 
predicted value based on the model.
AR models that depend only on one lag in the past are 
called First-order Auto-Regressive Model or AR(1) 
models and can be expressed as:
yt = β0 +β1 y(t-1) +εt                (8)
AR models that depend only on two lags in the past are 
called Second-order Auto-Regressive Model or AR(2) 
models and can be expressed as:
yt = β0 +β1 y(t-1) + β2 y(t-2) + εt              (9)
The variables of  interest in the AR model are forecasted 
using the linear combination of  the past values of  the 
variable and the term ‘Autoregression’ indicates that it is 
a regression of  the variable against itself. Hence, the AR 
model of  order p can be represented as:
yt = β0 +β1 y(t-1) + β2 y(t-2) + β3 y(t-3) + β4 y(t-4) + β5 y(t-5) + β6 
y(t-6) + …………… βp y(t-p) + εt            (10)
The AR models are also called long-memory models 
as they can take into account a large range of  past 
observations to predict the current value.

Moving Average (MA) Model
Models used to forecast the series based solely on the 
past errors in the series i.e., error lags are called Moving 
Average (MA) models. In MA, the forecasted value ‘Y’ at 
a time ‘t’ is the function of  the lags of  its error value at 
the time ‘t’,
Yt = f(εt, ε(t-1), ε(t-2), ε(t-3),…………….)             (11)
The model MA(q) can be mathematically expressed as:
yt = µ + εt + θ1 * ε(t-1) + θ2 * ε(t-2) + θ3 * ε(t-3) + θ4 * ε(t-4) + 
…….+ θq * ε(t-q)                (12)
where: yt represents time series data at time t and the 
current value we are trying to model, µ is the mean of  
the time series data and the expected value of  yt when 
all other terms in the equation are zero, εt is the error 
term at time t and represents the deviation of  the actual 

value from the predicted value based on the model, and θ 
represents the weight assigned to the lagged error terms 
in the model.
MA models that depend upon only one error lag are 
called first-order MA models, denoted by
MA(1): yt = µ + εt  + θ1 * ε(t-1)            (13)
The second-order MA model, denoted by
MA(2) is: yt = µ + εt + θ1 * ε(t-1) + θ2 * ε(t-2)           (14)
Moving average models are the short memory models 
since the errors in them don’t last long into the future. 

Auto-Regressive Moving Average (ARMA) Model
ARMA is the combination of  AR and MA models and 
is used to describe the behavior of  the time series and 
to forecast future values based on historical data. The 
ARMA(p,q) model can be mathematically represented as: 
yt = c + β1 y(t-1) + β2 y(t-2) + β3 y(t-3) + ….……..… + βp y(t-p) 
+ θ1 * ε(t-1) + θ2 * ε(t-2) + θ3 * ε(t-3) + ...………… + θq * ε(t-q)  
+ εt               (15)
where: yt represents time series data at time t and the 
current value we are trying to model, c is a constant 
term, β1, β2, β3, ……., βp are the coefficients of  
the autoregressive part, θ1, θ2, θ3, ……, θq are the 
coefficients of  the moving average part, and εt is the 
error term at time t. ARMA model is purely stationary 
without difference and a blend of  AR and MA models.

Auto-Regressive Integrated Moving Average 
(ARIMA) Model
ARIMA models, also known as Box-Jenkins models 
(Montgomery et al., 2015), are a statistical method useful 
for forecasting data based on their temporal structures. 
This model is useful for analyzing and forecasting time 
series exhibiting trends and seasonal patterns. It is, in 
simple terms, an integration of  the Auto Regression 
and Moving Average model with differencing made to 
make the time series stationary. Its parameters can be 
represented as (p, d, q) and can be expressed as ARIMA 
(p, d, q), where, p = number of  lag observations or the 
order for the autoregression, d = order of  differencing 
made to make the data stationary for the non-stationary 
series, and q = order of  the moving average.

