互联网信息服务带来改变 消费者确定性、消费者满意度和中国品牌的发展

随着时间的推移,中国的消费者将能够在越来越充分的信息背景下做出消费选择,即使是针对新的、不熟悉的商品。随着时间的推移,制造商会意识到开发质量稳定、品牌知名的优势产品的莫大益处。届时,品牌的价值将大大增加,高质量产品的价格将会上升,劳动力也将得到     

Handling Missing Data by Re-approaching Non-respondents

When handling missing data, a researcher should be aware of the mechanism underlying the missingness. In the presence of non-randomly missing data, a model of the missing data mechanism should be included in the analyses to prevent the analyses based on the data from becoming biased. Modeling the missing data mechanism, however, is a difficult task. One way in which knowledge about the missing data mechanism may be obtained is by collecting additional data from non-respondents. In this paper the method of re-approaching respondents who did not answer all questions of a questionnaire is described. New answers were obtained from a sample of these non-respondents and the reason(s) for skipping questions was (were) probed for. The additional data resulted in a larger sample and was used to investigate the differences between respondents and non-respondents, whereas probing for the causes of missingness resulted in more knowledge about the nature of the missing data patterns.     

Capitalization and the Voucher: An Analysis of Precinct Returns from California's Proposition 174

In 1993, Californians voted on a school voucher initiative. We hypothesize that homeowners in good school districts understood the voucher to be a threat to their property values and thus voted against it. Precinct returns from Los Angeles County confirm this hypothesis. We also examine an alternative hypothesis explaining the relationship between school quality and precinct returns. According to the alternative, voters perceived the initiative to be a referendum on public school quality. To distinguish between the two hypotheses, we compare the voting patterns of homeowners and renters. The comparison does not favor one hypothesis over the other.     

A Brief Survey of Modern Optimization for Statisticians

Modern computational statistics is turning more and more to high‐dimensional optimization to handle the deluge of big data. Once a model is formulated, its parameters can be estimated by optimization. Because model parsimony is important, models routinely include non‐differentiable penalty terms such as the lasso. This sober reality complicates minimization and maximization. Our broad survey stresses a few important principles in algorithm design. Rather than view these principles in isolation, it is more productive to mix and match them. A few well‐chosen examples illustrate this point. Algorithm derivation is also emphasized, and theory is downplayed, particularly the abstractions of the convex calculus. Thus, our survey should be useful and accessible to a broad audience.     

Modelling innovation support systems for regional development – analysis of cluster structures in innovation in Portugal

The present article offers a concise theoretical conceptualization and operational analysis of the contribution of innovation to regional development. The latter concepts are closely related to geographical proximity, knowledge diffusion and filters and clustering. Institutional innovation profiles and regional patterns of innovation are two mutually linked, novel conceptual elements in this article. Next to a theoretical framing, the article employs the regional innovation systems concept as a vehicle to analyse institutional innovation profiles. Our case study addresses three Portuguese regions and their institutions, included in a web-based inventory of innovation agencies which offered the foundation for an extensive database. This data-set was analysed by means of a recently developed principal coordinates analysis followed by a Logistic Biplot approach (leading to a Voronoi mapping) to design a systemic typology of innovation structures where each institution is individually represented. There appears to be a significant difference in the regional innovation patterns resulting from the diverse institutional innovation profiles concerned. These profiles appear to be region specific. Our conclusion highlights the main advantages in the use of the method used for policy-makers and business companies.     

Institutional Pressures and an Evolving Forest Carbon Market

It is estimated that 87 million acres of forestland in the United States (US) is managed by private industrial forestland owners (nearly 14% of the forestland nationwide). Private industrial forestland owners include forest product companies, Timber Investment Management Organizations (TIMOs) and Real Estate Investment Trusts (REITs). Current regulatory and voluntary carbon markets, as well as proposed national emission reduction legislation, in the US make provisions for substantial carbon offsets from domestic forestry projects. This research employs the theory of planned behavior by means of an online questionnaire in order to survey large industrial forestland owners (>30 000 acres) regarding intentions to manage forestland for carbon sequestration and trading. Quantitative results suggest that very few organizations (18%) were managing forestland for carbon values. Attitudes towards carbon sequestration and trading were significantly influenced by the managers' beliefs that emission reduction legislation would become law and that US forest carbon offsets can be used as a legitimate climate change mitigation tool. Qualitative results revealed that most organizations are taking a passive approach to carbon sequestration and trading until a suitable regulatory framework emerges and carbon prices provide the conditions for a sound investment. The researchers suggest that, given the size and scope of the climate change phenomenon as well as the multifaceted and unified mitigation strategy required to address it, the development of functional carbon markets will be an iterative process and may require a global carbon framework that reflects the globalized nature of the forest economy. Copyright © 2012 John Wiley & Sons, Ltd and ERP Environment     

Efficient minimum distance estimation with multiple rates of convergence

This paper extends the asymptotic theory of GMM inference to allow sample counterparts of the estimating equations to converge at (multiple) rates, different from the usual square-root of the sample size. In this setting, we provide consistent estimation of the structural parameters. In addition, we define a convenient rotation in the parameter space (or reparametrization) to disentangle the different rates of convergence. More precisely, we identify special linear combinations of the structural parameters associated with a specific rate of convergence. Finally, we demonstrate the validity of usual inference procedures, like the overidentification test and Wald test, with standard formulas. It is important to stress that both estimation and testing work without requiring the knowledge of the various rates. However, the assessment of these rates is crucial for (asymptotic) power considerations.Possible applications include econometric problems with two dimensions of asymptotics, due to trimming, tail estimation, infill asymptotic, social interactions, kernel smoothing or any kind of regularization.     

Public school availability for two-year olds and mothers' labour supply

French children start public school either the year they turn two or the year they turn three. We evaluate the impact of this unique schooling policy on maternal labour supply. Using a Regression–Discontinuity design, we show that early school availability has a significant employment effect on lone mothers, but no effect on two-parent families. Also we show that the effect grows larger as the child grows older and as the family loses eligibility for child benefits. Finally, we provide some new evidence that school enrolment at the age of two has no adverse effect on children's subsequent educational outcomes.     

Bayesian inference for periodic regime-switching models

We present a general class of nonlinear time-series Markov regime-switching models for seasonal data which may exhibit periodic features in the hidden Markov process as well as in the laws of motion in each of the regimes. This class of models allows for non-trivial dependencies between seasonal, cyclical and long-term patterns in the data. To overcome the computational burden we adopt a Bayesian approach to estimation and inference. This paper contains two empirical examples as illustration, one uses housing starts data while the other employs US post-Second World War industrial production. © 1998 John Wiley & Sons, Ltd.     

Open Variety Rights: Rethinking the Commodification of Plants

Open Variety Rights refer to the removal of the conditions attached to the usage of plants in agriculture, and to an analogy with open licensing in informatics that is notably different from the one that is taking shape at the intersection of the life sciences and informatics. This paper argues for the open licensing of plants as part of a response to the commodification of DNA; rather than a singular focus on farming or source code, this implies an analogy that aims at the removal of restrictions, whether as seeds, biodiversity, genetic sequences or DNA in informatics formats. First, the commodification of plants will be conceptualized. Second, the proposal for open licensing for varieties of crops will be discussed. Third, examples of the introduction of open licensing in the life sciences will be examined in relation to the potential to reformulate the definition of ‘openness’ to support those who wish to live and work with plants on their own terms.