MulTi - Multiple Time Series Analysis in Economics

在最近的十年中，我们观察到一个强劲增长的需求分析工具解开动态关系（高维）系统的社会人口，经济和金融过程。考虑到更复杂的模型可能难以实现，研究软件面临的挑战也在发生变化。通过满足科学界的需求，我们通过开发一个直观，全面和一致的软件框架为现代多变量时间序列分析做出了贡献：MulTi包括一个用于多个时间序列研究的复杂研究库和分析工具。在这个项目中，我们将联合收割机的透明和基于网络的开发与创新的协作方法相结合。基于Java的原型JMulTi提供了一个广泛的参考实现库，促进了时间序列方法的使用。该软件已在科研、教学和实践中取得成功。然而，我们认为，其潜力可以大大增加。通过阐述一个新颖的可持续发展概念并认识到数据科学的最新趋势，我们从根本上修改了软件，以支持其持续可用性。为此，我们调整其结构，以适应现代工作流程和用户的偏好，从科学界不同的编程环境。为了尽可能地做出最大的贡献，我们将JMulTis库从Java和GAUSS转移到Python。我们将生成的模块发布到不同的存储库，在那里可以免费安装和使用。一个新的基于向导的图形用户界面适合于年轻科学家的培训。为了满足跨学科社区的多方面需求，我们进一步实现了与编程环境R和Stata的接口。我们考虑到最近的文献，如贝叶斯工具，大多还没有被包括在任何软件中，扩大图书馆的模型和方法。结合国家的最先进的频率论方法的发展，我们设计MulTi成为经济学中的复制研究的重要基础。提供知识和培训，我们为研讨会和研讨会创建基于网络的互动教学材料。我们的主要目标是为多变量时间序列分析创建一个独特的，一致的参考库。一些国际知名的时间序列研究人员同意在这个项目上进行合作。在整个项目期间，他们通过方法和技术贡献、监督和传播的方式支持开发。整个开发和开发进度的公开文档在协作平台GitHub上透明地进行。

Within the recent decade, we observe a strongly growing demand for analysis tools unravelling dynamic relationships in (high dimensional) systems of socio-demographic, economic and financial processes. Considering more complex models which can be difficult to implement, the challenges for research software are also shifting. By meeting the needs of the scientific community, we contribute to modern multivariate time series analysis by means of developing an intuitive, comprehensive and consistent software framework: MulTi comprises a sophisticated research library and analysis tool for multiple time series research. In this project, we combine its transparent and web-based development with an innovative collaboration approach. The Java-based prototype JMulTi offers an extensive library of reference implementations promoting the use of time series methods. The software has proven successful in research, teaching and practice. However, we believe that the its potential can be largely increased. Incorporating a novel sustainability concept and recognizing recent trends in data science, we revise the software fundamentally in favor for its continuous usability. To this end, we adapt its structure to modern work flows and users preferences from the scientific community for distinct programming environments. Aiming for strongest possible contribution, we transfer JMulTis library from Java and GAUSS to Python. We publish the resulting module to distinct repositories from where it can be installed and employed freely. A new wizard-based graphical user interface is suitable for the training of young scientists. Meeting the versatile needs of an interdisciplinary community, we implement further interfaces to the programming environments R and Stata. We expand the library for models and methods by taking into account recent literature, such as Bayesian tools that mostly have not been included in any software. Jointly with the development of state-of-the-art frequentist methods, we design MulTi to become an important basis for replication studies in economics. Providing knowledge and training, we create interactive web-based teaching materials for workshops and seminars. Our key objective is to create a unique, consistent reference library for multivariate time series analysis. A number of internationally well-known time series researchers agreed to cooperate on this project. Throughout the project period, they support the development by means of methodical and technical contributions, supervision and dissemination. The entire development and a public documentation of the development progress take place transparently on the collaborative platform GitHub.