Modeling and predicting U.S. recessions using machine learning techniques

The most representative machine learning techniques are implemented for modeling and forecasting U.S. economic activity and recessions in particular. An elaborate, comprehensive, and comparative framework is employed in order to estimate U.S. recession probabilities. The empirical analysis explores the predictive content of numerous well-followed macroeconomic and financial indicators, but also introduces a set of less-studied predictors. The predictive ability of the underlying models is evaluated using a plethora of statistical evaluation metrics. The results strongly support the application of machine learning over more standard econometric techniques in the area of recession prediction. Specifically, the analysis indicates that penalized Logit regression models, k-nearest neighbors, and Bayesian generalized linear models largely outperform ‘original’ Logit/Probit models in the prediction of U.S. recessions, as they achieve higher predictive accuracy across long-, medium-, and short-term forecast horizons.     

Can satellite‐based weather index insurance improve the hedging of yield risk of perennial non‐irrigated olive trees in Spain?*

Olive oil yields fluctuate strongly due to their dependence on sufficient precipitation. An interesting option to hedge the yield risk in olive cultivation could be satellite‐based weather index insurance. Therefore, we implement index insurance as a hedging alternative for non‐irrigated olive groves using MODerate‐resolution Imaging Spectroradiometer (MODIS) satellite data. For this purpose, we focus on the Spanish region of Andalusia, given its importance in olive production at the international level. We calculate three satellite indices: the Vegetation Condition Index (VCI), the Temperature Condition Index (TCI) and the Vegetation Health Index (VHI). Meteorological indices related to temperature and precipitation are used as benchmarks. Firstly, we estimate the periods that have the greatest influence on the critical vegetative phase of olives, which extends from March to September. Based on the indices, insurance contracts are designed using a copula approach, which is then employed to evaluate their hedging effectiveness. On average, the hedging effectiveness of VCI‐, VHI‐ and TCI‐based weather index insurance contracts amounts to 38 per cent, 38 per cent and 29 per cent, respectively. Moreover, VCI‐ and VHI‐based weather index insurance contracts outperform traditional weather index insurance contracts based on precipitation (by 29 per cent) and temperature (by 16 per cent) indices.     

太阳神树、太阳崇拜与太阳历法

从三星堆遗址中出土的许多考古材料都与古蜀人的太阳崇拜和历法有关，而作为典型器物之一的青铜“太阳神树”，就是古蜀人太阳崇拜和使用太阳历法的最佳体现。它不仅具有“日、鸟、天、龙、树”等宗教崇拜的象征意义和神话色彩，又是古蜀时期历法等科学知识的结晶，它克分反映了三星堆文化中辩证、和谐的思维和审美意识，内涵极为丰富，极具研究价值。     

黄土高原地区退耕还林(草)的基本思路

文章分析了我国黄土高原地区坡耕地引起水土流失、肥力低下、水源缺乏、灾害频繁的四大问题,根据黄土高原生态环境脆弱的具体情况,提出了以下对策:①提高思想认识;②保证人均一定的农田;③实现农林牧业最佳结合;④采取合理的措施;⑤健全保障机制;⑥正确处理各种关系。     

Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time series

In this study, we addressed the problem of point and probabilistic forecasting by describing a blending methodology for machine learning models from the gradient boosted trees and neural networks families. These principles were successfully applied in the recent M5 Competition in both the Accuracy and Uncertainty tracks. The key points of our methodology are: (a) transforming the task into regression on sales for a single day; (b) information-rich feature engineering; (c) creating a diverse set of state-of-the-art machine learning models; and (d) carefully constructing validation sets for model tuning. We show that the diversity of the machine learning models and careful selection of validation examples are most important for the effectiveness of our approach. Forecasting data have an inherent hierarchical structure (12 levels) but none of our proposed solutions exploited the hierarchical scheme. Using the proposed methodology, we ranked within the gold medal range in the Accuracy track and within the prizes in the Uncertainty track. Inference code with pre-trained models are available on GitHub.¹     

Fifty Years of Classification and Regression Trees

Fifty years have passed since the publication of the first regression tree algorithm. New techniques have added capabilities that far surpass those of the early methods. Modern classification trees can partition the data with linear splits on subsets of variables and fit nearest neighbor, kernel density, and other models in the partitions. Regression trees can fit almost every kind of traditional statistical model, including least‐squares, quantile, logistic, Poisson, and proportional hazards models, as well as models for longitudinal and multiresponse data. Greater availability and affordability of software (much of which is free) have played a significant role in helping the techniques gain acceptance and popularity in the broader scientific community. This article surveys the developments and briefly reviews the key ideas behind some of the major algorithms.     

Loss given default for leasing: Parametric and nonparametric estimations

This study employs a dataset from three German leasing companies with 14,322 defaulted leasing contracts to analyze different approaches to estimating the loss given default (LGD). Using the historical average LGD and simple OLS-regression as benchmarks, we compare hybrid finite mixture models (FMMs), model trees and regression trees and we calculate the mean absolute error, root mean squared error, and the Theil inequality coefficient. The relative estimation accuracy of the methods depends, among other things, on the number of observations and whether in-sample or out-of-sample estimations are considered. The latter is decisive for proper risk management and is required for regulatory purposes. FMMs aim to reproduce the distribution of realized LGDs and, therefore, perform best with respect to in-sample estimations, but they show poor performance with respect to out-of-sample estimations. Model trees, by contrast, are more robust and outperform all other methods if the sample size is sufficiently large.     

阔叶红松林的保护和培育的研讨

结合阔叶红松林的价值和现状，本文提出切实可行的阔叶红松林保护和培育措施     

邯郸市主城区行道树绿化现状与配置技巧的探讨

行道树是城市园林绿化的骨架,是体现城镇园林绿化水平的重要标志。本文针对邯郸市主城市行道树的绿化现状及其存在的问题,根据行道树树种选择原则和配置技巧的探讨,提出了适合邯郸市生态环境的行道树树种,且对行道树现有的配置方案进行了探讨,提出进一步的优化方式。