中国房地产周期波动研究----一个基于时序全局主成分方法的分析

在现有的关于我国房地产周期波动的研究中,所使用的研究工具大多都不能深入刻画我国房地产的周期波动规律和影响我国房地产周期波动的主成分。为此,我们利用时序全局主成分这一新方法,研究了自1998年-2008年我国房地产周期波动情况及其主成分。我们发现,自1998年第四季度至2008年第3季度,我国房地产增长周期大概经历了两个完整的周期,他们分别是1999年1季度到2003年4季度,2004年1季度到2007年4季度,2008年开始我国房地产进入了向下调整的过程中。从主成分的综合影响度来看,对房地产业增长率影响较大的因素依次是：完成开发投资额增长率、施工面积增长率、空置面积增长率、人均收入水平增长率、GDP增长率、消费水平增长率、销售价格增长率、建筑贷款增长率、广义货币供应M2增长率。而其他因素的增长率对房地产业的综合影响较小。     

长江经济带人才集聚水平测度及时空演变研究——基于价值链视角

基于价值链视角，构建涵盖人才资源在区域空间层面集聚生成、地区配置、效能产出3个维度在内的区域人才集聚水平评价指标体系，采用专家咨询AHP-信息熵组合赋权法构建综合评价模型和耦合协调度模型，对2010—2018年长江经济带沿线省市人才集聚水平进行测度与时空演化特征分析。结果表明，就长江经济带整体而言，考察期内地区人才集聚水平总体呈现逐年上升态势，但仍处于低水平状态；沿线省市人才集聚水平差异显著，缩小趋势渐缓，整体呈现局部高水平、全局低效率的不利境况；各省市人才集聚水平关键维度的耦合协调度整体呈显著波动递增态势，但内部耦合协调度存在不平衡发展问题，影响地区人才集聚水平整体提升。为提升长江经济带人才集聚水平，推进沿线地区协调发展，需强化区域人才合作机制，采取差别化精准策略破解沿线地区人才集聚低水平、不协调的短板因素。     

Exploring the representativeness of the M5 competition data

The main objective of the M5 competition, which focused on forecasting the hierarchical unit sales of Walmart, was to evaluate the accuracy and uncertainty of forecasting methods in the field to identify best practices and highlight their practical implications. However, can the findings of the M5 competition be generalized and exploited by retail firms to better support their decisions and operation? This depends on the extent to which M5 data is sufficiently similar to unit sales data of retailers operating in different regions selling different product types and considering different marketing strategies. To answer this question, we analyze the characteristics of the M5 time series and compare them with those of two grocery retailers, namely Corporación Favorita and a major Greek supermarket chain, using feature spaces. Our results suggest only minor discrepancies between the examined data sets, supporting the representativeness of the M5 data.     

Spatio-temporal probabilistic forecasting of wind power for multiple farms: A copula-based hybrid model

Accurate probabilistic forecasting of wind power output is critical to maximizing network integration of this clean energy source. There is a large literature on temporal modeling of wind power forecasting, but considerably less work combining spatial dependence into the forecasting framework. Through the careful consideration of the temporal modeling component, complemented by support vector regression of the temporal model residuals, this work demonstrates that a DVINE copula model most accurately represents the residual spatial dependence. Additionally, this work proposes a complete set of validation mechanisms for multi-h-step forecasts that, when considered together, comprehensively evaluate accuracy. The model and validation mechanisms are demonstrated in two case studies, totaling ten wind farms in the Texas electric grid. The proposed method outperforms baseline and competitive models, with an average Continuous Ranked Probability Score of less than 0.15 for individual farms, and an average Energy Score of less than 0.35 for multiple farms, over the 24-hour-ahead horizon. Results show the model’s ability to replicate the power output dynamics through calibrated and sharp predictive densities.     

The cost of capital and optimal financing policy in a dynamic setting

This paper revisits the Modigliani–Miller propositions on the optimal financing policy and cost of capital in a dynamic setting. In an environment without taxes and bankruptcy costs, the results are generally consistent with the Modigliani–Miller Propositions 1 and 2. However, the first proposition should be presented and interpreted more carefully, as given firm characteristics, there is only one optimal capital structure. Thus, a firm’s capital structure is relevant. A relaxation of assumptions about either taxes or bankruptcy costs leads to conclusions that are generally different from those in Modigliani and Miller (1958). The model predicts that leverage and sales-to-capital ratios decrease but firm size and capital stock increase with the subjective discount factor of the firm’s manager if there are taxes and bankruptcy costs. The empirical analysis supports these predictions.     

