摘要翻译：
经济数据统计方法领域的进步为设计有效的军事管理政策的迫切需要铺平了道路。就2019年的军事支出而言，印度排名第三。因此，本研究旨在利用Box-Jenkins ARIMA模型对印度未来时期的军费开支进行时间序列预测。该模型是根据SIPRI从1960年到2019年的60年印度军费开支数据集生成的。对趋势进行了分析，以生成最适合预测的模型。该研究强调了最小AIC值，并涉及ADF测试（增强的Dickey-Fuller）将支出数据转换为平稳形式以生成模型。为了有效的预测，本文还着重于绘制残差分布。本文提出了一个ARIMA(0,1,6）模型用于印度军费的最优预测，预测精度为95.7%。因此，该模型充当移动平均(MA)模型，并预测到2024年印度军费开支的稳态指数增长36.94%。
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英文标题：
《Forecasting and Analyzing the Military Expenditure of India Using
  Box-Jenkins ARIMA Model》
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作者：
Deepanshu Sharma and Kritika Phulli
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：General Economics        一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  The advancement in the field of statistical methodologies to economic data has paved its path towards the dire need for designing efficient military management policies. India is ranked as the third largest country in terms of military spender for the year 2019. Therefore, this study aims at utilizing the Box-Jenkins ARIMA model for time series forecasting of the military expenditure of India in forthcoming times. The model was generated on the SIPRI dataset of Indian military expenditure of 60 years from the year 1960 to 2019. The trend was analysed for the generation of the model that best fitted the forecasting. The study highlights the minimum AIC value and involves ADF testing (Augmented Dickey-Fuller) to transform expenditure data into stationary form for model generation. It also focused on plotting the residual error distribution for efficient forecasting. This research proposed an ARIMA (0,1,6) model for optimal forecasting of military expenditure of India with an accuracy of 95.7%. The model, thus, acts as a Moving Average (MA) model and predicts the steady-state exponential growth of 36.94% in military expenditure of India by 2024.
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