贫困、经济增长与脱贫时间:基于中国城镇居民调查数据的分析

贫困指数是衡量社会总体贫困程度的指标,在众多的贫困指数中.本文选择具有良好性能的瓦特指数和由此派生出来的脱贫时间指数,分析了我国城镇上个世纪90年代以来的贫困变化趋势.研究发现,城镇贫困在1997年达到高峰,然后逐渐缓和;贫困人口的收入增长在初期能使脱贫时间迅速下降,但边际效果是递减的;贫困人口之间的收入分配不平等延长了脱贫时间:如果贫困人口的初始收入增加,会明显降低脱贫时间.     

农村信贷配给实证分析

信贷配给是制约我国农村信贷市场建设的重要因素,也阻碍了农村金融环境优化、影响了农村经济的良性发展。通过引入制度因子,量化信贷配给,构建了信贷配给宏观计量模型,对我国农村信贷配给状况进行实证检验,结果证明,利率和制度因子是造成我国农村信贷配给的两大因子。应合理调控利率、推进深化改革、加大三农扶持力度,实现农村经济社会的和谐发展。     

Modeling and optimization of a container drayage problem with resource constraints

This paper investigates the problem faced by firms that transport containers by truck in an environment with resource constraints. The considered area is export-dominant. As a result, there are three types of container movements as inbound full, outbound full, and inbound empty movements. Both the time windows at the terminal and at the customers’ places and the operation times are considered. Empty containers are also regarded as separate transportation resources besides trucks. The total operating time including waiting time of all the trucks in operation is minimized. The problem is first formulated as a directed graph and then mathematically modeled based on the graph. It falls into a multiple traveling salesman problem with time windows (m-TSPTW) with resource constraints. An algorithm based on reactive tabu search (RTS) is developed to solve the problem. A number of randomly generated examples indicate that the algorithm can be applied to the real world.     

时序分析在地下洞室变形监测数据处理中的应用

时序分析法是广泛应用于多领域的统计分析方法,在岩土工程数值分析当中也有广泛应用,SAS/ETS(Ti me Series Forecasting)时序分析系统是一种智能化时序分析工具,本文运用SAS数据挖掘工具中时序分析组件对某水电站地下引水发电系统原位监控位移时间序列进行时序预测分析。实践表明,SAS/ETS时序分析方法基本能反映出蕴涵在位移序列中的岩体变形规律,预测结果与实测结果一致,可以运用于工程实践。     

Forecasting inflation in Latin America with core measures

We explore the ability of core inflation to predict headline CPI annual inflation for a sample of eight developing economies in Latin America over the period January 1995–May 2017. Our in-sample and out-of-sample results are roughly consistent in providing robust evidence of predictability in four of the countries in our sample. Mixed evidence is found for the other four countries. The bulk of the out-of-sample evidence of predictability concentrates on the short horizons of one and six months. In contrast, at the longest horizon of 24 months, we only find out-of-sample evidence of predictability for two countries: Chile and Colombia, with robust results only for the latter. This is both important and challenging, given that the monetary authorities in our sample of developing countries are currently implementing or are taking steps toward the future implementation of inflation targeting regimes, which are based heavily on long-run inflation forecasts.     

Investment and financing choices by time-inconsistent managers

This paper provides an analytically tractable continuous-time model in which a time-inconsistent manager can divert part of the firm’s cash flows as private benefits at the expense of outside shareholders. We endogenously determine the investment scale, investment threshold, optimal coupon and default threshold under managerial discretion. We demonstrate that time-inconsistent managers each have a trade-off between the timing and scale of investment.Among a number of important implications, by exploring agency costs of equity as deviations from the investment and financing policies that maximize equity value, our analysis reveals that a certain degree of time inconsistency in managerial preferences decreases the agency costs of equity. We also find that a naive manager more severely distorts the investment and financing policies than does a sophisticated manager, which leads to higher agency costs of equity. Finally, we document that the impacts of corporate governance variables, such as the managers’ property parameter and/or the level of managers’ ownership, depend on the managers’ beliefs regarding their future time-inconsistent behavior; this prediction provides novel empirical tests.     

