基于动态偏离-份额模型的武汉市产业结构演变研究

区域产业结构的优化和升级是区域经济发展的基本动力.文章分别运用静态和动态偏离-份额分析法研究了2000～2009年武汉市以湖北省为背景区域的三次产业结构.采集数据后,整理数据得到了静态和动态武汉市各产业和整体的分享份额、结构份额和竞争份额.分析后得到了武汉市三次产业结构演进的趋势,并具体分析了第一产业、第二产业、第三产业的发展形势与优势,为客观分析武汉产业发展阶段与产业发展优势提供决策参考.     

论CAFTA进程中我国周边省份产业结构调整策略

随着经济全球化与一体化的发展,产业结构的高度与产业竞争力将决定着一国或地区的经济发展速度与质量。中国——东盟自由贸易区的建立,为我国周边省区的经济发展提供了有利的机遇,但这些省区之间、与东盟各国都存在一定程度的产业同构现象,因此必须合理配置同构产业、加快产业结构的调整、整合产业链、加快产业的自主技术创新,并进行相应的就业结构协调。     

能源富集区域产业同构测度研究——以环鄂尔多斯盆地陕甘宁晋蒙五省区为例

基于正态分布的区间估计方法,设计新的产业同构测度指数,对环鄂尔多斯盆地陕甘宁晋蒙五省区能源产业同构进行实证测度。研究结果表明:由于缺乏区域之间的统筹协调和分工协作,五省区能源产业结构高度趋同,因此需要在国家战略层面上通过一种制度安排,使该地区经济发展战略同国家整体发展战略相兼容,从而在符合能源禀赋和比较优势的区域经济发展与基于行政区划下的地方治理之间取得均衡。     

长三角经济圈与京津冀经济圈的比较分析

本文通过对长三角经济圈与京津冀经济圈在全国的地位及比较分析,讨论两大经济圈发展状况和在各自所处的区域经济发展阶段所面临的问题。长三角经济圈需要进一步推动产业升级和结构调整,解决产业同构问题,协同参与国际分工与竞争;京津冀经济圈则亟待完善区域一体化设计,强化市场调节作用,形成整体产业互补和分工。     

中国式产业现代化的测度及区域差异分析

文章从中国式产业基础高级化、中国式产业结构现代化、中国式产业协同现代化、中国式产业创新现代化与中国式产业融合现代化5个方面构建了中国式产业现代化评价指标体系,基于2015—2021年我国30个省份的面板数据,使用QAP方法分析中国式产业现代化发展状况。研究结果表明：中国式产业现代化虽然整体发展水平较低但表现出攀升趋势;区域差异显著,呈现东部地区>中部地区>西部地区的阶梯分布状态;中国式产业现代化水平的区域差异主要源自地区间差异,并表现出减小趋势;中国式产业基础高级化子维度差异是引起中国式产业现代化产生区域差异的主要原因,其次为中国式产业融合现代化、中国式产业结构现代化、中国式产业协同现代化、中国式产业创新现代化子维度差异。应完善中国式产业现代化顶层设计、实施地区产业协同发展战略、加速中国式产业基础高级化进程,推动中国式产业现代化发展。     

产业同构的利弊分析

由于历史原因和现实生产生活过程中对新型消费品需求的同步化趋势、地区利益的客观存在等原因,产业结构趋同化现象在我国已是由来已久.产业同构究竟是利是弊?学界对此多有关注、多有评述.     

区域主导产业的选择基准研究

 中国成功加入WTO，这表明中国将要融入经济全球化，经济全球化的本质特征是一场以世界发达国家为主导，国际跨国公司为主要动力的全球范围内的产业结构调整，这种调整，不仅是一些产业的整体转移，更重要的是同一产业的部分环节在全球范围内的转移。为适应经济全球化的要求，中国必须进行产业结构的调整与升级，优化区域产业结构，按经济全球化条件下国际分工的要求，围绕主导产业建立起以主导产业为核心的、各产业协调配套、竞争优势显著的区域产业系统。区域主导产业是区域产业系统中的经济主体和骨骼，能否正确的选择区域的主导产业，合理的确定其发展规模和发展速度，关系到区域经济得到整体发展方向和发展速度，关系到区域经济的整体发展方向和发展速度，更关系到我国经济在世界经济中的竞争地位。     

中国区域高技术产业技术创新效率评价研究——基于客观加权的网络SBM模型

将高技术产业技术创新活动分解为技术研发过程和技术转化过程,通过构建网络SBM模型和Tobit模型,较全面的评价和分析了中国区域高技术产业技术创新活动.研究结果表明:现阶段中国高技术产业形成了以原始创新资源投入为主,集成创新和引进消化吸收再创新资源投入为辅的创新模式;技术创新活动的整体效率和两子过程效率都较低,存在明显的省际差异;相对于技术研发过程的低效而言,技术转化过程并不是导致技术创新活动效率低下的主要原因;大部分地区的高技术产业表现为两过程效率的双重低下或一高一低;区域经济实力、产业结构、政府对技术创新活动的支持力度对高技术产业技术创新效率具有显著的促进作用.     