Seasonal Auto-Regressive Integrated Moving 
Average (SARIMA) Model
A widely used variation of  the ARIMA model called 
the SARIMA model (Chen et al., 2018) was proposed 
by Box and Jenkins. This model shows the significance 
of  seasonality (Vaswani et al., 2023). It integrates both 
the seasonal and non-seasonal components (Adams et 
al., 2019) and can be represented as: ARIMA (p, d, q) 
(P, D, Q)(s), where, p = non-seasonal Auto Regression 
order, d = non-seasonal differencing order, q = non-
seasonal Moving Average order. Similarly, P = seasonal 
Auto Regression order, D = seasonal differencing order, 
Q = seasonal Moving Average order, and s = period in a 
seasonal pattern.



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Box-Jenkins Approach
The Box-Jenkins approach to modeling was developed 
by George Box and Gwilym Jenkins in the early 1970s. 
This methodology consists of  three major stages: 
Identification, Estimation, and Diagnostic Checking. 
At the identification stage, stationarity is checked. The 
process advances to model estimation if  the data is 
stationary; otherwise, the data is transformed to make 
it stationary through decomposition and differencing 
methods. The Autocorrelation Function (ACF) and 
Partial Auto-Correlation functions (PACFs) are visualized 
in this stage.
After identifying the model structure, parameters for the 
model are estimated by testing least squares estimation, 
such as Mean Error (ME), Root Mean Squared Error 
(RMSE), Mean Absolute Error (MAE), Mean Percentage 
Error (MPE), Mean Absolute Percentage Error (MAPE), 
and Mean Absolute Scale Error (MASE). ME represents 
the average error, whereas RMSE is the square root of  the 
average of  the squared errors. Similarly, MAE represents 
the average of  the absolute errors, MPE is the average 
of  the percentage errors, and MAPE is the average of  
the absolute percentage of  errors. Likewise, MASE 
measures prediction accuracy, and ACF1 is the first-order 
autocorrelation of  the residuals. Lower values indicate a 
better-fitting model. Maximum Likelihood Estimation is 
also one of  the techniques used for estimating models, 
such as Akaike’s Information Criterion (AIC), AICs, 
Bayesian Information Criterion (BIC), etc.
To determine the representativeness of  the model 
regarding the dataset, diagnostic tests are conducted after 
model estimation (Young, 1977). The diagnostic test 
ensures whether or not the model adequately captures the 
underlying patterns and features of  the data. A famous 
test called the Box-Ljung test (Ljung & Box, 1978), used 
in time series analysis, determines the presence/absence 

of  autocorrelations among the residuals. The residuals 
are examined for randomness and autocorrelation, 
i.e., the graphs, test statistics, ACFs, and PACFs of  the 
residuals are used for model verification. The steps of  
Identification, Estimation, and Diagnostics are repeated 
if  the model is not verified as the best model. After 
completing the above-mentioned stages, the model is 
ready to forecast the future values of  any time series data.

RESULTS AND DISCUSSION
A forecast of  the global price of  corn was attempted 
using the SARIMA (Seasonal Auto-Regressive Integrated 
Moving Average) model. R 4.3.2 for Windows was used to 
model and forecast the monthly global price of  corn from 
January 2014 to December 2023, retrieved from FRED 
and converted into a time series object. The time series 
plot (Figure 1) and its lag plot (Figure 2) of  global corn 

Figure 1: Time series plot of  the monthly global price of  
Corn (January 2014 ~ December 2023)
Source: Own computation based on FRED (January 2014 ~ 
December 2023) data

Figure 2: Lag plot of  the monthly global price of  Corn (January 2014 ~ December 2023)
Source: Own computation based on FRED (January 2014 ~ December 2023) data



