在大数据下区域经济增长的多变量VAR系统
Ping Yang1, Fuchang Yu2, Jing Liu1
1School of Business Administration, Shandong Women's University, Jinan 250300, Shandong, China.
Heliyon
|November 11, 2024
概括
大数据分析通过与矢量自回归 (VAR) 系统集成来增强区域经济增长研究. 历史模拟方法提供比马尔科夫链蒙特卡洛算法更准确的风险预测.
科学领域:
- 经济学 经济学 经济学
- 数据科学数据科学数据科学
- 计算方法 计算方法
背景情况:
- 传统研究在数据大小,变量选择和实时分析方面面临局限性.
- 大数据时代需要先进的分析技术来发现发展模式.
研究的目的:
- 将矢量自回归 (VAR) 系统与大数据分析集成,用于研究区域经济增长.
- 模拟经济变化和分析工业转型过程.
主要方法:
- 使用了VAR系统与大数据分析技术相结合.
- 采用马尔科夫链蒙特卡洛 (MCMC) 算法和历史模拟方法进行工业变化分析.
- 使用VAR测试后历史数据观察损失值评估模型准确性.
主要成果:
- 历史模拟方法在0.95的置信度水平下,在风险预测方面表现出更高的准确性.
- 在VAR系统中添加额外的变量导致模型信息丢失和参数数量的增加.
结论:
- 与VAR系统集成的大数据分析为区域经济增长研究提供了强大的框架.
- 在这种情况下,历史模拟方法是更可靠的风险预测方法.
- 在VAR建模中,仔细选择变量至关重要,以防止信息丢失和参数膨胀.
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