An Elastic Net Based Algorithm for China Agriculture GDP Prediction

Zihan Qiu ()
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Zihan Qiu: Xiamen University

A chapter in Proceedings of the 2022 International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2022), 2022, pp 843-849 from Springer

Abstract: Abstract Gross domestic product (GDP) refers to the final result of production activities of all resident units in a country or region within a certain period of time. There are a variety of GDP forecasting methods, which can be classified into three types: Time Series Analysis, Regression Analysis and VAR Model. In our paper, we utilize the agricultural yields data to predict the agriculture GDP, that can be seen as a regression model. We adopt Elastic net linear regression using the penalties from both the lasso and ridge techniques to regularize regression models. We evaluate our result using the metrics of Mean Absolute Error (MAE). The lower MAE, the better performance the model will owns. From the result, Elastic Net method owns the lowest MAE score 2.34. In contrast, the other methods like Linear Regression, Lasso, Ridge and VAR’s MAE are 3.25, 4.25, 3.06, 4.45 respectively.

Keywords: GDP prediction; Regression Analysis; Elastic Net; Mean Absolute Error (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-052-7_96

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DOI: 10.2991/978-94-6463-052-7_96

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