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.     

基于模糊时间序列的预测模型——以上证指数为例

模糊理论使用语义变量本身所蕴含的特性,能减少处理问题时的不确定性所带来的困扰,被广泛的应用于各种领域的研究。首先回顾了基于模糊理论的模糊时间序列定义,对现有的模糊时间序列模型进行分析;在此基础上提出了一种新的模糊时间序列预测方法,以上证指数为对象进行了拟合。从结果看,新的基于模糊时间序列预测方法在MSN、平均误差(%)和标准误差(%)等指标上要优于现有的的预测方法。     

The price of rapid exit in venture capital-backed IPOs

This paper proposes an explanation for two empirical puzzles surrounding initial public offerings (IPOs). Firstly, it is well documented that IPO underpricing increases during “hot issue” periods. Secondly, venture capital (VC) backed IPOs are less underpriced than non-venture capital backed IPOs during normal periods of activity, but the reverse is true during hot issue periods: VC backed IPOs are more underpriced than non-VC backed ones. This paper shows that when IPOs are driven by the initial investor’s desire to exit from an existing investment in order to finance a new venture, both the value of the new venture and the value of the existing firm to be sold in the IPO drive the investor’s choice of price and fraction of shares sold in the IPO. When this is the case, the availability of attractive new ventures increases equilibrium underpricing, which is what we observe during hot issue periods. Moreover, I show that underpricing is affected by the severity of the moral hazard problem between an investor and the firm’s manager. In the presence of a moral hazard problem the degree of equilibrium underpricing is more sensitive to changes in the value of the new venture. This can explain why venture capitalists, who often finance firms with more severe moral hazard problems, underprice IPOs less in normal periods, but underprice more strongly during hot issue periods. Further empirical implications relating the fraction of shares sold and the degree of underpricing are presented.      

我国货币政策对房价作用时滞的实证分析

本文应用向量自回归模型的脉冲响应函数和预测方差分解的方法．对我国货币政策影响房价的外部时滞进行分析。利用2004年1月至2008年6月的月度数据测算出的结果是：信贷规模、狭义货币供应量和利率对房价的作用时滞分别为7个月、3个月和11个月。信贷规模和利率水平度对房价的冲击效应较小。而货币供应量对房价的冲击效应较为明显。央行在制定货币政策时应综合考虑货币工具的特点、作用时滞、经济形势和理性预期的影响。     

Profiling high-frequency equity price movements in directional changes

Market prices are traditionally sampled in fixed time intervals to form time series. Directional change (DC) is an alternative approach to record price movements. Instead of sampling at fixed intervals, DC is data driven: price changes dictate when a price is recorded. DC provides us with a complementary way to extract information from data. It allows us to observe features that may not be recognized in time series. The argument is that time series and DC-based analysis complement each other. With data sampled at irregular time intervals in DC, however, some of the time series indicators cannot be used in DC-based analysis. For example, returns must be time adjusted and volatility must be amended accordingly. A major objective of this paper is to introduce indicators for profiling markets under DC. We analyse empirical high-frequency data on major equities traded on the UK stock market, and through DC profiling extract information complementary to features observed through time series profiling.     

Time series momentum and moving average trading rules

We compare and contrast time series momentum (TSMOM) and moving average (MA) trading rules so as to better understand the sources of their profitability. These rules are closely related; however, there are important differences. TSMOM signals occur at points that coincide with a MA direction change, whereas MA buy (sell) signals only require price to move above (below) a MA. Our empirical results show MA rules frequently give earlier signals leading to meaningful return gains. Both rules perform best outside of large stock series which may explain the puzzle of their popularity with investors, yet lack of supportive evidence in academic studies.     

养老保险体制改革的动态时间一致性研究

本文构建了一个动态时间一致性模型,并利用中国数据对养老保险体系的动态时间一致性做了经验检验和动态预测。研究发现,按照帕累托效率标准对养老保险体系的参数或结构进行调整只是手段,而提高动态时间一致性才是目的。一项养老改革只有在能够同时增进社会福利和个人投资收益率时,才符合动态时间一致性原则;经验检验表明,1978～2012年间,现收现付制向基金制转轨是不符合动态时间一致性原则的;而1994年之后,将统筹比率控制在06～075之间,则有利于提高养老保险体系的动态时间一致性。预测结果表明,中国政府在2014～2037年和2038～2044年间应将统筹比率分别控制在06～09和025～06之间。     

有偏技术进步视角下中国工业产能过剩的影响因素分析

本文通过将产能利用率在生产侧与消费侧进行分解,测算了中国工业行业产能过剩指数,并考察了技术进步偏向、投资比重等因素对产能过剩的影响效果。研究发现：受生产侧与消费侧产能利用率提升的影响,中国工业总体产能过剩现象呈现下降趋势;生产技术偏向于资本,导致生产过程中偏爱资本投入,加速了投资对产能过剩的恶化程度,这在重工业行业中表现尤为明显。因此,纠正生产过程的要素使用偏好,有利于缓解当前的产能过剩现象。