A Litterman BVAR approach for production forecasting of technology industries

Forecasting the production of technology industries is important to entrepreneurs and governments, but usually suffers from market fluctuation and explosion. This paper aims to propose a Litterman Bayesian vector autoregression (LBVAR) model for production prediction based on the interaction of industrial clusters. Related industries within industrial clusters are included into the LBVAR model to provide more accurate predictions. The LBVAR model possesses the superiority of Bayesian statistics in small sample forecasting and holds the dynamic property of the vector autoregression (VAR) model. Two technology industries in Taiwan, the photonics industry and semiconductor industry are used to examine the LBVAR model using a rolling forecasting procedure. As a result, the LBVAR model was found to be capable of providing outstanding predictions for these two technology industries in comparison to the autoregression (AR) model and VAR model.     

Better technology forecasting using systematic innovation methods

An evolved version of the Soviet-originated Theory of Inventive Problem Solving, TRIZ, contains a series of generically predictable technology and business evolution trends uncovered from the systematic analysis of over 2 million patents, academic journals and business texts. The current state of the art—recorded for the first time together in this paper—now bring the total number of generic technical trends to over 30, and the number of business trends to over 20. The paper describes some of the newly discovered trends, and their incorporation into a design method that allows individuals and businesses to first establish the relative maturity of their current systems, and then, more importantly, to identify areas where ‘evolutionary potential’ exists. The paper introduces this concept of evolutionary potential—defined as the difference between the relative maturity of the current system, and the point where it has reached the limits of each of the evolution trends—through a number of case study examples focused on the design and evolution of complex systems.     

Vacant technology forecasting using new Bayesian patent clustering

Vacant technology forecasting (VTF) is a technology forecasting approach to find technological needs for given industrial field in the future. It is important to know the future trend of developing technology for the R&D planning of a company and a country. In this paper, we propose a new Bayesian model for patent clustering. This is a VTF methodology based on patent data analysis. Our method is composed of Bayesian learning and ensemble method to construct the VTF model. To illustrate the practical way of the proposed methodology, we perform a case study of given technology domain using retrieved patent documents from patent databases in the world.     

A stochastic model for technology forecasting

A stochastic model for technology forecasting is proposed. A complete analysis of the model is given and application to a real problem is presented.     

The methodological worth of the Delphi forecasting technique

Focusing on the general issues of reliability and validity, several specific problems evident in reported applications of the Delphi method to forecasting are discussed. Reliability threats, which are noted in a number of procedural aspects of the method, arise from ill-considered procedural variations and lack of standardization. While validity threats are also found in several procedural aspects of the tool, they arise principally from pressures for convergence of predictions. This feature of the method, along with certain structural characteristics, is found to undermine critically its forecasting ability. Having discussed in some detail the nature of these difficulties, the paper closes with consideration of the reasons for the continued use of Delphi in spite of its shortcomings and with comments on alternative approaches.     

A reliability engineering approach for forecasting technological breakthroughs

One of the major attributes determining system reliability, the one that has received the most thorough and systematic study for many years, is system survival function. A “survival function” is a mathematical formula relating the probability of satisfactory performance of a system to time. Here, probability of satisfactory performance is synonymous with probability of nonfailure or probability of survival of a performing system.In breakthrough analysis of complex technological systems, the situation is somewhat similar but opposite to the above system reliability case. For breakthrough forecasting, the problem is to determine the probability of occurrence of success of a nonperforming system. Thus this paper presents a quantitative methodology for forecasting technological breakthroughs using a new concept of “attainability function,” derived in a similar fashion as the “reliability function.”     

Brownian agent-based technology forecasting

Today's innovation process is best characterized by nonlinearity and interaction. Agent-based models build on these concepts, but have not been useful in practice because they are either too complex or too simple to make a good match with reality. As a remedy, we employ a Brownian agent model with intermediate complexity to produce value-added technology forecasting. As an illustration with Korea's software industry data, computer simulation is carried out. Attracted by higher technology value, agents concentrate on specific technology regions, and form co-existing major technology regions of high density. A rough comparison with actual software production data exhibits a fair reflection of reality, and supports the underlying idea that economic motivation of agents should be considered.     

