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This paper considers the problem of estimating a partially linear varying coefficient fixed effects panel data model. Using the series method, we establish the root N normality for the estimator of the parametric component; and we show that the unknown function can be consistently estimated at the standard nonparametric rate. Furthermore, we extend the model to allow endogeneity in the parametric component and establish the asymptotic properties of the semiparametric instrumental variable estimators.

Modelling and forecasting interval-valued time series (ITS) have received increasing attention in statistics and econometrics. An interval-valued observation contains more information than a point-valued observation in the same time period. The previous literature has mainly considered modelling and forecasting a univariate ITS. However, few works attempt to model a vector process of ITS. In this paper, we propose an interval-valued vector autoregressive moving average (IVARMA) model to capture the cross-dependence dynamics within an ITS vector system. A minimum-distance estimation method is developed to estimate the parameters of an IVARMA model, and consistency, asymptotic normality and asymptotic efficiency of the proposed estimator are established. A two-stage minimum-distance estimator is shown to be asymptotically most efficient among the class of minimum-distance estimators. Simulation studies show that the two-stage estimator indeed outperforms other minimum-distance estimators for various data-generating processes considered.

Most offender narrative being studied has been in oral forms, produced in the reciprocal process of researcher-(ex) offender interviews. This chapter offers an introduction to a variation of offender narrative study within the prison and rehabilitation context: the narrative of written autobiography. Since the early 1940s, Chinese reform institutions have required written autobiographies from new admitters, provided with clear presubscripted guidelines of instructions as well as postcensorship. For this chapter, we trace back and analyse this model based on 28 prisoners' autobiographies in mainland China between 2007 and 2009, as well as archive documents in different historical periods. We have found that the mandatory offender autobiographies are highly functional writings with clear requirements that embody the existing power structure. We have also found considerable commonality with findings in Western contexts on the presence and problems of narrative compliance in rehabilitation. We argue that narrative criminology should further engage in understanding the practice of narrative censorship and co-authorship in criminal justice processes, as it takes on different forms in different historical–social contexts.

Data has become one of the most significant instruments in e-commerce innovation. Benefits to the entire society can be summarized as following: from the government’s perspective - to assess the impact of e-commerce to the economy; for merchants - to understand consumers’ needs; and for consumers - to be offered with the right product he/she is looking for. The digital revolution in the past five years has shown the need to offer more differentiated services than the physical stores, when consumers are not able to try and touch products. It is for this reason that e-commerce has continuously developed and transformed Research Online, Purchase Offline into a true experience. Considering the future of e-commerce is to enhance economic development and growth, this research will discuss the disruption of Research and Development through big data. The core objective of this research is to propose a predictive model to deeply understand consumer behavior by analyzing new regulations and transaction records.

This paper gives a selective review on some recent developments of nonparametric methods in both continuous and discrete time finance, particularly in the areas of nonparametric estimation and testing of diffusion processes, nonparametric testing of parametric diffusion models, nonparametric pricing of derivatives, nonparametric estimation and hypothesis testing for nonlinear pricing kernel, and nonparametric predictability of asset returns. For each financial context, the paper discusses the suitable statistical concepts, models, and modeling procedures, as well as some of their applications to financial data. Their relative strengths and weaknesses are discussed. Much theoretical and empirical research is needed in this area, and more importantly, the paper points to several aspects that deserve further investigation.