Modeling diffusion of multi-generational LCD TVs while considering generation-specific price effects and consumer behaviors

An effective diffusion model characterized by the technology-induced function diversification and cost reductions can be developed to reflect whether competition exists among multiple generations of technology and to interpret how price reductions stimulate consumption. New technology can enhance the production skill levels of LCD TV manufacturing, enabling successive generation of LCD TV to become larger-sized than the previous generation, reducing the overall cost in manufacturing process and resulting in the price reduction of LCD TVs. However, previous diffusive predictions of LCD TVs using conventional multi-generational models ignore the price effect on market potentials and generational substitutions, so a novel generation-specific multi-generational model for the first time incorporating heterogeneous price elasticity and consumer behaviors across various LCD TV sizes is constructed in this work. This study applies nonlinear least square method to simulate the parameters of our modified model and further compares the accuracy between our modified model and the existing models. Analytical results indicate that price reduction strongly correlates with LCD TV sales, implying that price reductions increase the market potential of each LCD TV generation. Our modified model performs superior to the conventional multi-generational model in terms of predicting future shipment orbits of 26-, 42-, and 46-in. LCD TVs.     

The challenges of pre-launch forecasting of adoption time series for new durable products

The successful introduction of new durable products plays an important part in helping companies to stay ahead of their competitors. Decisions relating to these products can be improved by the availability of reliable pre-launch forecasts of their adoption time series. However, producing such forecasts is a difficult, complex and challenging task, mainly because of the non-availability of past time series data relating to the product, and the multiple factors that can affect adoptions, such as customer heterogeneity, macroeconomic conditions following the product launch, and technological developments which may lead to the product’s premature obsolescence. This paper provides a critical review of the literature to examine what it can tell us about the relative effectiveness of three fundamental approaches to filling the data void : (i) management judgment, (ii) the analysis of judgments by potential customers, and (iii) formal models of the diffusion process. It then shows that the task of producing pre-launch time series forecasts of adoption levels involves a set of sub-tasks, which all involve either quantitative estimation or choice, and argues that the different natures of these tasks mean that the forecasts are unlikely to be accurate if a single method is employed. Nevertheless, formal models should be at the core of the forecasting process, rather than unstructured judgment. Gaps in the literature are identified, and the paper concludes by suggesting a research agenda so as to indicate where future research efforts might be employed most profitably.     

Forecasting sales response for multiple time horizons and temporally aggregated data: A comparison of constant and stochastic coefficient models

Past research on time-varying sales-response models emphasized the application of different estimation techniques in examining variation in advertising effectiveness over time. This study focuses on comparing sales forecasts using constant and stochastic coefficients sales-response models. Selected constant and stochastic coefficient models are applied to six sets of bimonthly and one set of annual advertising and sales data to assess forecasting accuracy for time horizons of various lengths. Results show improved forecasting accuracy for a first-order autoregressive stochastic coefficient model, particularly in short-run forecasting applications.     

Forecasting accuracy of wind power technology diffusion models across countries

Wind power technology is analyzed in terms of diffusion, with incentive effects introduced as exogenous dynamics in the Generalized Bass Model (GBM) framework. Estimates and short-term forecasts of the life-cycles of wind power are provided for the US and Europe, as they have similar geographic areas, as well as for some leading European countries. GBMs have the best performance in model selection, and are ranked first in terms of forecast accuracy over a set of different accuracy measures and forecasting horizons, relative to the Standard Bass, Logistic, and Gompertz models.     

Comparing forecasts from fixed and variable coefficient models: The case of money demand

In this paper we introduce a class of tentatively plausible, fixed-coefficient models of money demand and evaluate their forecast performance. When these models are reestimated allowing all coefficients to vary over time, the forecasting performance improves dramatically. Aside from offering insights about improved methods of analyzing time series data, the most promising direct use for point estimates derived from time-varying coefficients is as an aid in calibrating proposed models of the kind discussed here.     