Validating an innovative real-time Delphi approach - A methodological comparison between real-time and conventional Delphi studies

A novel and innovative real-time Delphi technique is introduced in order to address previously identified weaknesses of the conventional Delphi method, such as complicated facilitator tasks, lack of real-time presentation of results, and difficulties in tracking progress over time. We demonstrate how the real-time (computer-based) method increases the efficiency of the process, accommodates expert availability, and reduces drop-out-rates. Modifications in the Delphi procedure (e.g. change of iteration principle) not only increase efficiency but also change the nature and process of the survey technique itself. By identifying and analysing three individual effects (initial condition effect, feedback effect, and iteration effect) we examine whether the modifications in the survey process cause deviations to the survey results. Empirical data obtained from both conventional as well as real-time Delphi studies is analysed based on multiple statistical analyses. The research findings indicate that significant differences between the two Delphi survey formats do not exist and final survey results are not affected by changes in the survey procedure.     

A copula-VAR-X approach for industrial production modelling and forecasting

World economies, and especially European ones, have become strongly interconnected in the last decade and a joint modelling is required. We propose here the use of copulae to build flexible multivariate distributions, since they allow for a rich dependence structure and more flexible marginal distributions that better fit the features of empirical data, such as leptokurtosis. We use our approach to forecast industrial production series in the core European Monetary Union (EMU) countries and we provide evidence that the copula-Vector Autoregression (VAR) model outperforms or at worst compares similarly to normal VAR models, keeping the same computational tractability of the latter approach.     

The UK consumption function and structural instability: improving forecasting performance using a time-varying parameter approach

Previous studies indicate that the poor forecasting performance of constant parameter UK consumption expenditure models is caused by structural instability in the underlying data generating process. Typically, this instability is removed by reparameterization within the constant parameter framework. An alternative modelling strategy is to allow some, or all, of the parameters to vary over time. A UK non-durable consumption expenditure model with time-varying parameters is developed, based on the permanent income hypothesis of Friedman (1957). This model takes into account temporal changes in the average and marginal propensities to consume. The variation in the parameter estimates is given an economic interpretation in terms of the influence of omitted variables, namely UK financial liberalization and expectational changes. The forecasting performance of this model is superior to that of two widely used constant parameter models. Further tests show that, even if these constant parameter models are respecified as time varying parameter models, the authors' model still retains a superior forecasting performance.     

A dissensus based online Delphi approach: An explorative research tool

This paper presents an adapted Delphi methodology that is, contrary to the classical Delphi design is not aiming to minimize expert estimation variance, but to maximize the range of expert opinions inputted sequentially into an online system. After discussing the traditional Delphi approach and its dissensus based derivatives, the author opens the case for a dissensus Delphi based explorative research tool with special consideration of the Delphi aim, the expert sample and the Delphi design. The proposed online Delphi process is then presented conceptually. Next, the proposed tool is demonstrated based on a prototype, exploring the barrier factors to the adoption of mobile data services. A discussion on the theoretical design and practical R&D experience of the dissensus based online Delphi approach concludes the paper.     

Notes on forecasting a chaotic series using regression

It is well known that a nonlinear recursive equation can produce a chaotic sequence at certain values of the parameter. Furthermore, in the chaotic regime, extremely small changes in the initial value or in the value of the parameter produce very large changes in the sequence. It is surprising therefore that a short segment of a chaotic sequence can be used to reconstruct large portions of the sequence and to forecast future values of the sequence over short ranges. Furthermore, while the accuracy of the forecasts is dependent on the precision of the data, the relationship is much less sensitive than might have been expected. This is demonstrated by fitting a two-parameter model to two different types of chaotic equations: one a polynomial and the other a trigonometric function. This leads to the expectation that under certain circumstances, it may be possible to forecast values in a chaotic series over limited ranges, even if initial data are somewhat degraded.     