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prices recorded from January 2014 to December 2023 
show fluctuation over time but show some trends and 
patterns. Seasonal variations are evidenced in the prices 
which can be potentially influenced by weather events, 
harvest periods, demand, etc. There are fluctuations 
within each year. There appear to be periods of  both 
increases and decreases in the prices over the years. The 
price was lowest in 2020 and was a record high in 2022.
Its stationarity was checked using the Augmented 
Dickey-Fuller (ADF) test. The value of  ADF statistic was 
-2.3498 which reflects that the data is weakly stationary. 
The p-value suggests the level of  significance of  any 
observation. The high p-value (typically > α = 0.05) 
suggests that we fail to reject the null hypothesis of  non-
stationarity. Here, the p-value (0.4313) obtained from the 
ADF test for the original data suggested rejecting the null 
hypothesis of  non-stationarity. Autocorrelation (Figure 3) 

and partial autocorrelation (Figure 4) were also visualized. 
In the ACF plot (Figure 3) it can be seen that there is a 
gradual decay of  spikes but never cut off  to zero meaning 
the data needs to be made stationary for further testing.
Using decomposition, the monthly global price of  corn 
time series was decomposed into three components 
- trend, seasonal, and random - and each component 
was visualized (Figure 5) which shows the decomposed 
corn price for the various years recorded. It also can be 
observed that the existence of  the seasonal variation is 
constant over time. The random effect also seems to be 
constant over time. However, the trend of  the series seems 
to be constant till 2020 and then gradually rising upwards 
peaking at 2022 and gradually sliding downwards after 
that. The seasonal component was removed to make the 
data stationary (Figure 6), and the stationarity was again 
tested using the ADF test.

Figure 3: ACF plot of  the monthly global price of  Corn 
(January 2014 ~ December 2023)
Source: Own computation based on FRED (January 2014 ~ 
December 2023) data

Figure 5: Decomposition of  the monthly global price of  
Corn (January 2014 ~ December 2023)
Source: Own computation based on FRED (January 2014 ~ 
December 2023) data

Figure 6: Time series plot of  seasonally adjusted monthly 
global price of  Corn (January 2014 ~ December 2023)
Source: Own computation based on FRED (January 2014 ~ 
December 2023) data

Figure 4: PACF plot of  the monthly global price of  
Corn (January 2014 ~ December 2023)
Source: Own computation based on FRED (January 2014 ~ 
December 2023) data



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Figure 7: Time series plot of  the first difference of  the 
seasonally adjusted monthly global price of  Corn (January 
2014 ~ December 2023)
Source: Own computation based on FRED (January 2014 ~ 
December 2023) data

Figure 9: PACF plot of  the first difference of  the 
seasonally adjusted monthly global price of  Corn (January 
2014 ~ December 2023)
Source: Own computation based on FRED (January 2014 ~ 
December 2023) data

Figure 11: ACF plot of  the twelfth difference of  the 
seasonally adjusted monthly global price of  Corn (January 
2014 ~ December 2023)
Source: Own computation based on FRED (January 2014 ~ 
December 2023) data

Figure 12: PACF plot of  the twelfth difference of  the 
seasonally adjusted monthly global price of  Corn (January 
2014 ~ December 2023)
Source: Own computation based on FRED (January 2014 ~ 
December 2023) data

Figure 8: ACF plot of  the first difference of  the 
seasonally adjusted monthly global price of  Corn (January 
2014 ~ December 2023)
Source: Own computation based on FRED (January 2014 ~ 
December 2023) data

Figure 10: Time series plot of  the twelfth difference of  
the seasonally adjusted monthly global price of  Corn 
(January 2014 ~ December 2023)
Source: Own computation based on FRED (January 2014 ~ 
December 2023) data



