Econometric modeling of self-exciting process

This thesis focuses on modeling state dependence, a phenomenon where past experiences do alter the path of future events. The general idea behind this concept is that diferences among individuals are not merely explained by their characteristics, but also by their past experiences. Typical examples of state dependence in economics include the incidence of accidents, labor force participation and unemployment, consumer's purchase behaviors, learning etc. I use the self-exciting process to incorporate the state dependent structure in economic models. The self-exciting process is a counting process whose filtration includes a field that is generated by the process itself. Chapter I introduces notation and provides an introduction to the self-exciting process. A minimum distance estimation (MDE) method is also introduced. Monte Carlo exercises are performed to investigate the MDE performance. I also provide a comparison among the self-exciting process approach and existing methods such as counting data regression and duration models. Chapter II studies how past doctor visiting records could alter the preference individuals' future medical consumption choices, especially through the channel of a cost-sharing health insurance plan. This study contributes to the existing health insurance literature by providing additional evidence that supports the shadow price theory. While most previous studies that endorse the shadow price use specific company or social security designs, I investigate the shadow price using a genuine health insurance contract where medical cost has multiple sources. Chapter III contributes to the study of work absence. Early works in this literature usually focus on absence duration and assume independence among the duration. I took another approach in this study where both absence duration and working duration (the length of a working spell until an absence occurred) are analyzed. Special attention is given to studying how the state dependent absence score could affect these duration. I study a particular firm who has installed a experience-rated work absence regulation. I also distinguish between short and long term absences and find that individuals have diferent state dependent reaction to different types of absences. Chapter IV investigates the classical unemployment duration problem. I restrict the attention to Spanish Youth, who are well known for their high job turnover rate. A crucial element in this literature is the separation of the state dependent and the unobserved heterogeneity. I did so by assuming a multiplicative duration structure and perform a first ratio transformation on the individual's unemployment duration to swipe out the unobserved heterogeneity. The new estimator could be regarded as an extension to the existing dynamic panel data model but it avoids using instrumental variables and could allow unit root autoregressive coefficient and non-stationarity process.Programa de Doctorado en Economía por la Universidad Carlos III de MadridPresidente: Taisuke Otsu; Secretario: Juan Carlos Escanciano Reyero; Vocal: Juan Manuel Rodríguez Po

Building recognition on subregion’s multi-scale gist feature extraction and corresponding columns information based dimensionality reduction

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WHAT AFFECTS THE ADVERTISING SHARING BEHAVIOR AMONG MOBILE SNS USERS? THE RELATIONSHIPS BETWEEN SOCIAL CAPITAL, OUTCOME EXPECTATIONS AND PREVENTION PRIDE

Mobile networks practice of social networking service that gives individuals an easy way to exchange messages and ideas with others base on interpersonal relationships. However, why individuals spread advertisements in their social circles through mobile applications is not well understood: is this the result of environment impact or the result of individual characteristics? To tackle this problem, we apply social capital theory to examine how social capital influence advertising recommendation quality and advertising sharing behavior in mobile networks. And, we also use social cognitive theory and regulatory focus theory to investigate the motivations behind people\u27s advertising sharing behavioral in mobile networks. Data collected from 319 mobile social networking users provide support for the proposed model. The analysis of the sample shows that the social capital and outcome expectations are significant indicators of individual’s ad-sharing behavior in the mobile SNS environment. Moreover, the prevention pride has an obvious interaction influence on the perception and behavior of M-ad sharing. Implications for research and practice are discussed

Knowledge Sharing in Personal Networking Instead of Professional-instrumental Context: An Integrated Perspective of Psychological Defense

The approach and structure of online networking have different implications for the knowledge sharing behavior of workers across teams within an organization. Despite studies on the topic, it is still not clear how the characteristic of social ties influences knowledge sharing behavior via online platforms, which have increasingly highlighted two opposing attributes: instrumental/task-related networks and expressive/personal networks. This study investigates the role of psychological defense in shaping the knowledge sharing behavior of employees in personal networking tools. Empirical analysis based on data collected from 455 knowledge workers demonstrated that psychological defense has a fundamental impact on knowledge sharing in personal networking context. Specifically, our results show that psychological safety, need to belong, self-integrity, sense of control, work overload, and role conflict have significant impact on the sharing behavior of knowledge workers in the personal networking context. The theory and practice contributions provided by the current study were discussed

Message Passing in C-RAN: Joint User Activity and Signal Detection

In cloud radio access network (C-RAN), remote radio heads (RRHs) and users are uniformly distributed in a large area such that the channel matrix can be considered as sparse. Based on this phenomenon, RRHs only need to detect the relatively strong signals from nearby users and ignore the weak signals from far users, which is helpful to develop low-complexity detection algorithms without causing much performance loss. However, before detection, RRHs require to obtain the realtime user activity information by the dynamic grant procedure, which causes the enormous latency. To address this issue, in this paper, we consider a grant-free C-RAN system and propose a low-complexity Bernoulli-Gaussian message passing (BGMP) algorithm based on the sparsified channel, which jointly detects the user activity and signal. Since active users are assumed to transmit Gaussian signals at any time, the user activity can be regarded as a Bernoulli variable and the signals from all users obey a Bernoulli-Gaussian distribution. In the BGMP, the detection functions for signals are designed with respect to the Bernoulli-Gaussian variable. Numerical results demonstrate the robustness and effectivity of the BGMP. That is, for different sparsified channels, the BGMP can approach the mean-square error (MSE) of the genie-aided sparse minimum mean-square error (GA-SMMSE) which exactly knows the user activity information. Meanwhile, the fast convergence and strong recovery capability for user activity of the BGMP are also verified.Comment: Conference, 6 pages, 7 figures, accepted by IEEE Globecom 201