Disaggregation of annual flow data with multiplicative trends

Various methods based on smoothing or statistical criteria have been used for constructing disaggregated values compatible with observed annual totals. The present method is based on a time‐series model in a state space form and allows for a prescribed multiplicative trend. It is applied to US GNP data which have been used for comparing methods suggested for this purpose. The model can be extended to include quarterly series, related to the unknown disaggregated values. But as the estimation criteria are based on prediction errors of the aggregated values, the estimated form may not be optimal for reproducing high‐frequency variations of the disaggregated values. Copyright © 1999 John Wiley & Sons, Ltd.     

Disaggregation of annual time-series data to quarterly figures: A comparative study

In this paper we consider some of the prominent methods that are available in the literature for the problem of disaggregating annual time-series data to quarterly figures. The procedures are briefly described and illustrated through a real data set. The performances of the methods are compared in a Monte Carlo study. The results indicate that the complicated model-based procedure is usually superior to other non-model-based alternatives in the large sample situations. Based on the simulation results, we make some recommendations regarding the use of these methods.     

Forecasting European GNP data through common factor models and other procedures

In this paper we present an extensive study of annual GNP data for five European countries. We look for intercountry dependence and analyse how the different economies interact, using several univariate ARIMA and unobserved components models and a multivariate model for the GNP incorporating all the common information among the variables. We use a dynamic factor model to take account of the common dynamic structure of the variables. This common dynamic structure can be non‐stationary (i.e. common trends) or stationary (i.e. common cycles). Comparisons of the models are made in terms of the root mean square error (RMSE) for one‐step‐ahead forecasts. For this particular group of European countries, the factor model outperforms the remaining ones. Copyright © 2002 John Wiley & Sons, Ltd.     

The data measurement process for UK GNP: stochastic trends,long memory,and unit roots

Much published data is subject to a process of revision due, for example, to additional source data, which generates multiple vintages of data on the same generic variable, a process termed the data measurement process or DMP. This article is concerned with several interrelated aspects of the DMP for UK Gross National Product. Relevant questions include the following. Is the DMP well behaved in the sense of providing a single stochastic trend in the vector time series of vintages? Is one of the vintages of data, for example the ‘final’, the sole vintage generating the long‐memory component? Does the multivariate framework proposed here add to the debate on the existence of a unit root in GNP? The likely implicit assumptions of users (that the DMP is well behaved and the final vintage is ‘best’) can be cast in terms of testable hypotheses; and we show that these ‘standard’ assumptions have not always been empirically founded. Copyright © 2002 John Wiley & Sons, Ltd.     

创新转型的宏观技术经济条件 ——基于韩国创新转型经验的中国国情判断

基于韩国创新转型经验数据的格兰杰因果关系检验,分析了国家创新转型的宏观技术经济条件。结果表明,社会劳动生产率和人均GNP是国家创新转型的长期驱动因素,两者分别代表了国家创新转型的宏观技术条件和宏观经济条件。参照韩国创新转型时期的宏观技术经济条件,研究认为,我国整体上尚不具备创新转型的宏观技术经济条件,但有13个省会城市基本具备了创新转型的宏观技术经济条件。     

西部地区社会经济系统的脆弱性及生态风险评估方法

本文分析了西部地区地理条件和生态环境的现状,并探讨了导致西部地区社会经济系统脆弱性的原因．利用绿色GNP的概念讨论了绿色GNP变化特点以及生态风险的评估方法,该方法对在生态脆弱区建立生态环境预警机制,避免生态风险,实现可持续发展具有重要意义．     

西太平洋地区区域经济发展及中国的对策

本文从区域经济研究角度出发,用丰富翔实的资料说明:西太平洋地区资源丰富、战后经济迅速增长使区内资本雄厚和高技术产业发达,是未来本区经济发展的基础;西太平洋地区的四种经济结构类型、两种重要的生产运动形式、政治稳定,是本区经济持续稳定增长的推动力.文章最后通过分析国际国内政治、经济和文化环境,提出中国的主要对策和未来十年经济将稳定增长的必然性.     

Electrical conductivity retention and electrochemical activity of CSA doped graphene/gold nanoparticle@ polyaniline composites

This paper reports the synthesis of CTAB mediated CSA doped PANI and GN/GNP@ PANI composite nanofibers. The as synthesized composite nanofibers were examined by TEM, SEM, XRD, Raman spectroscopy; UV–visible diffused reflectance spectroscopy and TGA. The CTAB mediated CSA doped composite nanofibers showed 59% higher DC electrical conductivity at ambient temperature than that of PANI,which might be due to the enhancement in the mobility of the charge carriers and reduction in hopping distance in the composite system. The CTAB mediated CSA doped composite nanofibers compared to PANI was observed to be showing enhanced DC electrical conductivity retention after various cycles of heating, suggesting an enhancement in thermal stability of the composite structure, which could be attributed to the synergistic effect of GN,GNP and PANI. Additionally, the composite nanofibers showed greater electrochemical activity and better capacitive performance and reduced optical bandgap than that of PANI.     

科技投入对GNP产出的影响

本文依据系统工程的理论和方法,以上海市有关统计资料为实例,探讨了在客观存在的宏观社会经济闭环运行机制条件下,科技投入对国民生产总值(GNP)产出的影响问题。给出了数学模型并进行了灵敏度分析。     

Forecasting US GNP growth: The role of uncertainty

A large number of models have been developed in the literature to analyze and forecast changes in output dynamics. The objective of this paper was to compare the predictive ability of univariate and bivariate models, in terms of forecasting US gross national product (GNP) growth at different forecasting horizons, with the bivariate models containing information on a measure of economic uncertainty. Based on point and density forecast accuracy measures, as well as on equal predictive ability (EPA) and superior predictive ability (SPA) tests, we evaluate the relative forecasting performance of different model specifications over the quarterly period of 1919:Q2 until 2014:Q4. We find that the economic policy uncertainty (EPU) index should improve the accuracy of US GNP growth forecasts in bivariate models. We also find that the EPU exhibits similar forecasting ability to the term spread and outperforms other uncertainty measures such as the volatility index and geopolitical risk in predicting US recessions. While the Markov switching time‐varying parameter vector autoregressive model yields the lowest values for the root mean squared error in most cases, we observe relatively low values for the log predictive density score, when using the Bayesian vector regression model with stochastic volatility. More importantly, our results highlight the importance of uncertainty in forecasting US GNP growth rates.