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Given the p-value of  0.5211, which is quite high, we 
again failed to reject the null hypothesis and the data is 
still non-stationary. So, differencing (Figures 7 & 10) was 
attempted. The test statistics thus obtained were negative 
and significant
 The p-value was 0.01 in both cases which is < α = 0.05 
suggesting the stationarity of  data. The differenced series 
removed the trends and seasonality, making the data more 
stationary and ready for modeling. ACF (Figures 8 & 11) 
and PACF (Figures 9 & 12) from both cases were also 
visualized. These plots show the ACF and PACF of  the 
global corn price series with 95% confidence limits. It can 
be seen from the ACF plot that the spike is significant 
till the first lag and there are no significant spikes in the 
PACF plot.
In terms of  identifying and fitting the model, the best-
fitting seasonal ARIMA model was determined by using 
auto.arima() function in R which automatically took into 
account the AIC, AICc, or BIC values. The notation in the 

ARIMA model represents the parameters of  the ARIMA 
model. It indicates the order of  the Autoregressive (AR), 
differencing (I), and moving average (MA) components 
of  the model. In this case, an ARIMA (0,1,1)(0,0,1)
[12] model was obtained suggesting the series has a 
seasonal pattern and a first-order non-seasonal moving 
average term and a first-order seasonal moving average 
component has been influencing the series.
Thus, it is ARIMA (0,1,1) for the non-seasonal part and 
ARIMA (0,0,1) for the seasonal part, with a seasonal 
period of  12 months. This simply also suggested a 
moving average term and a seasonal moving average 
term. The coefficient for the MA term was 0.4628, and 
for the seasonal MA was -0.3777 and the estimated 
variance of  the residuals was 105.9 (Table 3). The log-
likelihood of  the model is the measure of  how well the 
model fits the data and it was -446.29 in this case. The 
model was selected based on AIC and was evaluated 
using its summary statistics.

Table 3: Parameters of  the ARIMA(0,1,1)(0,0,1)12 Model
Parameter Estimate Standard Error
Difference 1
MA1 0.4268 0.0919
SMA1 -0.3777 0.1312
Sigma2 = 105.9: Log Likelihood = -446.29
AIC = 898.57, AICc = 898.78, BIC = 906.91
RMSE = 10.16116, MAE = 7.525733, MAPE = 3.665245

Source: Own computation based on FRED (January 2014 ~ December 2023) data

The residual analysis was performed for the model 
verification. It can be seen from the Standardized 
Residuals (Figure 13) that the residuals of  the model 
have zero mean and a constant variance concentrated 
around 2 and -2. It can be visualized from an ACF plot of  
residuals (the second panel of  Figure 13) that there is no 
autocorrelation among the residuals; the autocorrelation 
is zero. This implies zero mean and constant variance 
among the residuals. Hence, the residuals follow a white 
noise process. The adequacy of  the model was also judged 
by the Ljung-Box test and the Box-Ljung test which were 
used to assess the autocorrelation of  the residuals from 
the model where autocorrelation means that the values of  
the series at different points in time are correlated with 
each other.
In the Ljung-Box test, the test statistics Q* are 
compared against a chi-squared distribution with 
degrees of  freedom equal to the number of  lags used 
in the test minus the model degrees of  freedom. The 
p-value (0.9303) obtained suggested no significant 
autocorrelation in the residuals. In the Box-Ljung test 

also, the X-squared statistic is compared against a chi-
squared distribution. The p-value (0.7013) in this test 
also indicated no significant autocorrelation in the 
residuals. This can also be visualized from the p-value 
of  the residuals (the third panel of  Figure 13) that 
its value exceeds the 5% confidence interval showing 
no significant departures from the white noise of  the 
residuals. Also, from the normality plot of  residuals 
(Figure 14), the residuals follow the normal distribution 
process i.e., there is a symmetric distribution of  the 
residuals around the mean residual. Thus, SARIMA 
(0,1,1) (0,0,1) [12] was selected to be the best-fitting 
model for this time series.
Forecasts were generated using the fitted ARIMA model 
and the forecasted values were visualized (Figure 15 and 
Figure 16). The forecast values – January to December 
2025 – seem to be relatively stable over the period, with a 
slight upward trend. The forecast values for each month 
in the first year (2024) are generally lower than those in 
the second year (2025) (Table 4).



