摘要翻译：
在几项发展中，经济复杂性(EC)领域已经显著地看到了两项新技术的引入。一个是自举选择性可预测性方案(SPSb)，它可以提供各国国内生产总值的定量预测。另一种，隐马尔可夫模型(HMM)正则化，对文献中典型使用的数据集进行去噪。我们沿着三个不同的方向为欧共体做出贡献。首先，我们证明了SPSb算法对一种著名的统计学习技术Nadaraya-Watson核回归的收敛性。后者的时间复杂度显著降低，产生确定性结果，并且可以与SPSb互换进行预测。其次，我们研究了HMM正则化对乘积复杂度和logPRODY度量的影响。我们发现logPRODY模型的原始解释证实了它描述了产品的全球市场结构的变化，新的见解允许复杂性度量的新解释，对此我们提出了修改。第三，我们探讨了规则化对数据的新影响。我们发现它降低了噪声，并首次观察到它增加了出口网络邻接矩阵中的嵌套性。
---
英文标题：
《Complexity of products: the effect of data regularisation》
---
作者：
Orazio Angelini, Tiziana Di Matteo
---
最新提交年份：
2018
---
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。
--
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
--

---
英文摘要：
  Among several developments, the field of Economic Complexity (EC) has notably seen the introduction of two new techniques. One is the Bootstrapped Selective Predictability Scheme (SPSb), which can provide quantitative forecasts of the Gross Domestic Product of countries. The other, Hidden Markov Model (HMM) regularisation, denoises the datasets typically employed in the literature. We contribute to EC along three different directions. First, we prove the convergence of the SPSb algorithm to a well-known statistical learning technique known as Nadaraya-Watson Kernel regression. The latter has significantly lower time complexity, produces deterministic results, and it is interchangeable with SPSb for the purpose of making predictions. Second, we study the effects of HMM regularization on the Product Complexity and logPRODY metrics, for which a model of time evolution has been recently proposed. We find confirmation for the original interpretation of the logPRODY model as describing the change in the global market structure of products with new insights allowing a new interpretation of the Complexity measure, for which we propose a modification. Third, we explore new effects of regularisation on the data. We find that it reduces noise, and observe for the first time that it increases nestedness in the export network adjacency matrix.
---
PDF链接：
https://arxiv.org/pdf/1808.08249