One model and various experts: Evaluating Dutch macroeconomic forecasts

The Netherlands Bureau for Economic Policy Analysis (CPB) uses a large macroeconomic model to create forecasts of various important macroeconomic variables. The outcomes of this model are usually filtered by experts, and it is the expert forecasts that are made available to the general public. In this paper we re-create the model forecasts for the period 1997-2008 and compare the expert forecasts with the pure model forecasts. Our key findings from the first time that this unique database has been analyzed are that (i) experts adjust upwards more often; (ii) expert adjustments are not autocorrelated, but their sizes do depend on the value of the model forecast; (iii) the CPB model forecasts are biased for a range of variables, but (iv) at the same time, the associated expert forecasts are more often unbiased; and that (v) expert forecasts are far more accurate than the model forecasts, particularly when the forecast horizon is short. In summary, the final CPB forecasts de-bias the model forecasts and lead to higher accuracies than the initial model forecasts.     

Forecasting television ratings

Despite the state of flux in media today, television remains the dominant player globally for advertising spending. Since television advertising time is purchased on the basis of projected future ratings, and ad costs have skyrocketed, there is increasingly pressure to forecast television ratings accurately. The forecasting methods that have been used in the past are not generally very reliable, and many have not been validated; also, even more distressingly, none have been tested in today’s multichannel environment. In this study we compare eight different forecasting models, ranging from a naïve empirical method to a state-of-the-art Bayesian model-averaging method. Our data come from a recent time period, namely 2004-2008, in a market with over 70 channels, making the data more typical of today’s viewing environment. The simple models that are commonly used in industry do not forecast as well as any econometric models. Furthermore, time series methods are not applicable, as many programs are broadcast only once. However, we find that a relatively straightforward random effects regression model often performs as well as more sophisticated Bayesian models in out-of-sample forecasting. Finally, we demonstrate that making improvements in ratings forecasts could save the television industry between $250 and $586 million per year.     

Kernel-based calibration diagnostics for recession and inflation probability forecasts

A probabilistic forecast is the estimated probability with which a future event will occur. One interesting feature of such forecasts is their calibration, or the match between the predicted probabilities and the actual outcome probabilities. Calibration has been evaluated in the past by grouping probability forecasts into discrete categories. We show here that we can do this without discrete groupings; the kernel estimators that we use produce efficiency gains and smooth estimated curves relating the predicted and actual probabilities. We use such estimates to evaluate the empirical evidence on the calibration error in a number of economic applications, including the prediction of recessions and inflation, using both forecasts made and stored in real time and pseudo-forecasts made using the data vintage available at the forecast date. The outcomes are evaluated using both first-release outcome measures and subsequent revised data. We find substantial evidence of incorrect calibration in professional forecasts of recessions and inflation from the SPF, as well as in real-time inflation forecasts from a variety of output gap models.     

Future memories

Although the concept of foresight is now widely used by Anglo-American writers, the Romance-language countries have continued to refer to the concept of la prospective or prospectiva since the early 1960s. Despite cultural differences, the two concepts are very similar. Nevertheless, the author argues that prospective is closer to strategic foresight. The prospective attitude does not wait for change and then react; it aims to master expected change (preactivity) and to induce a desired change (proactivity). Preactivity is what guides all approaches to future studies, forecasting, scenario planning and foresight. Proactivity is more voluntarist, and aims to bring about the desired changes by means of strategic planning. This leads to a hopeful message: We just have to rethink the problems to move forward. The author highlights the enduring relevance of several key thinkers ranging from Saint Augustine and Seneca to Gaston Berger and Igor Ansoff. He emphasizes the importance of a collectivity's thinking together about the future and taking action. Overall the article pleads for rigor yet some common sense explains the utility of participatory foresight with simple tools (morphological analysis, prospective workshops). In conclusion, this article emphasizes two symmetrical errors: ignore the existence of a hammer when in front of a nail or consider every problem a nail because you have a hammer!     