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Figure 13: Ljung-Box test of  the seasonally adjusted monthly global price of  Corn (January 2014 ~ December 2023)
Source: Own computation based on FRED (January 2014 ~ December 2023) data

Figure 14: Residual plots of  the seasonally adjusted monthly global price of  Corn (January 2014 ~ December 2023)
Source: Own computation based on FRED (January 2014 ~ December 2023) data

Table 4: The forecasted monthly global price of  Corn for 2024 and 2025
Month Point Forecast Lower 95% Confidence Limit Upper 95% Confidence Limit

2024 2025 2024 2025 2024 2025
January 211.4124 238.2565 191.2432 138.4384 231.5816 338.0746
February 212.1315 238.2565 176.9903 136.8447 247.2727 339.6682
March 212.5306 238.2565 167.1102 135.2757 257.9509 341.2373
April 208.9084 238.2565 155.1393 133.7303 262.6775 342.7827



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May 217.2596 238.2565 156.2742 132.2074 278.2450 344.3056
June 219.4492 238.2565 152.0154 130.7060 286.8831 345.8070
July 229.7617 238.2565 156.4444 129.2253 303.0790 347.2877
August 236.5466 238.2565 157.7841 127.7644 315.3091 348.7486
September 227.4025 238.2565 143.5477 126.3226 311.2574 350.1904
October 228.1253 238.2565 139.4701 124.8992 316.7805 351.6138
November 235.2223 238.2565 142.0137 123.4934 328.4309 353.0196
December 237.9829 238.2565 140.4332 122.1046 335.5326 354.4084

Source: Own computation based on FRED (January 2014 ~ December 2023) data

Figure 15: Forecasted monthly global price of  Corn 
under upper and lower confidence levels
Source: Own computation based on FRED (January 2014 ~ 
December 2023) data

Figure 16: Observed and the forecasted monthly global 
price of  Corn
Source: Own computation based on FRED (January 2014 ~ 
December 2023) data

The fluctuations in global corn prices over the years 
reflect the intricate interplay of  multifaceted factors, 
necessitating a comprehensive understanding of  the 
market dynamics. Global demand for corn is influenced 
by diverse factors, including population growth, dietary 
preferences, industrial uses, and government policies 
related to biofuel production and food security. Shifts 

in these demand drivers can lead to significant price 
fluctuations, affecting stakeholders across the corn supply 
chain, from producers to consumers.
Disruptions in supply chains, whether due to natural 
disasters, trade conflicts, or logistical challenges, can have 
profound effects on corn prices. For instance, extreme 
weather events, such as droughts or floods, can disrupt 
corn production, leading to reduced yields and increased 
prices. Similarly, trade tensions between major corn-
producing countries can impact market access and trade 
flows, affecting price dynamics on a global scale.
Geopolitical events and policy decisions also play a crucial 
role in shaping corn prices. Changes in trade agreements, 
tariffs, and subsidies can influence market conditions, 
creating uncertainties for market participants. Moreover, 
government policies promoting or restricting the use of  
corn-based ethanol as a renewable fuel source can impact 
both demand and prices in the corn market.
Disease outbreaks, such as the spread of  corn diseases 
or pests, pose additional challenges to corn production 
and prices. Crop diseases can devastate yields, leading 
to supply shortages and price spikes. Furthermore, the 
emergence of  new pests or pathogens can necessitate 
costly control measures, adding to production costs and 
potentially driving up prices for consumers.
Sudden climate changes, including shifts in temperature 
and precipitation patterns, pose significant risks to corn 
production worldwide. Climate variability and extreme 
weather events, exacerbated by climate change, can 
disrupt planting schedules, reduce yields, and increase the 
likelihood of  crop failures. These climate-related risks 
underscore the importance of  adaptive strategies and 
resilience-building efforts within the agricultural sector to 
mitigate the impacts of  climate change on corn prices 
and food security.
In recent years, the growing demand for corn-based 
products, such as ethanol and animal feed, has contributed 
to increased competition for corn resources. The 
expansion of  biofuel production, driven by renewable 
energy policies and environmental concerns, has led to 
a surge in corn consumption for ethanol production. 
This heightened demand for corn in non-food sectors 
has implications for food prices and market dynamics, 
underscoring the need for integrated approaches to 
balance competing demands for corn resources.
Moreover, speculation in commodity markets can 