A model for disruptive technology forecasting in strategic regional economic development

As regions look to increase their economic development activities, technology-based developments and the penchant for long-term developments in disruptive technologies like nanotechnology become an important part of the options available to these regions. There are typically many technologies and therefore product areas that the region, however, can further develop by investing resources in these areas. At the same time, other regions in the world are considering the same areas of great growth and potential financial and social returns. This paper proposes a model that analyzes several important factors that can lead to success in analyzing these factors promoting the idea that policy makers should analyze the situation from different perspectives to reach justifiable decisions. These factors include the research capabilities of the region, its commercialization and manufacturing capabilities and the markets on which they should focus. Several mathematical models are then presented to help in that endeavor.     

Testing the effectiveness of the French work-sharing reform: a forecasting approach

The macroeconomic impact of the French work-sharing reform of 2000 (a reduction of standard working hours in combination with wage subsidies) is analysed. Using a vector error correction model (VECM) for several labour market variables, as well as inflation and output, out-of-sample forecasts for 2000/2001 are produced. A comparison of these forecasts – which serve as a benchmark simulation without structural shifts – to the realized values (with shifts) suggests significant beneficial employment effects of the policy mix. Other shifts were absent and thus cannot explain the outcome. Output, productivity, hourly labour costs, and inflation are only transitorily affected or not at all.     

Innovation forecasting: A case study of the management of engineering and technology literature

The following paper contributes to the methodology of innovation forecasting. The paper analyzes the literature of engineering and technology management. A brief history and justification for interest in engineering and technology management is presented. The field has a sixty year history of interdisciplinary, and is therefore a ripe source for closer investigation into time trends of knowledge. The paper reviews the literature of innovation forecasting, examining a range of theoretical and methodological literatures interested in the evolution of knowledge. A new application of a model, suitable for sparse and count-like publication data, is presented. A mathematical presentation of the model is offered. A discussion is offered on how the model may be implemented in an approachable way within spreadsheet software. A time history of engineering management literature is extracted from a database and analyzed using the model. A projection of keyword growth is offered, and key features of the emerging knowledge base within engineering management are discussed. Recommendations for future research, as well as for those monitoring the status of the discipline of engineering management, are made.     

The forecasting approach to efficiency in the wool market

This paper develops and presents estimates of a simultaneous equations model of the Australian wool market, the world's largest producer and exporter of fine wool. The model contains functional relationships for unhedged inventories, consumption of raw wool, and the activities of both hedgers and speculators in wool futures. Expectations are represented by the adaptive hypothesis. This model extends the work of Leuthold and Hartmann (1979) and Leuthold and Garcia (1988) by including expectations in the spot-futures model, and that of Goss and Giles (1986) by including composite equations for hedger-speculators, extending the expectations hypothesis to the consumption equation, and by using the model to test the efficient markets hypothesis. Wald tests and likelihood ratio tests for unit roots in wool cash prices are conducted and in no case can the hypothesis of a single unit root be rejected. Estimation is by three stage least squares, with correction for first order serial correlation. The model provides good intra- and post-sample forecasts of most variables, especially of unhedged inventories and consumption of wool, both important spot market relationships. The model-derived forecast of the spot price is inferior to the forecast implicit in the futures price, although a compositive predictor clearly outperforms the futures price as an anticipation of subsequent cash prices. Nevertheless, it is suggested that the efficient markets hypothesis should not be rejected, because there is evidence that futures market agents are learning to use the information contained in the model.     

Outside the box: using synthetic control methods as a forecasting technique

We introduce synthetic control methods (SCM) as a forecasting technique. Using (i) as economic predictors solely the outcome itself, i.e. lagged values of the dependent variable, and (ii) lagged time series of the outcome to build the donor pool, we let SCM choose and weight appropriate values in order to come up with a sensible forecast of the US GDP growth. This procedure performs competitively viable compared with alternative forecasting methods.     

A fuzzy multiple criteria comparison of technology forecasting methods for predicting the new materials development

New materials have been recognized as key drivers for corporate profitability and growth in today's fast changing environments. To predict correctly the development of the new materials becomes a critical issue. However, little has been done in discussing the selection of technology forecasting methods for the new materials development. Accordingly, this study adopted the fuzzy AHP method to obtain professional's opinions on this issue. The efforts result in seven evaluation criteria with one, the “data validity” having the highest weight, followed by “method adaptability” and “technology predictability”. Delphi method and case study method are the two most applicable technology forecasting methods for predicting the new materials development.