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摘要翻译：
国内生产总值(GDP)是一个重要的经济指标，它汇集了有用的信息，以帮助经济主体和政策制定者进行决策。在这种背景下，GDP预测成为一个强有力的决策优化工具在许多领域。为了在这方面做出贡献，我们研究了应用于巴西国内生产总值的经典时间序列模型和一类状态空间模型的效率。使用的模型有：季节自回归积分滑动平均(SARIMA)和Holt-Winters方法，这是经典的时间序列模型；动态线性模型是一种状态空间模型。基于模型比较的统计指标，动态线性模型给出了分析期内的最佳预测模型和拟合性能，并且显著地融入了增长率结构。
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英文标题：
《Forecasting Quarterly Brazilian GDP: Univariate Models Approach》
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作者：
Kleyton Vieira Sales da Costa and Felipe Leite Coelho da Silva and
Josiane da Silva Cordeiro Coelho
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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：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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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：
Gross domestic product (GDP) is an important economic indicator that aggregates useful information to assist economic agents and policymakers in their decision-making process. In this context, GDP forecasting becomes a powerful decision optimization tool in several areas. In order to contribute in this direction, we investigated the efficiency of classical time series models and the class of state-space models, applied to Brazilian gross domestic product. The models used were: a Seasonal Autoregressive Integrated Moving Average (SARIMA) and a Holt-Winters method, which are classical time series models; and the dynamic linear model, a state-space model. Based on statistical metrics of model comparison, the dynamic linear model presented the best forecasting model and fit performance for the analyzed period, also incorporating the growth rate structure significantly.
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PDF链接：
https://arxiv.org/pdf/2010.13259
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