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exacerbate price volatility, amplifying the effects of  supply 
and demand shocks on corn prices. Financial market 
participants, including investors and hedge funds, engage 
in trading corn futures and derivatives, seeking to profit 
from price movements. However, speculative activities 
can introduce additional uncertainties and distortions 
into the corn market, leading to heightened price volatility 
and market inefficiencies.
In light of  these complex dynamics, forecasting corn 
prices becomes imperative for stakeholders across the 
corn supply chain. Accurate price forecasts enable farmers 
to make informed decisions regarding planting, input use, 
and marketing strategies. Similarly, policymakers rely on 
price forecasts to formulate agricultural policies, manage 
food security risks, and mitigate market disruptions. 
Additionally, consumers and food industry stakeholders 
use price forecasts to anticipate changes in food prices 
and adjust consumption patterns accordingly.
Given the importance of  price forecasting in navigating 
the uncertainties of  the corn market, researchers have 
developed and applied various time series forecasting 
models to predict corn prices. These models utilize 
historical price data, along with relevant explanatory 
variables, to generate forecasts of  future price 
movements. By evaluating the performance of  different 
forecasting models and refining their methodologies, 
researchers aim to enhance the accuracy and reliability of  
corn price forecasts, thereby empowering stakeholders to 
make informed decisions in an increasingly complex and 
dynamic market environment.

CONCLUSION
The price of  corn has exhibited fluctuations over the 
years, starting from a relatively low point in early 2014 and 
steadily increasing, peaking around March of  that year. 
These elevated prices persisted throughout 2014 until 
mid-March of  2015, after which they began to decline, 
reaching a nadir in mid-2016. Subsequently, from late 2016 
to 2017, global corn prices displayed some volatility but 
maintained a moderate and stable trajectory compared to 
the preceding years of  2014 and 2015. From 2018 to early 
2019, corn’s price gradually increased, reaching a relatively 
high point by early 2019. During the period from mid-
2019 to 2020, there were some fluctuations in price, but 
overall stability within a moderate range was observed. 
However, from mid-2020 onwards, a significant surge in 
corn prices ensued, culminating in a record high by early 
2022, followed by a gradual decline to lower levels by early 
2024. The application of  the Box-Jenkins methodology 
in time series forecasting effectively identified a seasonal 
pattern in global corn prices, resulting in an ARIMA 
(0,1,1)(0,0,1)[12] model. Furthermore, the model 
pinpointed the influence of  a first-order non-seasonal 
moving average term and a first-order seasonal moving 
average component on the series. The validation of  the 
model’s accuracy was conducted through the analysis of  
the Ljung–Box Q–test and AIC value. Such forecasting 
endeavors offer substantial benefits to farmers, 

stakeholders, policymakers, and investors alike. These 
forecasts provide farmers and stakeholders with valuable 
insights for price adjustments, while policymakers can 
utilize this information to formulate well-informed 
marketing strategies. Additionally, investors can leverage 
these forecasts to make prudent decisions regarding their 
investment choices. This comprehensive approach to 
price forecasting enhances decision-making processes 
across various sectors, facilitating more effective resource 
allocation and risk management strategies. Therefore, 
by furnishing insights for price adjustments, informing 
marketing strategies, and aiding investment decisions, 
these forecasts contribute to more informed decision-
making processes across sectors, ultimately improving 
resource allocation and risk management strategies. 

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