Destabilizing role of futures markets on North American hard red spring wheat spot prices

The objective of the paper is to determine if the futures prices of hard red spring wheat (HRSW) have stabilizing or destabilizing impact on spot HRSW price in North America. Several important results emerge from thorough empirical analysis. First, both Granger causality tests and directed acyclic graph algorithms (DAGs) point to two-way causality between futures and spot HRSW prices and thus endogeneity in both prices formation. To the contrary, both procedures suggest that ending stocks are exogenous to spot and futures HRSW prices. Both vector error correction model and impulse response functions point to a large and long-lasting impact of a shock to futures price on spot price level. Finally, variance decomposition analysis indicates that futures prices are responsible for the bulk of spot price volatility in both short and long run. Our result is consistent with those of theoretical models suggesting that when production (supply side) is the dominant disturbance, spot price is destabilized in both the short and the long run by futures prices. An important implication of this research is the need for alternative market mechanisms or alternative farm policy measures that would mitigate price risk and ensure sustainable farming of American HRSW farmers.     

A three-frequency dynamic factor model for nowcasting Canadian provincial GDP growth

This paper estimates a three-frequency dynamic factor model for nowcasting the Canadian provincial gross domestic product (GDP). The Canadian provincial GDP at market prices is released by Statistics Canada annually with a significant lag (11 months). This necessitates a mixed-frequency approach that can process timely monthly data, the quarterly national accounts, and the annual target variable. The model is estimated on a wide set of provincial, national and international data. In a pseudo real-time exercise, we find that the model outperforms simple benchmarks and is competitive with more sophisticated mixed-frequency approaches (MIDAS models). We also find that variables from the Labour Force Survey are important predictors of real activity. This paper expands previous work that has documented the importance of foreign variables for nowcasting Canadian GDP. This paper finds that including national and foreign predictors is useful for Ontario, while worsening the nowcast performance for smaller provinces.     

Feelings of ownership and volunteering: Examining psychological ownership as a volunteering motivation for nonprofit service organisations

Given many nonprofit service organisations rely on volunteers to provide valued services to their communities, examining the factors that impact the retention and recruitment of volunteers is of central importance for the success and longevity of many community-based services. Psychological ownership is a phenomenon whereby objects that are not physically or legally possessed can be the target of ownership feelings. While such psychological ownership has been examined in the paid-employment literature as an antecedent for prosocial behaviours, there has been no examination of psychological ownership within the volunteering literature. As such, this paper examines the role of psychological ownership as a factor in volunteer retention for predominantly community-based nonprofit service organisations by examining how the sense of ownership over the nonprofit service provider affects volunteering attitudes and intentions. Alongside ownership, previous volunteering behaviours, and time pressures are also examined as contributing factors. Results indicate that volunteering does increase ownership perceptions, and those ownership perceptions have positive outcomes for volunteering behaviours. However, time pressure is a significant moderator of these relationships and different volunteering behaviours can be observed for high and low time-pressured volunteers.     

Predicting housing prices in China based on modified Holt's exponential smoothing incorporating whale optimization algorithm

The forecast of the real estate market is an important part of studying the Chinese economic market. Most existing methods have strict requirements on input variables and are complex in parameter estimation. To obtain better prediction results, a modified Holt's exponential smoothing (MHES) method was proposed to predict the housing price by using historical data. Unlike the traditional exponential smoothing models, MHES sets different weights on historical data and the smoothing parameters depend on the sample size. Meanwhile, the proposed MHES incorporates the whale optimization algorithm (WOA) to obtain the optimal parameters. Housing price data from Kunming, Changchun, Xuzhou and Handan were used to test the performance of the model. The housing prices results of four cities indicate that the proposed method has a smaller prediction error and shorter computation time than that of other traditional models. Therefore, WOA-MHES can be applied efficiently to housing price forecasting and can be a reliable tool for market investors and policy makers.     

Modelling the iron ore price index: A new perspective from a hybrid data reconstructed EEMD-GORU model

As iron ore is the fundamental steel production resource, predicting its price is strategically important for risk management at related enterprises and projects. Based on a signal decomposition technology and an artificial neural network, this paper proposes a hybrid EEMD-GORU model and a novel data reconstruction method to explore the price risk and fluctuation correlations between China’s iron ore futures and spot markets, and to forecast the price index series of China’s and international iron ore spot markets from the futures market. The analysis found that the iron ore futures market in China better reflected the price fluctuations and risk factors in the imported and international iron ore spot markets. However, the forward price in China’s iron ore futures market was unable to adequately reflect the changes in the domestic iron ore market, and was therefore unable to fully disseminate domestic iron ore market information. The proposed model was found to provide better market risk perceptions and predictions through its combinations of the different volatility information in futures and spot markets. The results are valuable references for the early-warning and management of the related enterprise project risks.