社会网络调节下文化距离对双边贸易的影响——基于中国与“一带一路”沿线国家贸易的研究

随着“一带一路”倡议的推进和中国与沿线国家贸易联系的加强,除地理距离和制度距离等因素外,文化距离成为影响双边贸易关系的重要变量。孔子学院和华人网络构成的社会网络可在一定程度上调节两国的文化距离,减轻文化距离对双边贸易的抑制作用。构建以文化距离为核心解释变量,社会网络为调节变量的实证模型,基于2008—2018年中国与“一带一路”沿线52个国家的贸易数据,探讨社会网络调节下文化距离对双边贸易的影响,并在此基础上对我国与“一带一路”沿线国家贸易政策调整提出建议。     

旅行真人秀受众涉入对出境旅游意向影响研究——一个有中介的调节模型

近年来,旅行真人秀的热播引发了出境旅游热。为厘清这一作用机制,从受众涉入视角,运用结构方程模型,以目的地形象感知为中介变量、文化距离为调节变量,探究受众涉入对出境旅游意向的影响机制。结果表明：(1)受众涉入与出境旅游意向呈正相关;(2)受众涉入对目的地形象感知具有正向显著影响,目的地形象感知对出境旅游意向具有正向显著影响,目的地形象感知在受众涉入对出境旅游意向的影响中起中介作用;(3)文化距离负向调节了受众涉入对出境旅游意向的影响,且这一调节效应以目的地形象感知为中介变量。基于上述结果,目的地营销组织要精心设计节目内容,提高受众涉入度,强化目的地形象塑造与推广,突出文化相似性,激发受众到访目的地的意向。     

Basic structure of the asymptotic theory in dynamic nonlinear econometric models

This is the second of two papers that provide an expository discussion of the basic structure of the asymptotic theory of M-estimators in dynamic nonlinear models and a review of the literature. The first paper, Pötscher and Prucha(1991), deals with consistency. In the present paper we discuss asymptotic normality. As an important ingredient to the asymptotic normality proof in dynamic nonlinear models we consider central limit theorems for dependent random variables. We also discuss the estimation of the variance covariance matrix of m-estimators under heteroscedasticity and autocorrelation.     

A Comparison of Different Methods for Representing Categorical Data

We first compare correspondence analysis, which uses chi-square distance, and an alternative approach using Hellinger distance, for representing categorical data in a contingency table. We propose a coefficient which globally measures the similarity between these two approaches. This coefficient can be decomposed into several components, one component for each principal dimension, indicating the contribution of the dimensions to the difference between the two representations. We also make comparisons with the logratio approach based on compositional data. These three methods of representation can produce quite similar results. Two illustrative examples are given.     

Identification of multiple high leverage points in logistic regression

Leverage values are being used in regression diagnostics as measures of unusual observations in the X-space. Detection of high leverage observations or points is crucial due to their responsibility for masking outliers. In linear regression, high leverage points (HLP) are those that stand far apart from the center (mean) of the data and hence the most extreme points in the covariate space get the highest leverage. But Hosemer and Lemeshow [Applied logistic regression, Wiley, New York, 1980] pointed out that in logistic regression, the leverage measure contains a component which can make the leverage values of genuine HLP misleadingly very small and that creates problem in the correct identification of the cases. Attempts have been made to identify the HLP based on the median distances from the mean, but since they are designed for the identification of a single high leverage point they may not be very effective in the presence of multiple HLP due to their masking (false–negative) and swamping (false–positive) effects. In this paper we propose a new method for the identification of multiple HLP in logistic regression where the suspect cases are identified by a robust group deletion technique and they are confirmed using diagnostic techniques. The usefulness of the proposed method is then investigated through several well-known examples and a Monte Carlo simulation.     

Testing Exponentiality Based on Type II Censored Data and a New cdf Estimator

In this article, we use a new cdf estimator to obtain a nanparametric entropy estimate and use it for testing exponentiality and normality. We also use the new cdf estimator to estimate the joint entropy of the Type II censored data which we use for some goodness-of-fit tests based on Kullback–Leibler information and show, by simulation, that it compares favorably with the leading competitor.     

Performance Evaluation Based on the Robust Mahalanobis Distance and Multilevel Modeling Using Two New Strategies

In this article, we propose a general framework for performance evaluation of organizations and individuals over time using routinely collected performance variables or indicators. Such variables or indicators are often correlated over time, with missing observations, and often come from heavy-tailed distributions shaped by outliers. Two new double robust and model-free strategies are used for evaluation (ranking) of sampling units. Strategy 1 can handle missing data using residual maximum likelihood (RML) at stage two, while strategy two handles missing data at stage one. Strategy 2 has the advantage that overcomes the problem of multicollinearity. Strategy one requires independent indicators for the construction of the distances, where strategy two does not. Two different domain examples are used to illustrate the application of the two strategies. Example one considers performance monitoring of gynecologists and example two considers the performance of industrial firms.     

k-Means Algorithm in Statistical Shape Analysis

In this work it is shown how the k-means method for clustering objects can be applied in the context of statistical shape analysis. Because the choice of the suitable distance measure is a key issue for shape analysis, the Hartigan and Wong k-means algorithm is adapted for this situation. Simulations on controlled artificial data sets demonstrate that distances on the pre-shape spaces are more appropriate than the Euclidean distance on the tangent space. Finally, results are presented of an application to a real problem of oceanography, which in fact motivated the current work.     

Usefulness of Asymptotic Distributions in the Classical Occupancy Problem

Approximations to the distribution of a discrete random variable originating from the classical occupancy problem are explored. The random variable X of interest is defined to be how many of N elements selected by or assigned to K individuals when each of the N elements is equally likely to be chosen by or assigned to any of the K individuals. Assuming that N represents the number of cells and each of the K individuals is placed in exactly one of the cells, X can also be defined as the number of cells occupied by the Kindividuals. In the literature, various asymptotic results for the distributions of X and (N ? X) are given; however, no guidelines are specified with respect to their utilization. In this article, these approximations are explored for various values of K and N, and rules of thumb are given for their appropriate use.