Too few university graduates. Inclusiveness and effectiveness of the Italian higher education system

Despite the absence of formal entry barriers, Italy is lagging behind the majority of the developed countries in the share of young individuals with tertiary education. Exploiting the administrative data of a large public Italian institution, we analyze student academic careers across recent matriculation cohorts. We propose a flexible discrete-time competing risks estimation that allows overcoming some major limitations of conventional competing risks models. We find that student pathways vary tremendously across prior schooling profiles: for example, the within 4-year dropout probability ranges between 10% and 77%. We observe improvements over time in retention and time-to-degree, and by decomposing changes into components related to the composition of the enrolled population, the choice of the field of study and ‘individual behavior’ after enrolment, we find that the latter plays a major role. However, the improvement is limited in size and does not interest students from the vocational track. Since this progress is not accompanied by an increase in the share of students making the transition from high school to university, altogether our results call for great concern over the inclusiveness and effectiveness of the Italian university system.     

Uncertainty and herding behavior: evidence from cryptocurrencies

The aim of this study is to examine the existence of herding behavior in the cryptocurrency market under uncertainty by employing cross-sectional absolute deviation (CSAD) of returns, ordinary least squares (OLS), generalized autoregressive conditional heteroscedasticity (GARCH) methods and Time-Varying Markov-Switching (TV-MS) model for both overall sample and sub-periods which was determined based on the results of Quandt-Andrews and Bai-Perron breakpoint tests. We utilized the daily data of the 14 leading cryptocurrencies in terms of closing price, market cap and transaction volume. We also used dummy variables to analyze whether or not an asymmetric behavior occurred during the "up and down" market periods. Our results for the overall sample refer to an anti-herding behavior in each model. However, the results of the TV-MS model for the 3rd sub-period (2/28/2017−1/16/2018) imply the existence of a herding behavior in the low volatility regime, an anti-herding behavior occurred during the high volatility regime and the effect of uncertainty was significant on the anti-herding behavior. Finally, our results suggest that there was no significant asymmetric behavior during the "up and down" market periods.     

Does a reward program affect customers’ behavioural intention of visiting the airport? A case study of Singapore Changi Airport

An understanding of the behavioural intentions of customers in visiting airports may help airport management to boost airport retail revenue (i.e. non-aeronautical revenue) by increasing customer spending at the airports. This study aims to examine the connection between airport reward programs and customers' behavioural intentions of visiting the airport, attempting to bring some benefits and credits for implementing reward programs in the airport sector by using Singapore Changi Airport as a case study. Two types of customer behavioural intentions (i.e. time spent at the airport and various purposes of visiting the airport) were investigated in the study to examine the effects of Changi Airport's reward program (i.e. Changi Rewards). An on-line survey was conducted, and the participants' responses were analysed via a structural equation modelling approach. The results showed that the attractive benefits of Changi Rewards could have a positive influence on arousing customers' intentions to engage with the programme. In addition, customers' intentions to engage with the programme can have a positive effect on increasing passengers' time spent at the airport and inducing customers to visit the airport for leisure purposes. Managerial implications to the airport sector are discussed.     

Are forecasting competitions data representative of the reality?

Forecasters typically evaluate the performances of new forecasting methods by exploiting data from past forecasting competitions. Over the years, numerous studies have based their conclusions on such datasets, with mis-performing methods being unlikely to receive any further attention. However, it has been reported that these datasets might not be indicative, as they display many limitations. Since forecasting research is driven somewhat by data from forecasting competitions, it becomes vital to determine whether they are indeed representative of the reality or whether forecasters tend to over-fit their methods on a random sample of series. This paper uses the data from M4 as proportionate to the real world and compares its properties with those of past datasets commonly used in the literature as benchmarks in order to provide evidence on that question. The results show that many popular benchmarks of the past may indeed deviate from reality, and ways forward are discussed in response.