Forecasting the UK economy: Alternative forecasting methodologies and the role of off-model information

How did DSGE model forecasts perform before, during and after the financial crisis, and what type of off-model information can improve the forecast accuracy? We tackle these questions by assessing the real-time forecast performance of a large DSGE model relative to statistical and judgmental benchmarks over the period from 2000 to 2013. The forecasting performances of all methods deteriorate substantially following the financial crisis. That is particularly evident for the DSGE model’s GDP forecasts, but augmenting the model with a measure of survey expectations made its GDP forecasts more accurate, which supports the idea that timely off-model information is particularly useful in times of financial distress.     

Modelling optimal strategies for the allocation of wealth in multicurrency investments

This paper analyzes rates of return on financial assets denominated in five major currencies and provides a framework for the determination of optimal strategies for the allocation of wealth in multicurrency investments. Three models are estimated: a univariate autoregressive conditional heteroskedasticity (ARCH) model, an extended ARCH model using the random coefficient (RC) procedure, and a pure RC model. A comparison of the forecasts of these models with those generated by a random walk model demonstrates that forecasts based on the RC/extended ARCH procedure are superior to those based on the random walk model and those based on direct ARCH estimation. These results could be useful for both international investors for the allocation of their wealth among fixed-income investment securities and central banks for the management of their external reserve assets.     

创新扩散理论应用于IT领域研究综述

创新扩散理论(Innovation Diffusion Theory,简称IDT)主要研究创新在社会系统中传播扩散的过程。罗杰斯的创新扩散理论作为当前国际学术界技术创新扩散理论的重要研究成果,已被我国学者普遍了解和接受,并运用到众多研究领域。本文主要通过搜集2002-2010年期间有关创新扩散理论应用于IT领域的研究文献,分析综述。     

技术创新的扩散与中国的技术进步(英文)

本文讨论了技术创新转移过程中的激励、评估以及对引进技术的消化和再创新等问题.     

A conditionally heteroskedastic independent factor model with an application to financial stock returns

We propose a new conditionally heteroskedastic factor model, the GICA-GARCH model, which combines independent component analysis (ICA) and multivariate GARCH (MGARCH) models. This model assumes that the data are generated by a set of underlying independent components (ICs) that capture the co-movements among the observations, which are assumed to be conditionally heteroskedastic. The GICA-GARCH model separates the estimation of the ICs from their fitting with a univariate ARMA-GARCH model. Here, we will use two ICA approaches to find the ICs: the first estimates the components, maximizing their non-Gaussianity, while the second exploits the temporal structure of the data. After estimating and identifying the common ICs, we fit a univariate GARCH model to each of them in order to estimate their univariate conditional variances. The GICA-GARCH model then provides a new framework for modelling the multivariate conditional heteroskedasticity in which we can explain and forecast the conditional covariances of the observations by modelling the univariate conditional variances of a few common ICs. We report some simulation experiments to show the ability of ICA to discover leading factors in a multivariate vector of financial data. Finally, we present an empirical application to the Madrid stock market, where we evaluate the forecasting performances of the GICA-GARCH and two additional factor GARCH models: the orthogonal GARCH and the conditionally uncorrelated components GARCH.     

Diffusion of environmental business practices: A network approach

The purpose of this research is to build a conceptual foundation that examines network effects on the diffusion of environmental business practices (EBP) among suppliers. This research extends a network perspective to adoption of an environmental business practice across a large network of suppliers. The context of EBP is used to better understand adoption of a complex business practice, with perceived costs that are often greater than the perceived benefit. Variation in the level of structural and relationship embeddedness affect network diffusion of environmental business practices differently. Increased levels of structural and relational embeddedness are proposed to be positively associated with diffusion of EBP. From a practical standpoint, firms that leverage embeddedness may facilitate higher diffusion and adoption of environmental business practices. This facilitation may lead network actors to engage in EBP, and leverage benefits that may stem from these practices. This research introduces the concept of embeddedness to the environmental supply chain literature. Practicing managers can use the findings in this research to better position themselves within a network to diffuse EBP. This research also helps managers understand how supply chain members that are weakly connected to the primary network are important for introducing new ideas and innovations.     