中国中西部能源产业的份额、偏离和影响的扩展研究

首次将区域发展优势变量加入到传统SSA模型中对该模型进行计算维度的扩展,并将其运用于中西部能源产业发展进程的实证分析。研究结果表明:扩展后的SSA模型在产业结构、区域发展及行业竞争等方面的表征意义更加显著、内涵更为丰富,再次有力地证明了中国的能源产业正在逐步向西部转移的结论;析出了西部各省区具有产业结构优势、竞争力优势且应作为重点发展对象的产业部门;比较了国家政策和地方政策对于能源产业发展推动力的区别,并指出在制定区域发展政策时各省区应该重点考量的产业特征。     

广东省产业结构演进预测及发展战略选择

广东省正在进行新一轮的经济结构调整.产业结构合理性影响着区域经济的可持续发展.文章对广东省产业结构总体发展趋势做出判断:广东省产业发展正处于"二、三、一"向"三、二、一"后期工业化阶段发展的过渡阶段;应用时间序列模型预测了广东省2010年及2015年的产业结构演进趋势,并据此剖析了产业演进转型中的偏差与问题.最后给出了广东省推进产业结构演进的战略选择建议.     

A Review of Some Modern Approaches to the Problem of Trend Extraction

This article presents a review of some modern approaches to trend extraction for one-dimensional time series, which is one of the major tasks of time series analysis. The trend of a time series is usually defined as a smooth additive component which contains information about the time series global change, and we discuss this and other definitions of the trend. We do not aim to review all the novel approaches, but rather to observe the problem from different viewpoints and from different areas of expertise. The article contributes to understanding the concept of a trend and the problem of its extraction. We present an overview of advantages and disadvantages of the approaches under consideration, which are: the model-based approach (MBA), nonparametric linear filtering, singular spectrum analysis (SSA), and wavelets. The MBA assumes the specification of a stochastic time series model, which is usually either an autoregressive integrated moving average (ARIMA) model or a state space model. The nonparametric filtering methods do not require specification of model and are popular because of their simplicity in application. We discuss the Henderson, LOESS, and Hodrick–Prescott filters and their versions derived by exploiting the Reproducing Kernel Hilbert Space methodology. In addition to these prominent approaches, we consider SSA and wavelet methods. SSA is widespread in the geosciences; its algorithm is similar to that of principal components analysis, but SSA is applied to time series. Wavelet methods are the de facto standard for denoising in signal procession, and recent works revealed their potential in trend analysis.     

On spatiotemporal prediction for on-line monitoring data

Spatiotemporal prediction is of interest in many areas of applied statistics, especially in environmental monitoring with on-line data information. At first, this article reviews the approaches for spatiotemporal modeling in the context of stochastic processes and then introduces the new class of spatiotemporal dynamic linear models. Further, the methods for linear spatial data analysis, universal kriging and trend surface prediction, are related to the method of spatial linear Bayesian analysis. The Kalman filter is the preferred method for temporal linear Bayesian inferences. By combining the Kalman filter recursions with the trend surface predictor and universal kriging predictor, the prior and posterior spatiotemporal predictors for the observational process are derived, which form the main result of this article. The problem of spatiotemporal linear prediction in the case of unknown first and second order moments is treated as well.     

A New Model of Trend Inflation

This article introduces a new model of trend inflation. In contrast to many earlier approaches, which allow for trend inflation to evolve according to a random walk, ours is a bounded model which ensures that trend inflation is constrained to lie in an interval. The bounds of this interval can either be fixed or estimated from the data. Our model also allows for a time-varying degree of persistence in the transitory component of inflation. In an empirical exercise with CPI inflation, we find the model to work well, yielding more sensible measures of trend inflation and forecasting better than popular alternatives such as the unobserved components stochastic volatility model. This article has supplementary materials online.     

Case study: shipping trend estimation and prediction via multiscale variance stabilisation

Shipping and shipping services are a key industry of great importance to the economy of Cyprus and the wider European Union. Assessment, management and future steering of the industry, and its associated economy, is carried out by a range of organisations and is of direct interest to a number of stakeholders. This article presents an analysis of shipping credit flow data: an important and archetypal series whose analysis is hampered by rapid changes of variance. Our analysis uses the recently developed data-driven Haar–Fisz transformation that enables accurate trend estimation and successful prediction in these kinds of situation. Our trend estimation is augmented by bootstrap confidence bands, new in this context. The good performance of the data-driven Haar–Fisz transform contrasts with the poor performance exhibited by popular and established variance stabilisation alternatives: the Box–Cox, logarithm and square root transformations.     

