The impact of the COVID-19 pandemic on business expectations

We document and evaluate how businesses are reacting to the COVID-19 crisis through August 2020. First, on net, firms see the shock (thus far) largely as a demand rather than supply shock. A greater share of firms report significant or severe disruptions to sales activity than to supply chains. We compare these measures of disruption to their expected changes in selling prices and find that, even for firms that report supply chain disruptions, they expect to lower near-term selling prices on average. We also show that firms are engaging in wage cuts and expect to trim wages further before the end of 2020. These cuts stem from firms that have been disproportionally negatively impacted by the pandemic. Second, firms (like professional forecasters) have responded to the COVID-19 pandemic by lowering their one-year-ahead inflation expectations. These responses stand in stark contrast to that of household inflation expectations (as measured by the University of Michigan or the New York Fed). Indeed, firms’ one-year-ahead inflation expectations fell precipitously (to a series low) following the onset of the pandemic, while household measures of inflation expectations jumped markedly. Third, despite the dramatic decline in firms’ near-term inflation expectations, their longer-run inflation expectations have remained relatively stable.     

Long-term forecasting of fuel demand at theater entry points

Managing the distribution of fuel in theater requires Army fuel planners to forecast demand at the strategic level to ensure that fuel will be in the right place, at the right time, and in the amounts needed. This work presents a simulation approach to forecasting that accounts for the structure of the supply chain network when aggregating the demand of war fighters across the theater over the forecasting horizon. The resulting empirical distribution of demand at the theater entry point enables planners to identify forecast characteristics that impact their planning process, including the amplitudes and temporal positions of peaks in demand, and the estimated lead time to the point of use. Experimentation indicates that the forecasts are sensitive to the pattern of war fighter demand, the precise structure of the in-theater supply chain network, and the constraints and uncertainty present in the network, all of which are critical planning considerations.     

我国出口汇率传递的非对称性:来自中日贸易的证据

文章扩展了Yang（2007）的厂商定价模型，对汇率传递非对称性、特点和成立条件进行理论分析，发现对称传递需要满足严格的条件，而现实中更常见的是非对称汇率传递。在一定条件下，商品需求弹性越大，越可能呈现出口国货币贬值传递率高而升值传递率低的特点。利用2000年1月至2011年12月我国出口日本的966种商品价格数据，发现汇率波动向价格传递具有非对称性：（1）人民币升值时，日元价格上涨较少；而贬值时，价格下降较多。我国出口商品的需求弹性较大，在日本市场上面对的是一个强竞争结构。（2）若月度升值超过一定幅度（测算约为2.43%），传递率又会有所上升，说明尽管日元价格易跌难涨，但在升值导致成本上涨较多、明显挤压利润时，厂商不得不适当提高日元售价。采用2000年至2018年9月日本从中国进口单位价值指数进行稳健性检验，得出类似结论。现阶段稳定的人民币名义汇率对我国出口企业是更为有利的。     

A hybrid machine learning model for forecasting a billing period’s peak electric load days

Many models have been studied for forecasting the peak electric load, but studies focusing on forecasting peak electric load days for a billing period are scarce. This focus is highly relevant to consumers, as their electricity costs are determined based not only on total consumption, but also on the peak load required during a period. Forecasting these peak days accurately allows demand response actions to be planned and executed efficiently in order to mitigate these peaks and their associated costs. We propose a hybrid model based on ARIMA, logistic regression and artificial neural networks models. This hybrid model evaluates the individual results of these statistical and machine learning models in order to forecast whether a given day will be a peak load day for the billing period. The proposed model predicted 70% (40/57) of actual peak load days accurately and revealed potential savings of approximately USD $80,000 for an American university during a one-year testing period.     

M5 competition uncertainty: Overdispersion,distributional forecasting,GAMLSS, and beyond

The M5 competition uncertainty track aims for probabilistic forecasting of sales of thousands of Walmart retail goods. We show that the M5 competition data face strong overdispersion and sporadic demand, especially zero demand. We discuss modeling issues concerning adequate probabilistic forecasting of such count data processes. Unfortunately, the majority of popular prediction methods used in the M5 competition (e.g. lightgbm and xgboost GBMs) fail to address the data characteristics, due to the considered objective functions. Distributional forecasting provides a suitable modeling approach to overcome those problems. The GAMLSS framework allows for flexible probabilistic forecasting using low-dimensional distributions. We illustrate how the GAMLSS approach can be applied to M5 competition data by modeling the location and scale parameters of various distributions, e.g. the negative binomial distribution. Finally, we discuss software packages for distributional modeling and their drawbacks, like the R package gamlss with its package extensions, and (deep) distributional forecasting libraries such as TensorFlow Probability.     

Forecasting upon a star: Forecasting or wishful thinking?