The future of the future: Strategic foresight in Latin America

With few differences in timing, future studies in Latin America were initiated with a linear conception of reality. Orchestrated with techniques such as the Delphi, future studies were supported by mathematical principles of probability. Several countries have surpassed this stage while moving into strategic foresight. Strategic foresight analyzes the future as a multiple reality. In other words, it means thinking differently and not reading reality in a linear manner. It means accepting that there is not one future but several futures and that one question could have more than one answer.This change has demanded a shift in mentality. The presence of the disciplines in universities is of vital importance. For example, the Technological Institute of Monterrey (Mexico) and the Universidad Externado de Colombia (Bogota) offer foresight and strategy programs at the graduate level.This article describes how different individuals, groups, agencies and institutions in Latin America have emerged thanks to the support provided by the science and technology governmental agencies of each country. The author provides examples from different countries including Colombia, Brazil, Chile and Mexico. Currently, strategic foresight is slowly gaining recognition and acceptance as a compass for productive sectors and as a generator of competitiveness.However, despite the contributions of strategic foresight, Latin American countries, with the exception of some of the above-mentioned countries, have struggled to take off into the future. The role of strategic foresight can become the driving force behind this awakening, if the challenge of linking global trends with local contextualization through scenarios is accepted.     

A Scenario-Based Assessment Model—SBAM

Scenario generation methods have often been used by policy makers as an instrument to manage the uncertainty of the technological environment and to support the shaping of long-term technology policies. Although scenario generation methods are conceptually useful in the shaping of long-term technology policies, the literature suggests that there is a methodological gap between the two. In an effort to bridge this gap, the authors propose a Scenario-Based Assessment Model (SBAM). This work introduces the principles and methodological basics of the SBAM. Moreover, an example is given to illustrate how this model can be applied.     

Integrating business modeling and roadmapping methods - The Innovation Support Technology (IST) approach

The purpose of this paper is to report the integration studies of business modeling and roadmapping methods for the “Innovation Support Technology (IST)” and the IST's practical application to real-world cases. The IST is conducted for the purpose of offering a convenient tool for engineers and researchers in order to enhance corporate value from R&D outputs. “Japan's lost decade” has forced companies to change R&D management and R&D operation style, especially regional industries. We propose the framework for revitalization of regional industries by using the Strategic Technology Roadmap made by the Ministry of Economy, Trade and Industry (METI-TRM) with business modeling. We applied this IST method to several real-world cases to show its effectiveness. This study represents the result of over four years (fall 2002) of work with the value creation framework of the business modeling method for R&D outputs, done by a group of researchers from JATES (Japan Techno-Economics Society).     

The role of networking in the competitiveness of firms

Two main forces characterise production systems today: on the one hand, the increasingly global nature of markets and economies has resulted in increasing competition and a new, more global division of labour. On the other, the greater complexity of technology makes innovation a key aspect in the competitiveness of manufacturing firms. The establishment of cooperative networks seems to be important in both processes. This paper aims to explore these aspects by analysing the competitiveness of firms in four different sectors of the manufacturing industry: food, chemicals, electronics and vehicles. Data have been obtained from a survey conducted specifically for this purpose at company level in Spain. Findings from the empirical analysis, based on the application of the Polytomous Logistic Universal Model (PLUM), confirm the positive effects that the ability to network has on company performance. In particular, among all the potential organisations that work as partners of the firms, the paper shows the importance of intra-firm cooperation, the user-producer relationships and the cooperation between competitors.     

Causes of inefficiency in Japanese railways: Application of DEA for managers and policymakers

Although railway services have been suffering financially due to modal shifts and aging populations, they have been, and will continue to be, an essential component of nations' basic social infrastructures. Since railway firms generate positive externalities, and are required to operate in pre-determined licensed areas, governmental intervention/support may, in some cases, be justified. Indeed, many types of subsidies are created and offered for railway operations in Japan; while some are meant to cover large investments, others are used as compensation for regional disparities. However, thus far, no attempt has been made to analyze the reasons for the underperformance of Japanese railway services. In other words, it is unclear whether this underperformance can be attributed to exogenous and uncontrollable causes, or endogenous phenomena and, hence, capable of being handled by managers. The optimal degree of intervention is thus not sufficiently known. In the current paper, we propose a method based on data envelopment analysis (DEA) to analyze the causes of inefficiency in Japanese railway operations, and, further, to calculate optimal subsidy levels. The latter are designed to compensate for railways' lack of complete discretion in changing location of their operations and/or increasing/decreasing these operations since they are a regulated service. Our proposed method was applied to 53 Japanese railway operators. In so doing, we identified several key characteristics related to their inefficiencies, and developed optimal subsidies designed to improve performance.