使用非线性自回归分布式滞后和机器学习算法预测和揭示孟加拉国总出口的受阻因素
Tanzin Akhter1, Tamanna Siddiqua Ratna1, Ferdous Ahmed2
1Department of Quantitative Sciences, International University of Business Agriculture and Technology, (IUBAT), 4 Embankment Drive Road, Sector -10, Uttara, Dhaka, Bangladesh.
Heliyon
|September 16, 2024
概括
油价上成为孟加拉国面临的挑战.
科学领域:
- 经济学 经济学 经济学
- 计量经济学 计量经济学
- 能源经济学 能源经济学
背景情况:
- 孟加拉国等新兴经济体面临着全球油价上带来的重大挑战.
- 之前关于石油价格冲击影响的研究主要集中在发达国家,忽视了新兴经济体.
- 孟加拉国不断增加的石油消费增加了其对价格波动的脆弱性.
研究的目的:
- 调查油价波动对孟加拉国出口总收入的影响.
- 在不同的石油价格情景下预测孟加拉国总体出口量.
- 分析油价变化对出口业绩的不对称影响.
主要方法:
- 非线性自回归分布式滞后 (NARDL) 模型应用于1991-2021年的数据.
- 对称性测试用于评估出口量与石油价格之间的非线性关系.
- 先知和长短期记忆 (LSTM) 模型用于预测出口量和评估预测准确性.
主要成果:
- 长期分析表明,正面和负面的石油价格冲击都会增加出口收入.
- 短期分析显示,油价变化对出口产生了显著的负面影响.
- 短期内,通货膨胀会对出口收入产生负面影响,但从长远来看会产生积极影响.
- LSTM模型在先知模型上表现出优越的预测性能,其低RMSE为1.88.
结论:
- 该研究强调了孟加拉国出口部门多元化的必要性,以减轻与油价波动相关的风险.
- 政策干预应考虑石油价格和通货膨胀对出口收入的不对称和时间变化的影响.
- 使用像LSTM这样的先进机器学习技术进行准确的预测对于脆弱国家的经济规划至关重要.
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