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Estimation of Impacts of Global Factors on World Food Prices: A Comparison of Machine Learning Algorithms and Time Series Econometric Models. | LitMetric

AI Article Synopsis

  • - Global food prices have surged, prompting a study that analyzes various global factors' impact on predicting these prices through machine learning and time series econometric models.
  • - The research uses data from January 1991 to May 2021 and finds that machine learning, especially the Multi-layer Perceptron algorithm, outperforms traditional econometric models in predicting food prices.
  • - Key findings indicate that the most influential factor on food prices is the previous month's food prices, followed by raw material, fertilizer, and oil prices, emphasizing how global fluctuations affect food pricing.

Article Abstract

It is a well-felt recent phenomenal fact that global food prices have dramatically increased and attracted attention from practitioners and researchers. In line with this attraction, this study uncovers the impact of global factors on predicting food prices in an empirical comparison by using machine learning algorithms and time series econometric models. Covering eight global explanatory variables and monthly data from January 1991 to May 2021, the results show that machine learning algorithms reveal a better performance than time series econometric models while Multi-layer Perceptron is defined as the best machine learning algorithm among alternatives. Furthermore, the one-month lagged global food prices are found to be the most significant factor on the global food prices followed by raw material prices, fertilizer prices, and oil prices, respectively. Thus, the results highlight the effects of fluctuations in the global variables on global food prices. Additionally, policy implications are discussed.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9957413PMC
http://dx.doi.org/10.3390/foods12040873DOI Listing

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