摘要翻译：
本文运用Gretl模型，运用ARMA、向量ARMA、VAR、状态空间模型和Kalman滤波、转移函数和干预模型、单位根检验、协整检验、波动性模型(ARCH,GARCH,ARCH-M,GARCH-M,Taylor-Schwert GARCH,GJR,TARCH,NARCH,APARCH,EGARCH）对1980-2013年GDP和政府消费支出与总投资(GCEGI)的季度时间序列进行了分析。本文的组织结构如下：（一）定义；㈡回归模型；(III)讨论。此外，本文还发现了GDP和GCEGI之间在短期和长期的独特互动关系，并为政策制定者提供了一些建议。例如，在短期内，GDP对GCEGI的反应是正的，非常显著(0.00248)，而GCEGI对GDP的反应是正的，但不太显著（0.08051）。从长期来看，当前GDP对过去GCEGI的冲击反应是负的和永久性的(0.09229)，而当前GCEGI对过去GDP的冲击反应是负的和暂时性的（0.29821）。因此，政策制定者不应仅仅根据当前和过去的GDP状况来调整当前的GCEGI。虽然增加GCEGI在短期内确实有助于GDP，但显著突然增加GCEGI可能不利于GDP的长期健康。相反，我们建议采取一种平衡、可持续和经济上可行的解决办法，这样，增加GCEGI对当前经济的短期好处往往主要由长期贷款保证，超过或至少等于贷款产生的长期债务对未来经济的负面影响。最后，我发现非正态分布的波动率模型通常比正态分布的波动率模型表现得更好。更具体地说，TARCH-GED在非正态分布组中表现最好，而GARCH-M在正态分布组中表现最好。
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英文标题：
《Comprehensive Time-Series Regression Models Using GRETL -- U.S. GDP and
  Government Consumption Expenditures & Gross Investment from 1980 to 2013》
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作者：
Juehui Shi
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最新提交年份：
2019
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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：
  Using Gretl, I apply ARMA, Vector ARMA, VAR, state-space model with a Kalman filter, transfer-function and intervention models, unit root tests, cointegration test, volatility models (ARCH, GARCH, ARCH-M, GARCH-M, Taylor-Schwert GARCH, GJR, TARCH, NARCH, APARCH, EGARCH) to analyze quarterly time series of GDP and Government Consumption Expenditures & Gross Investment (GCEGI) from 1980 to 2013. The article is organized as: (I) Definition; (II) Regression Models; (III) Discussion. Additionally, I discovered a unique interaction between GDP and GCEGI in both the short-run and the long-run and provided policy makers with some suggestions. For example in the short run, GDP responded positively and very significantly (0.00248) to GCEGI, while GCEGI reacted positively but not too significantly (0.08051) to GDP. In the long run, current GDP responded negatively and permanently (0.09229) to a shock in past GCEGI, while current GCEGI reacted negatively yet temporarily (0.29821) to a shock in past GDP. Therefore, policy makers should not adjust current GCEGI based merely on the condition of current and past GDP. Although increasing GCEGI does help GDP in the short-term, significantly abrupt increase in GCEGI might not be good to the long-term health of GDP. Instead, a balanced, sustainable, and economically viable solution is recommended, so that the short-term benefits to the current economy from increasing GCEGI often largely secured by the long-term loan outweigh or at least equal to the negative effect to the future economy from the long-term debt incurred by the loan. Finally, I found that non-normally distributed volatility models generally perform better than normally distributed ones. More specifically, TARCH-GED performs the best in the group of non-normally distributed, while GARCH-M does the best in the group of normally distributed.
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PDF链接：
https://arxiv.org/pdf/1412.5397