AI 中文总结
该研究设计了一套R软件包，结合C++效率与R便利性，实现多种高级计量模型，用于宏观经济预测，可提升预测表现，助力相关原创研究。
AI 中文摘要
我们推出一套用于宏观经济预测的R软件包，其利用了先进的贝叶斯、结构、多变量、动态、分层、非线性及非高斯模型。该软件包可同时支持结构分析与预测分析，适用于各类类型、维度及采样频率的时间序列数据。每增加一项功能都会提升计算复杂度，为应对这一挑战，我们的软件设计包含精心筛选的模型、用C++实现的高效算法、先进的计量与数值方法、对复杂输入输出对象的稳健处理，以及标准化工作流程。该方法结合了C++的计算效率与R的数据处理便利性。我们通过示例表明，我们的软件包可助力原创研究贡献，例如：相较于带中心化随机波动率的模型，带非中心化随机波动率的向量自回归模型能提升密度预测与点预测的表现。
英文摘要
We present a suite of R packages for macroeconomic forecasting that leverages advanced Bayesian, structural, multivariate, dynamic, hierarchical, non-linear, and non-Gaussian models. The suite enables both structural and predictive analyses, and is adapted to time series data across various types, dimensions, and sampling frequencies. Each additional feature increases computational complexity. To address this challenge, our software design incorporates a carefully curated selection of models, efficient algorithms implemented in C++, advanced econometric and numerical methods, robust handling of complex input and output objects, and standardised workflows. This approach combines the computational efficiency of C++ with the convenience of working with data in R. We demonstrate that our packages facilitate original research contributions in forecasting, as illustrated by our example in which vector autoregressions with non-centred stochastic volatility enhance density and point predictions relative to models with centred stochastic volatility.