The purpose of this paper was to perform a detailed analysis of the challenges faced while developing and using econometric models to forecast future transportation demand. To this end, a comparative analysis of the state of practice and state of the art was undertaken on a concrete example – Viracopos Airport in Brazil. A review of relevant technical and scientific literature identified a number of approaches and each representative example was synthesized into a specification “template”. We then compared the performance of each “template” with the observed demand, through an intra-series forecast. A general finding was that econometric model specifications proved to be somewhat homogenous and simpler in nature, with results indicating a relatively small difference in fit and forecast capability across models. Even with the elimination of what is typically considered the main culprit for deviations – the forecast uncertainty of the explanatory (input) variables – the forecast is still subject to sizeable deviation. To address this issue, we proposed developing some sanity check indexes, particularly relevant for long-term forecasts. We conclude that the challenges faced at the Viracopos Airport Concession were far from econometric ones, that the success of the demand forecast and the concession itself required more than a well estimated econometric model. Finally, regarding investment obligations within the concession agreement, we strongly recommend making them conditional to meeting demand milestones, given the inherent unpredictability in forecasts.     

工业体系和民营企业兴起——基于252家中国最大民营制造业企业的经验研究

主流经济学家把改革开放后民营企业的大规模兴起，主要归因为市场经济条件下发挥企业家精神和遵循比较优势的必然结果。但是这无法解释为什么民营企业在兴起之初就广泛和快速地进入到各个工业，尤其是技术和资本密集型工业。基于中国工业史和252家中国最大民营制造业企业创业发展史的历史证据，文章找回"工业体系"这一被主流所忽略的重大历史现象和学术概念，从而首次提出一种理解中国民营企业发展独特性的结构性解释。文章发现：从计划经济时期继承而来的门类齐全和独立完整的工业体系，是解释民营企业创业和发展独特性的关键变量；已有的由国有企业和科研院所组成的工业体系，从市场机会、技术能力和组织资源等方面塑造了民营企业的创业选择和发展路径。所以，中国民营企业的快速发展不是仅凭自由市场就会自动出现的普遍现象，而是在市场条件下充分利用工业体系提供的技术和市场条件才得以出现的特殊现象。     

非洲铁路发展需求分析与市场开发策略

中资企业面向非洲铁路市场,多以"广覆盖"的形式投入开发,一定程度上造成了资源浪费。为优化中资企业在非洲铁路市场布局以及资源配置,在分析非洲铁路发展现状、铁路网发展影响因素基础上,基于铁路系统需求度、铁路发展需求度分析非洲铁路发展需求,提出了非洲大陆49个国家高、较高、中、低4个等级的铁路发展需求度,同时提出非洲铁路市场开发策略建议,即:优化非洲铁路市场开发资源配置,分区域实施铁路市场开发及项目建设,创新合作模式、拓宽资金来源。     

经济开放与货币需求:国际金融风险及持币成本的测度

在中国开放经济体制下的基准货币需求模型中,本文将源于国际金融市场的持币成本设为遗漏潜变量,并构建特定的国际金融综合指数(CIFI)作为该潜变量的测度。借鉴机器学习与测度理论,本文利用对数误差修正模型提出了分步降维的CIFI构造算法,构造了长期CIFI和短期CIFI。结果表明,CIFI构造中的无监督降维步骤有助于减少高维金融数据中的冗余信息。实证分析发现,国际机会成本对中国货币需求具有规律性的前导影响,而在2007至2008年国际金融危机期间,央行的应急措施对长期CIFI所代表的非均衡冲击起到明显的阻截效果,对短期CIFI的影响基本是持续不变的。通过综合指数构造与宏观货币需求模型的算法连接,可以利用CIFI的构成结构从前导时间与影响强度两方面追踪冲击货币需求的国际金融风险的具体来源,这为宏观决策者监测国际金融市场提供了颇有规律的信息。在方法论上,本研究为如何利用模型监测国际金融市场影响宏观经济开辟了一条新路。     

A novel model to manage air cargo disruptions caused by global catastrophes such as Covid-19

The outbreak of the COVID-19 pandemic has drastically disrupted the air cargo industry. This disruption has taken many directions, one of which is the demand imbalance which occurs due to the sudden change in the cargo capacity, as well as demand. Therefore, the random change leads to excessive demand in some routes (hot-selling routes), while some other routes suffer from a big shortage of demand (underutilized routes). Routes are substitutable when there are several adjacent airports in the Origin & Destination (O&D) market. In this market, demand imbalance between substitutable routes occurs because of the above reasons. To tackle the demand imbalance problem, a novel model is introduced to estimate the quantity combinations which maintains the balance between underutilized and hot-selling routes. This model is a variant of the classic Cournot model which captures different quantity scenarios in the form of the best response for each route compared to the other. We then cultivate the model by integrating the Puppet Cournot game with the quantity discount policy. The quantity discount policy is an incentive which motivates the freight forwarders to increase their orders in the underutilized routes. After conducting numerical experiments, the results reveal that the profit can increase up to 25% by using the quantity discount. However, the quantity discount model is only applicable when the profit increase in the hot-selling route is greater than the profit decrease in the underutilized route.