On the Discretization of Continuous-Time Filters for Nonstationary Stock and Flow Time Series

This article discusses the discretization of continuous-time filters for application to discrete time series sampled at any fixed frequency. In this approach, the filter is first set up directly in continuous-time; since the filter is expressed over a continuous range of lags, we also refer to them as continuous-lag filters. The second step is to discretize the filter itself. This approach applies to different problems in signal extraction, including trend or business cycle analysis, and the method allows for coherent design of discrete filters for observed data sampled as a stock or a flow, for nonstationary data with stochastic trend, and for different sampling frequencies. We derive explicit formulas for the mean squared error (MSE) optimal discretization filters. We also discuss the problem of optimal interpolation for nonstationary processes – namely, how to estimate the values of a process and its components at arbitrary times in-between the sampling times. A number of illustrations of discrete filter coefficient calculations are provided, including the local level model (LLM) trend filter, the smooth trend model (STM) trend filter, and the Band Pass (BP) filter. The essential methodology can be applied to other kinds of trend extraction problems. Finally, we provide an extended demonstration of the method on CPI flow data measured at monthly and annual sampling frequencies.     

Inference of the Trend in a Partially Linear Model with Locally Stationary Regressors

In this article, we construct the uniform confidence band (UCB) of nonparametric trend in a partially linear model with locally stationary regressors. A two-stage semiparametric regression is employed to estimate the trend function. Based on this estimate, we develop an invariance principle to construct the UCB of the trend function. The proposed methodology is used to estimate the Non-Accelerating Inflation Rate of Unemployment (NAIRU) in the Phillips Curve and to perform inference of the parameter based on its UCB. The empirical results strongly suggest that the U.S. NAIRU is time-varying.     

The Estimation and Inference of a Panel Cointegration Model with a Time Trend

This article investigates the asymptotic properties of coefficient estimators in the panel cointegration model with a time trend. We find that the bias of OLS estimator for the slope coefficient in the panel cointegration model with a time trend is distinct from that in the panel cointegration model without a time trend. Meanwhile, the variance of the limiting distribution for the slope coefficient is larger in the panel cointegration model with a time trend than without a time trend.     

Spatial modeling using frequentist approach for disease mapping

In this article, a generalized linear mixed model (GLMM) based on a frequentist approach is employed to examine spatial trend of asthma data. However, the frequentist analysis of GLMM is computationally difficult. On the other hand, the Bayesian analysis of GLMM has been computationally convenient due to the advent of Markov chain Monte Carlo algorithms. Recently developed data cloning (DC) method, which yields to maximum likelihood estimate, provides frequentist approach to complex mixed models and equally computationally convenient method. We use DC to conduct frequentist analysis of spatial models. The advantages of the DC approach are that the answers are independent of the choice of the priors, non-estimable parameters are flagged automatically, and the possibility of improper posterior distributions is completely avoided. We illustrate this approach using a real dataset of asthma visits to hospital in the province of Manitoba, Canada, during 2000–2010. The performance of the DC approach in our application is also studied through a simulation study.     

An Accelerated Model for Recurrence Data

Recurrence data usually come from sampling a system in different ages. It is common in areas of manufacturing, reliability, medicine, and risk analysis. In this article, an accelerated model for recurrence data is proposed. The model is based on the time between failures (TBFs) for an accelerated time regression method using the number of failures as a dummy covariate. Using this model, the repair (or cure) effect can also be studied. A graphical display of recurrence data created by plotting the log-TBFs versus the number of failures is proposed to detect any linear or nonlinear trend for log-TBFs. The model is then extended to incorporate covariates and/or time factors. Two data sets are used to demonstrate the usefulness of the proposed model.     

Unit Roots,Level Shifts,and Trend Breaks in Per Capita Output: A Robust Evaluation

Determining whether per capita output can be characterized by a stochastic trend is complicated by the fact that infrequent breaks in trend can bias standard unit root tests towards nonrejection of the unit root hypothesis. The bulk of the existing literature has focused on the application of unit root tests allowing for structural breaks in the trend function under the trend stationary alternative but not under the unit root null. These tests, however, provide little information regarding the existence and number of trend breaks. Moreover, these tests suffer from serious power and size distortions due to the asymmetric treatment of breaks under the null and alternative hypotheses. This article estimates the number of breaks in trend employing procedures that are robust to the unit root/stationarity properties of the data. Our analysis of the per capita gross domestic product (GDP) for Organization for Economic Cooperation and Development (OECD) countries thereby permits a robust classification of countries according to the “growth shift,” “level shift,” and “linear trend” hypotheses. In contrast to the extant literature, unit root tests conditional on the presence or absence of breaks do not provide evidence against the unit root hypothesis.