A fast and scalable ensemble of global models with long memory and data partitioning for the M5 forecasting competition

This work presents key insights on the model development strategies used in our cross-learning-based retail demand forecast framework. The proposed framework outperforms state-of-the-art univariate models in the time series forecasting literature. It has achieved 17th position in the accuracy track of the M5 forecasting competition, which is among the top 1% of solutions.     

Combining forecasts for universally optimal performance

There are two potential directions of forecast combination: combining for adaptation and combining for improvement. The former direction targets the performance of the best forecaster, while the latter attempts to combine forecasts to improve on the best forecaster. It is often useful to infer which goal is more appropriate so that a suitable combination method may be used. This paper proposes an AI-AFTER approach that can not only determine the appropriate goal of forecast combination but also intelligently combine the forecasts to automatically achieve the proper goal. As a result of this approach, the combined forecasts from AI-AFTER perform well universally in both adaptation and improvement scenarios. The proposed forecasting approach is implemented in our R package AIafter, which is available at https://github.com/weiqian1/AIafter.     

基于标准数据的创新程度提升路径研究

创新程度在资源配置中的作用日趋明显，但目前创新程度常以专利作为测量值。随着标准在产业发展中引导性增强，作为创新重要产出的标准在创新程度提升中的地位日渐凸显。提出一个以标准数据为依据，通过对标准创新程度值的测量，结合国际国内创新程度四分图模型，研究具体领域创新程度提升路径。以信息技术应用领域标准为分析对象，结合国际标准化组织颁布标准和中国标准，确定信息技术在各应用领域创新现状。结果显示：信息技术在办公系统和银行业务两个领域应用的国际国内创新程度都很低，应在完成产业升级后逐渐淘汰；其在健康关怀领域应用的国际发展前景良好但国内创新程度很低，是重点研究领域。随后，对信息技术在健康关怀领域应用的国际和国内标准具体内容进行对比分析，发现中国与国际发展存在差距，并以国际国内创新程度为依据对位于四分图不同区域的信息技术应用领域创新程度提升提出合理建议。     

Stock replenishment policies for a vendor-managed inventory in a retailing system

Using Vendor Managed Inventory (VMI), the vendor determines the replenishment decisions at the location of buyers (retailers). This strategy is used primarily for handling demand fluctuations stemming from the Bullwhip effect, leading the system to prevent from holding excessive inventory that result in a reduction in the overall cost of the supply chain. The main advantages of VMI for vendors are higher levels of accessibility to inventory information and more direct contact with the customers. Similarly, VMI has some pros for the buyers, such as shared risk with upper levels of supply chain and reduction in their holding costs of inventory. In this paper, a vendor-managed inventory system is developed containing one vendor and two buyers in which the main assumption is that back-ordering and lost sales are permitted. In this system, (r, Q) and (R, T) replenishment policies are compared according to their performances to see which one performs more cost-efficiently when partial back-ordering is allowed. In accordance, mathematical models utilizing (r, Q) and (R, T) replenishment policies are developed, and algorithms for deriving the optimal replenishment decision variables are proposed. Moreover, significant differences between the two replenishment policies are discussed. The main finding obtained by this research is that when shortage is permitted, both (r, Q) and (R, T) replenishment policies under VMI have pros and cons in different contexts.     

基于组态视角的众创空间创客集聚机制研究

以江苏省383家众创空间为样本，应用组态思维和QCA方法整合资源与服务两个层面的6个条件，探讨形成众创空间创客集聚差异的多重并发和复杂机理。结果发现：①众创空间高创客集聚存在服务主导型、场地-创客教育主导型、全要素型3个组态，其中，服务主导型、场地-创客教育主导型能深层次、强有力地影响众创空间高创客集聚；②众创空间非高创客集聚存在单纯地产式、单纯线下投资促进、单纯线上投资促进3种组态，且对众创空间非高创客集聚的影响基本相当；③资金资源不充足、场地资源不充足与人员服务不充足在解释非高创客集聚时具有替代作用。     

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.     

Incorporating active adjustment into a financing based model of capital structure

The conventional partial adjustment model, which focuses on leverage evolution, has difficulty identifying deliberate capital structure adjustments as it confounds financing decisions with the mechanical autocorrelation of leverage. We propose and estimate a financing-based partial adjustment model that separates the effects of financing decisions on leverage evolution from mechanical evolution. The speed of adjustment (SOA) is firm-specific and stochastic, and active targeting of capital structure has a multiplier effect that depends on the size of financial deficit. Overall, we find expected SOA from active rebalancing (30%) more than doubles what is expected from mechanical mean reversion alone (13%).     

Fiscal rules to tame the political budget cycle: Evidence from Italian municipalities

The paper provides evidence that fiscal rules can limit the political budget cycle. It uses data on Italian municipalities during the 2000s and shows that: 1) municipalities are subject to political budget cycles in capital spending; 2) the Italian sub-national fiscal rule (Domestic Stability Pact, DSP) introduced in 1999 has been enforced by the central government; 3) municipalities subject to the fiscal rule show more limited political budget cycles than municipalities not subject to the rule. In order to identify the effect, we rely on the fact that the domestic fiscal rule does not apply to municipalities below 5000 inhabitants. We find that the political budget cycle increases real capital spending by about 10–20 percent on average in the years prior to municipal elections and that municipalities subject to the DSP show a pre-electoral increase in capital spending which is only a quarter of the one of municipalities not subject to the rule.     

Non-linear Gaussian sovereign CDS pricing models

Prior literature indicates that quadratic models and the Black–Karasinski model are very promising for CDS pricing. This paper extends these models and the Black [J. Finance 1995, 50, 1371–1376] model for pricing sovereign CDS’s. For all 10 sovereigns in the sample quadratic models best fit CDS spreads in-sample, and a four factor quadratic model can account for the joint effects on CDS spreads of default risk, default loss risk and liquidity risk with no restriction to factors correlation. Liquidity risk appears to affect sovereign CDS spreads. However, quadratic models tend to over-fit some CDS maturities at the expense of other maturities, while the BK model is particularly immune from this tendency. The Black model seems preferable because its out-of-sample performance in the time series dimension is the best.     

Club convergence and drivers of digitalization across Indian states

Disparity in the level of digitalization is a crucial driver of economic inequality in an economy. Although a pocket of its population is still bereft of the benefits of digitalization, India currently has the second highest number of internet subscribers in the world despite the nation's late adoption of digital technology. An accurate assessment of the current state of digitalization in the country is required for devising effective initiatives towards building a Digital India and bridging the nation's internal digital divide. Considering 17 major Indian states and 21 variables for 10 years, we constructed a composite index of digitalization with the help of Principal Component Method (PCM). This paper identifies factors responsible for the digitalization divide across states. In the second part of the analysis, the study confirms, with the help of club convergence test, the absence of overall convergence towards digitalization and the existence of the non-convergent group. This paper also shows that the non-convergent group of states are at the bottom of the ranking table which indicates the need for greater attention to initiatives to bridge the digital divide.