Estimation procedures for a family of density functions representing various life-testing models

A family of density functions is considered which contains several life-testing models as specific cases. Uniformly minimum variance unbiased estimators are obtained for the positive and negative powers of the parameter, moments and reliability function. These general results provide the estimators for the specific models.     

Modeling time-varying higher-order conditional moments: A survey

Since the introduction of the Autoregressive Conditional Heteroscedasticity (ARCH) model, the literature on modeling the time-varying second-order conditional moment has become increasingly popular in the last four decades. Its popularity is partly due to its success in capturing volatility in financial time series, which is useful for modeling and predicting risk for financial assets. A natural extension of this is to model time variation in higher-order conditional moments, such as the third and fourth moments, which are related to skewness and kurtosis (tail risk). This leads to an emerging literature on time-varying higher-order conditional moments in the last two decades. This paper outlines recent developments in modeling time-varying higher-order conditional moments in the economics and finance literature. Using the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) framework as a foundation, this paper provides an overview of the two most common approaches for modeling time-varying higher-order conditional moments: autoregressive conditional density (ARCD) and autoregressive conditional moment (ARCM). The discussion covers both the theoretical and empirical aspects of the literature. This includes the identification of the associated skewness–kurtosis domain by using the solutions to the classical moment problems, the structural and statistical properties of the models used to model the higher-order conditional moments and the computational challenges in estimating these models. We also advocate the use of a maximum entropy density (MED) as an alternative method, which circumvents some of the issues prevalent in these common approaches.     

Bayesian Comparison of ARIMA and Stationary ARMA Models

Time series analysts have long been concerned with distinguishing stationary generating processes from processes for which differencing is required to induce stationarity. In practical applications, this issue is addressed almost invariably through formal hypothesis testing. In this paper, we explore some aspects of the Bayesian approach to the problem, leading to the calculation of posterior odds ratios. Interesting features arise in the simplest possible variant of the problem, where a choice has to be made between a random walk and a stationary first order autoregressive model. We discuss in detail the analysis of this case, and also indicate how our approach extends to the more general comparison of an ARIMA model with a stationary competitor.     

A Point Processes approach to bicycle sharing systems’ design and management

Public bicycle systems have attracted a great deal of attention in recent years. The success of this service heavily depends on the topology of the city and on the locations of bike stations in relation to potential demand. In fact, it is primarily important that users find bike stations in convenient locations, sufficiently close both to the origins of their trips and to their destinations, and that each rental station guarantees the availability both of enough bicycles and of enough empty docking slots.This paper proposes a point processes approach to the study of bike-sharing systems, allowing us to quantify and control parameters having a key role in decisions both of strategic and operational type.Differently from previous studies, the point processes approach catches both the interdependence among the stations and the links between spatial and time aspects of the problem.The application of point processes, in particular spatial mixed Poisson processes, to this field requires the statement and proof of an invariance property of such processes under stochastic dependent transformations, that may be of interest also from a theoretical point of view.     

Goodness of fit in the linear model without a constant term

Some properties of two goodness-of-fit measures are analysed in the case the linear model does not contain a constant term. One measure is proposed by Batten (1987), the other is the squared sample correlation coefficient. In particular, attention is paid to the bias and moments of the measures.     

Method-of-moments estimation and choice of instruments: Numerical computations

We numerically evaluate asymptotic variances and biases of various method-of-moments estimators in the Hansen–Singleton model calibrated to real data. Inspection of resulting figures leads to a conclusion that applied researchers do not always form instrument sets judiciously.     

关于服务过程质量管理的思考

本文剖析了导致服务过程质量问题的因素,提出了目前在服务过程质量管理中存在着两大难点:一个压力点--对“真实瞬间“的管理;一个盲点--对顾客行为的管理,并就实践中如何加强对这“两点“的管理作了深入的探讨.     

Is the internet penetration pro-poor? Evidence from a panel data analysis

The impacts of internet use on income and poverty status are contentious despite extensive research efforts. This paper re-examines these influences using five-wave panel data extracted from the Vietnam Access to Resources Household Survey (VARHS). A two-step system called Generalized Methods of Moments (GMM) and Propensity Score Matching (PSM) is the primary metric for estimating internet-income and internet-poverty correlations. The analysis revealed that internet use assisted rural households, including the poor ones, in fostering their livelihoods in the 2008–2016 period: (1) internet access increased the average annual household income per capita by 4.5 million Vietnamese dongs, accounting for at least 17% of the overall income; (2) it decreased the probability of being poor by around 4%. This study suggests that the universalisation of the internet, along with improvements in digital literacy and digital applications provided for rural dwellers, particularly the needy, should be essential for poverty reduction programmes in the context of developing countries.     

L‐moments skewness and kurtosis as measures of regional convergence and cohesion

The paper deals with the statistical modeling of convergence and cohesion over time with the use of kurtosis, skewness and L‐moments. Changes in the shape of the distribution related to the spatial allocation of socio‐economic phenomena are considered as an evidence of global shift, divergence or convergence. Cross‐sectional time‐series statistical modeling of variables of interest is to overpass the minors of econometric theoretical models of convergence and cohesion determinants. L‐moments perform much more stable and interpretable than classical measures. Empirical evidence of panel data proves that one pure pattern (global shift, polarization or cohesion) rarely exists and joint analysis is required.