A note on forecasting international tourism demand in Spain

This paper considers the extent to which price and income proxy variables help in forecasting tourist demand in Spain. Contrary to some recent studies, we found that the inputs' contribution in terms of fitting and forecasting is nil when compared with alternative univariate models. Whether these findings are the results of the restrictions embedded in building the proxy inputs or in a poor specification of the dynamics of these models remains to be seen. We also contend that when dealing with medium, long-term forecasting comparisons, the use of the traditional aggregate accuracy measures like RMSE and MAPE help very little in discriminating among competing models. In these situations, predicted annual growth rates may be a better alternative.     

Forecasting with factor-augmented error correction models

As a generalization of the factor-augmented VAR (FAVAR) and of the Error Correction Model (ECM), Banerjee and Marcellino (2009) introduced the Factor-augmented Error Correction Model (FECM). The FECM combines error-correction, cointegration and dynamic factor models, and has several conceptual advantages over the standard ECM and FAVAR models. In particular, it uses a larger dataset than the ECM and incorporates the long-run information which the FAVAR is missing because of its specification in differences. In this paper, we examine the forecasting performance of the FECM by means of an analytical example, Monte Carlo simulations and several empirical applications. We show that FECM generally offers a higher forecasting precision relative to the FAVAR, and marks a useful step forward for forecasting with large datasets.     

Forecasting automobile insurance paid claim costs using econometric and ARIMA models

Automobile insurance companies in the United States currently utilize simple exponential trend models to forecast paid claim costs, an important variable in ratemaking. This paper tests the performance of econometric and ARIMA models, as well as the current insurance industry method, in forecasting two paid claim cost series. The experiments encompass eight forecast periods ranging from 1974 through early 1983. The results indicate that automobile insurers could significantly improve their forecasts of property damage liability claim costs by adopting econometric models. For bodily injury liability claim costs, the accuracy of the econometric and insurance industry methods is approximately the same, and both outperform the ARIMA models. Overall, a net gain in accuracy could be achieved by adopting econometric models.     

Macroeconomic forecasting using penalized regression methods

We study the suitability of applying lasso-type penalized regression techniques to macroe-conomic forecasting with high-dimensional datasets. We consider the performances of lasso-type methods when the true DGP is a factor model, contradicting the sparsity assumptionthat underlies penalized regression methods. We also investigate how the methods handle unit roots and cointegration in the data. In an extensive simulation study we find that penalized regression methods are more robust to mis-specification than factor models, even if the underlying DGP possesses a factor structure. Furthermore, the penalized regression methods can be demonstrated to deliver forecast improvements over traditional approaches when applied to non-stationary data that contain cointegrated variables, despite a deterioration in their selective capabilities. Finally, we also consider an empirical applicationto a large macroeconomic U.S. dataset and demonstrate the competitive performance of penalized regression methods.     

组织派系中的“小世界”对知识扩散的影响研究评述

企业及学校中的“团队”建设和“团队”教学无不体现着小世界模型对于组织派系内创新知识的传播和培训效果的迁移的指导作用。本文介绍了小世界社会网络模型的基本特征和理论来源，以及它在实际工作和教学学习中的运用，并在最后提出了该模型进一步发展的趋势。     

基于扩散指数模型的房地产市场景气循环研究——以北京市为例

基于扩散指数模型构建房地产市场景气循环指标体系,采用北京市住宅市场与经济基本面的历史数据,计算不同时期北京市房地产市场的扩散指数,绘制北京市房地产市场的景气循环曲线,并根据景气循环曲线与历史数据对北京市未来房地产市场的景气情况进行预测。最后,提出在上述市场预测情况下的市场调控策略。     

农产品质量的安全与监管

农产品质量安全事关人民群众身体健康和生命安全,事关农业、农村经济的健康发展。因此,要通过加强农业标准化建设,推行农产品市场准入制度,建立、健全农产品检验检测体系和监管体系、可追溯制度,实现从农田到市场的全程监管。