Estimation of a Conditional Copula and Association Measures

Abstarct. This paper is concerned with studying the dependence structure between two random variables Y ₁ and Y ₂ conditionally upon a covariate X. The dependence structure is modelled via a copula function, which depends on the given value of the covariate in a general way. Gijbels et al. (Comput. Statist. Data Anal., 55, 2011, 1919) suggested two non‐parametric estimators of the ‘conditional’ copula and investigated their numerical performances. In this paper we establish the asymptotic properties of the proposed estimators as well as conditional association measures derived from them. Practical recommendations for their use are then discussed.     

The study of commission rate problem in real-time bidding

This paper studies the commission rate in real-time bidding based on the generalized first-price auction mechanism. Firstly, the equilibrium bidding strategies are provided when there are fixed commission rate and the linear commission rate, which proves that if the there is no reserve price, the expected payment of DSP is not related to the rate of commission. The commission rate affects the equilibrium bidding strategies only. Secondly, this paper shows that if there are the reserve price and the commission, the payment of DSP is increasing on the commission rate. That is, if there is reserve price, the DSP cannot control the increasing of the expected payment by adjusting the equilibrium bidding strategy, when the commission rate increases. Finally, provided that the reserve price and commission rate are exogenous respectively, this paper draws to the conclusion that the conditions of optimal commission rate and the optimal reserve price must be satisfied.     

Solutions to the Challenge of Meeting Rural Transportation Needs: Middle-Aged and Older Adults’ Perspectives

This study aimed to explore how the aging population in Appalachia manages its transportation and plans for the transition to non-driving and to seek possible solutions to the challenge of meeting rural transportation needs. Four focus groups (N = 38) were conducted, supplemented by a questionnaire, in Appalachian Kentucky. The results showed that few alternative transportation existed except a local paratransit service and informal transportation support. Compared to older adults, middle-aged adults reported a greater willingness to use mobile phones and the Internet to arrange transportation if they were available. Participants also recommended expanding the use of existing transportation in the community – such as church vans – to better meet the aging population’s needs. The findings suggest that partnership between government, non-profit, and private sectors is needed, not solely focusing on a tax-dependent solution. Additionally, information and communication technology-based transportation management system would help maximize the use of scare but existing resources in rural areas.     

Thirty Years of Mergers and Acquisitions Research: Recent Advances and Future Opportunities

The complex phenomenon that mergers and acquisitions (M&As) represent has attracted substantial interest from a variety of management disciplines over the past 30 years. Three primary streams of enquiry can be identified within the strategic and behavioural literature, which focus on the issues of strategic fit, organizational fit and the acquisition process itself. The recent achievements within each of these research streams are briefly reviewed. However, in parallel to these research advances, the failure rates of mergers and acquisitions have remained consistently high. Possible reasons for this dichotomy are discussed, which in turn highlight the significant opportunities that remain for future M&A research.     

Sieve Empirical Likelihood and Extensions of the Generalized Least Squares

The empirical likelihood cannot be used directly sometimes when an infinite dimensional parameter of interest is involved. To overcome this difficulty, the sieve empirical likelihoods are introduced in this paper. Based on the sieve empirical likelihoods, a unified procedure is developed for estimation of constrained parametric or non-parametric regression models with unspecified error distributions. It shows some interesting connections with certain extensions of the generalized least squares approach. A general asymptotic theory is provided. In the parametric regression setting it is shown that under certain regularity conditions the proposed estimators are asymptotically efficient even if the restriction functions are discontinuous. In the non-parametric regression setting the convergence rate of the maximum estimator based on the sieve empirical likelihood is given. In both settings, it is shown that the estimator is adaptive for the inhomogeneity of conditional error distributions with respect to predictor, especially for heteroscedasticity.     

Analytic computation of nonparametric Marsan–Lengliné estimates for Hawkes point processes

In 2008, Marsan and Lengliné presented a nonparametric way to estimate the triggering function of a Hawkes process. Their method requires an iterative and computationally intensive procedure which ultimately produces only approximate maximum likelihood estimates (MLEs) whose asymptotic properties are poorly understood. Here, we note a mathematical curiosity that allows one to compute, directly and extremely rapidly, exact MLEs of the nonparametric triggering function. The method here requires that the number q of intervals on which the nonparametric estimate is sought equals the number n of observed points. The resulting estimates have very high variance but may be smoothed to form more stable estimates. The performance and computational efficiency of the proposed method is verified in two disparate, highly challenging simulation scenarios: first to estimate the triggering functions, with simulation-based 95% confidence bands, for earthquakes and their aftershocks in Loma Prieta, California, and second, to characterise triggering in confirmed cases of plague in the United States over the last century. In both cases, the proposed estimator can be used to describe the rate of contagion of the processes in detail, and the computational efficiency of the estimator facilitates the construction of simulation-based confidence intervals.     

Dynamic models and cross-sectional data: Estimation of dynamic parameters in cross-sectional data

Serious difficulties in the interpretation of structural coefficients may arise when a static, cross-sectional model is applied to data generated by a dynamic process. One alternative is to collect data at several points in time, and specify the dynamic features that are proposed to exist. Another alternative is discussed here. Overidentifying restrictions in a static simultaneous equation system may, under certain conditions, be used to identify dynamic parameters (autoregression and autocorrelation coefficients) when data are available at only one point in time.