Chinatown 2.0: the difficult flowering of an ethnically themed shopping area

Right in Amsterdam’s picturesque Canal Zone, on and around Zeedijk, Chinese entrepreneurs have carved out a presence in what seems like the local Chinatown. The businessmen have been targeting Asian and non-Asian customers by offering products that – to an extent – can be associated with Asia, China in particular. Since the early 1990s, individual entrepreneurs and their business organisations have campaigned for official acknowledgement of Zeedijk as an ethnic-only district and for governmental support of the enhancement of Chineseness. Following Hackworth and Rekers. [(2005). “Ethnic Packaging and Gentrification. The Case of Four Neighborhoods in Toronto.” Urban Affairs Review 41 (2): 211–236], we argue that this case challenges traditional understandings of ethnic commercial landscapes. In sharp contrast to the current orthodoxy, which would conceive the proliferation of such an ‘ethnic enclave’ as part of a larger process of assimilation, we have approached Amsterdam’s Chinatown first and foremost as a themed economic space: Chinese and other entrepreneurs compete for a share of the market and in doing also for the right to claim the identity of the area. What is the historical development of the Zeedijk area, how did Chinese entrepreneurs and their associations try to boost Chinatown and negotiate public Chineseness, and how did governmental and non-governmental institutional actors respond to those attempts?     

“It Is What It Is”: Masculinity,Homosexuality, and Inclusive Discourse in Mixed Martial Arts

In this article we make use of inclusive masculinity theory to explore online media representations of male homosexuality and masculinity within the increasingly popular combat sport of mixed martial arts (MMA). Adopting a case-study approach, we discuss narratives constructed around one aspirational male MMA fighter, Dakota Cochrane, whose history of having participated in gay pornography became a major talking point on a number of MMA discussion/community Web sites during early 2012. While these narratives attempted to discursively rescue Cochrane’s supposedly threatened masculinity, highlighting both his “true” heterosexuality and his prodigious fighting abilities, they also simultaneously celebrated the acceptance of homosexual men within the sport that Cochrane’s case implied. Thus, we suggest that these media representations of homosexuality and masculinity within MMA are indicative of declining cultural homophobia and homohysteria and an inclusive vision of masculinity, as previously described by proponents of inclusive masculinity theory.     

金融自由化浪潮下我国金融业发展对策

20世纪70年代以来,国际金融自由化的进程明显加快,金融工具不断创新,金融信息化蓬勃发展.利率市场化、放松金融管制、开放国内资本市场、加强对全球金融的统一监管成为大势所趋.文章通过国际金融自由化进程中一些规律性经验的总结及我国金融改革历程的回顾,对我国金融市场在利率、外汇及证券等业务的开放提出了建议.     

十六届三中全会后我国所有制问题最新研究综述

党的十六届三中全会提出,“要大力发展混合所有制经济,使股份制成为公有制的主要实现形式”,引发经济学界对所有制问题展开了激烈的争论。学界对混合所有制经济提出许多不同的理解,对股份制企业的所有制性质作出许多不同的判断。鉴于此,本文对十六届三中全会以来国内对所有制问题的争论作一个简要的综述,以反映我国所有制问题研究的最新进展。     

Benchmark Dose Analysis via Nonparametric Regression Modeling

Estimation of benchmark doses (BMDs) in quantitative risk assessment traditionally is based upon parametric dose‐response modeling. It is a well‐known concern, however, that if the chosen parametric model is uncertain and/or misspecified, inaccurate and possibly unsafe low‐dose inferences can result. We describe a nonparametric approach for estimating BMDs with quantal‐response data based on an isotonic regression method, and also study use of corresponding, nonparametric, bootstrap‐based confidence limits for the BMD. We explore the confidence limits’ small‐sample properties via a simulation study, and illustrate the calculations with an example from cancer risk assessment. It is seen that this nonparametric approach can provide a useful alternative for BMD estimation when faced with the problem of parametric model uncertainty.     

