中国彝语文研究的回顾与展望（1992-2021）——基于CiteSpace的文献计量分析

本文以CNKI核心期刊和CSSCI文献为数据来源,运用文献计量工具CiteSpace,从发文量、刊发载体、作者、研究机构、研究热点和研究演进等维度,对1992-2021年间国内彝语文研究文献进行了可视化分析。研究发现,国内彝语文发文量总体比较稳定,已形成核心作者和核心研究机构,近三十年彝语文研究热点主要集中在“凉山彝语”“彝语支”“彝文古籍”和“信息处理”等。同时,研究还发现,彝语文研究阵地分布不均,彝语文研究的跨学科表现力还有待进一步增强;彝语文研究者缺乏合作,核心研究机构分布失衡;研究角度和内容单一,新的研究热点尚未形成。基于此,本文提出了针对性的建议,以期为彝语文研究提供参考和借鉴。     

Nonparametric Nonlinear Cotrending Analysis,With an Application to Interest and Inflation in the United States

Given the assumption that the components of a vector time series are stationary around nonlinear deterministic time trends, nonlinear cotrending is the phenomenon that one or more linear combinations of the time series are stationary around a linear trend or a constant; hence, the series have common nonlinear deterministic time trends. In this article, I develop nonparametric tests for nonlinear cotrending, and I derive nonparametric estimators of the cotrending vectors. I apply this approach to the federal funds rate and the consumer price index inflation rate in the United States, using monthly data, to analyze the price puzzle.     

Religion and Trends in Euthanasia Attitudes among U.S. Adults, 1977–2004

Though many studies address the role of religion in predicting social attitudes over time, none has examined this relationship specifically for euthanasia. Using a large, nationally representative data source, this study seeks to address this void. Our findings indicate that considerable differences exist among religious denominations regarding the legalization of euthanasia. Specifically, we note a liberalizing trend for all included denominations. We also demonstrate substantial differences in the rates of liberalization, particularly in comparison to conservative Protestants. We conclude with an assessment of our findings relative to previous studies on religion and public opinion.

奥巴马政府对华政策新态势与中美关系

奥巴马上台后对美国的国家安全战略进行了重大调整,以重塑美国的领导地位。这种调整必然带来美国对华政策的变化,美国对华政策展现出一些新的特点,其总体特征是“积极接触+多方位合作+战略遏制”。对于奥巴马政府对华政策的新态势,中国应该积极加以应对,维护本国利益。      

从《课程计划》的变化看我国中小学课程改革的方向

新中国建立以来,中小学的课程设置大体上分工具类、知识类、技艺类三大类。国家也对中小学课程进行了多次改革,虽取得很大成就,但也存在诸多弊端。新时期课程改革呈现新的趋势：(一)课程的理论研究与改革实践密切结合。(二)进一步优化课程结构。(三)注重课程内容的现代化、人本化。(四)实现课程管理的科学化、民主化。     

新时代中国经济高质量发展若干问题研究

党的十九届六中全会强调要推动经济高质量发展。党的十八大以来,高质量发展逐步成为新时代中国发展的主题。中国经济高质量发展是适应新时代中国社会主要矛盾变化的主动作为,是应对百年未有之大变局的必然选择,是实现新时代发展目标的必由之路,是资源环境约束下可持续发展的必然要求。高质量发展呈现五大特征,即发展方式由规模速度型转向质量效率型,产业结构由中低端水平转向中高端水平,增长动力由传统要素驱动转向新兴要素驱动,资源配置由市场起基础性作用转向起决定性作用,经济福祉由先好先富起来转向包容共享共富。需要重点把握发展速度和质量、创新和其他生产要素、供给侧和需求侧三对关系,从生产、分配、流通、消费四个环节,系统推动中国经济高质量发展。     

Urban vans,e-commerce and road freight transport

Abstract

A city will typically depend on at least 150 supply chains and freight is a key part of them. This article aims to explore at the qualitative and qualitative level (1) how e-commerce drives both travel and urban freight transport and (2) to estimate how various manufacturing activities determine the stability of demand for freight transport. The article provides elasticity estimates on (1) the demand for road freight transport for five industry sectors using a time series framework; data on GDP (gross value added) and on fuel costs per t-km (tonne-km). A key finding is that e-commerce induces freight traffic, by vans, but the decline in freight intensity of the economy is only temporary since urban economic activity contributes to growth of freight. Our analysis of five manufacturing sectors confirms that cyclical sectors are more sensitive to energy price gyrations over time than non-cyclical sectors. Price elasticities are high but comparable to other studies. The income elasticity of freight transport is large for the five sectors, but the high heterogeneity of freight sectors means that predicting this activity is challenging. It is observed that truck freight intensity (km of trucks per GVA) declines but not enough to offset the rise in energy needed to fuel the entire freight transport sector; it is likely that the rise in van freight increases the demand for freight energy in urban regions.     

Estimation When a Parameter is on a Boundary

This paper establishes the asymptotic distribution of an extremum estimator when the true parameter lies on the boundary of the parameter space. The boundary may be linear, curved, and/or kinked. Typically the asymptotic distribution is a function of a multivariate normal distribution in models without stochastic trends and a function of a multivariate Brownian motion in models with stochastic trends. The results apply to a wide variety of estimators and models. Examples treated in the paper are: (i) quasi-ML estimation of a random coefficients regression model with some coefficient variances equal to zero and (ii) LS estimation of an augmented Dickey-Fuller regression with unit root and time trend parameters on the boundary of the parameter space.     

Empirical Limits for Time Series Econometric Models

This paper characterizes empirically achievable limits for time series econometric modeling and forecasting. The approach involves the concept of minimal information loss in time series regression and the paper shows how to derive bounds that delimit the proximity of empirical measures to the true probability measure (the DGP) in models that are of econometric interest. The approach utilizes joint probability measures over the combined space of parameters and observables and the results apply for models with stationary, integrated, and cointegrated data. A theorem due to Rissanen is extended so that it applies directly to probabilities about the relative likelihood (rather than averages), a new way of proving results of the Rissanen type is demonstrated, and the Rissanen theory is extended to nonstationary time series with unit roots, near unit roots, and cointegration of unknown order. The corresponding bound for the minimal information loss in empirical work is shown not to be a constant, in general, but to be proportional to the logarithm of the determinant of the (possibility stochastic) Fisher–information matrix. In fact, the bound that determines proximity to the DGP is generally path dependent, and it depends specifically on the type as well as the number of regressors. For practical purposes, the proximity bound has the asymptotic form (K/2)log n, where K is a new dimensionality factor that depends on the nature of the data as well as the number of parameters in the model. When ‘good’ model selection principles are employed in modeling time series data, we are able to show that our proximity bound quantifies empirical limits even in situations where the models may be incorrectly specified. One of the main implications of the new result is that time trends are more costly than stochastic trends, which are more costly in turn than stationary regressors in achieving proximity to the true density. Thus, in a very real sense and quantifiable manner, the DGP is more elusive when there is nonstationarity in the data. The implications for prediction are explored and a second proximity theorem is given, which provides a bound that measures how close feasible predictors can come to the optimal predictor. Again, the bound has the asymptotic form (K/2)log n, showing that forecasting trends is fundamentally more difficult than forecasting stationary time series, even when the correct form of the model for the trends is known.