Robust bootstrap forecast densities for GARCH returns and volatilities

Bootstrap procedures are useful to obtain forecast densities for both returns and volatilities in the context of generalized autoregressive conditional heteroscedasticity models. In this paper, we analyse the effect of additive outliers on the finite sample properties of these bootstrap densities and show that, when obtained using maximum likelihood estimates of the parameters and standard filters for the volatilities, they are badly affected with dramatic consequences on the estimation of Value-at-Risk. We propose constructing bootstrap densities for returns and volatilities using a robust parameter estimator based on variance targeting implemented together with an adequate modification of the volatility filter. We show that the performance of the proposed procedure is adequate when compared with available robust alternatives. The results are illustrated with both simulated and real data.     

Lag Length Selection for Unit Root Tests in the Presence of Nonstationary Volatility

A number of recent papers have focused on the problem of testing for a unit root in the case where the driving shocks may be unconditionally heteroskedastic. These papers have, however, taken the lag length in the unit root test regression to be a deterministic function of the sample size, rather than data-determined, the latter being standard empirical practice. We investigate the finite sample impact of unconditional heteroskedasticity on conventional data-dependent lag selection methods in augmented Dickey–Fuller type regressions and propose new lag selection criteria which allow for unconditional heteroskedasticity. Standard lag selection methods are shown to have a tendency to over-fit the lag order under heteroskedasticity, resulting in significant power losses in the (wild bootstrap implementation of the) augmented Dickey–Fuller tests under the alternative. The proposed new lag selection criteria are shown to avoid this problem yet deliver unit root tests with almost identical finite sample properties as the corresponding tests based on conventional lag selection when the shocks are homoskedastic.     

Performance of traditional f tests in repeated measures designs under covariance heterogeneity

This study investigates the performance of two traditional F tests, one for main effects and the other for interaction in repeated measures designs under several conditions of covariance heterogeneity. Overall, the test for interaction is more vulnerable than the one for main effects. Distortion in the level of significance is less serious for the case of equal group size.     

Estimation and Inference for Linear Panel Data Models Under Misspecification When Both n and T are Large

This article considers fixed effects (FE) estimation for linear panel data models under possible model misspecification when both the number of individuals, n, and the number of time periods, T, are large. We first clarify the probability limit of the FE estimator and argue that this probability limit can be regarded as a pseudo-true parameter. We then establish the asymptotic distributional properties of the FE estimator around the pseudo-true parameter when n and T jointly go to infinity. Notably, we show that the FE estimator suffers from the incidental parameters bias of which the top order is O(T^{? 1}), and even after the incidental parameters bias is completely removed, the rate of convergence of the FE estimator depends on the degree of model misspecification and is either (nT)^{? 1/2} or n^{? 1/2}. Second, we establish asymptotically valid inference on the (pseudo-true) parameter. Specifically, we derive the asymptotic properties of the clustered covariance matrix (CCM) estimator and the cross-section bootstrap, and show that they are robust to model misspecification. This establishes a rigorous theoretical ground for the use of the CCM estimator and the cross-section bootstrap when model misspecification and the incidental parameters bias (in the coefficient estimate) are present. We conduct Monte Carlo simulations to evaluate the finite sample performance of the estimators and inference methods, together with a simple application to the unemployment dynamics in the U.S.     

Regression for non-Euclidean data using distance matrices

Regression methods for common data types such as measured, count and categorical variables are well understood but increasingly statisticians need ways to model relationships between variable types such as shapes, curves, trees, correlation matrices and images that do not fit into the standard framework. Data types that lie in metric spaces but not in vector spaces are difficult to use within the usual regression setting, either as the response and/or a predictor. We represent the information in these variables using distance matrices which requires only the specification of a distance function. A low-dimensional representation of such distance matrices can be obtained using methods such as multidimensional scaling. Once these variables have been represented as scores, an internal model linking the predictors and the responses can be developed using standard methods. We call scoring as the transformation from a new observation to a score, whereas backscoring is a method to represent a score as an observation in the data space. Both methods are essential for prediction and explanation. We illustrate the methodology for shape data, unregistered curve data and correlation matrices using motion capture data from an experiment to study the motion of children with cleft lip.