Forecasting tourist arrivals using time-varying parameter structural time series models

Empirical evidence has shown that seasonal patterns of tourism demand and the effects of various influencing factors on this demand tend to change over time. To forecast future tourism demand accurately requires appropriate modelling of these changes. Based on the structural time series model (STSM) and the time-varying parameter (TVP) regression approach, this study develops the causal STSM further by introducing TVP estimation of the explanatory variable coefficients, and therefore combines the merits of the STSM and TVP models. This new model, the TVP-STSM, is employed for modelling and forecasting quarterly tourist arrivals to Hong Kong from four key source markets: China, South Korea, the UK and the USA. The empirical results show that the TVP-STSM outperforms all seven competitors, including the basic and causal STSMs and the TVP model for one- to four-quarter-ahead ex post forecasts and one-quarter-ahead ex ante 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.     

Group-based forecasting?: A social psychological analysis

The potential of group (vs. individual) forecasting is analyzed from the perspective of the social psychology of groups. The social decision scheme theory (SDST) is summarized, and several simulations are presented to demonstrate the dependence of group aggregation accuracy upon factors such as group size, the accuracy and distribution of individual forecasts, and shared representations of the forecasting problem. Many advantages and disadvantages of group aggregation are identified and related to four generic methods of group aggregation (statistical aggregation, prediction markets, the Delphi method, and face-to-face discussion). A number of aspects of forecasting problems are identified which should govern whether or not group forecasting can be relied upon, and if so, what aggregation method should be used.     

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.     

Forecasting elections in Turkey

This paper proposes a model for forecasting elections in Turkey. In doing so, this study is based on three theoretical premises: first, that the voters reward or punish parties according to their performances relative to the macroeconomic conditions; second, that the popularity of the political parties in Turkey are closely connected to their performances in local elections; and third, that the centre-periphery distinction affects the fortunes of the political parties in Turkey. The contribution of this analysis is the introduction of an explicit model on which can forecast the impact of economic and political variables on the elections in Turkey by using reliable, public and macro level data. Our findings show that the dynamics of the evaluation of political parties in Turkey follow a similar pattern to those of contemporary democracies, being driven by both economic and political factors.

“…why did AKP win? There cannot be a scientific and sociological explanation of this.”Özdemir ?nce, 17 August 2007, Hürriyet, emphasis added.

     

Quality assessment in a turbulent environment: The case of the stockfish industry

Quality assessment is frequently applied in business-to-business marketing. It is usually assumed that quality assessment builds on the use of “objective” rules and criteria, but this is not always true. In a longitudinal study it was found that an institutionalized practice of quality assessment was based on both “objective” product characteristics, as well as more subjective supply/demand considerations, making quality assessment much more complex than usually assumed. Theoretical and managerial implications are highlighted.     

The performance effect of organizational learning and market orientation

In recent decades an important set of articles on management has focused upon the marketing concept and the related construct of market orientation. The next challenge is to understand how this organizational orientation can be achieved and maintained. Using data from 451 companies and through structural equation models, this study considers that organizational learning represents the capacity of a company to move from a given situation to another desired situation of market orientation and performance. The results suggest that the influence of market orientation on performance is only significant when it is mediated by organizational learning. The results also indicate that organizational learning has a positive effect on performance.     

Does cooperation absorb complexity? Innovation networks and the speed and spread of complex technological innovation

A new technological paradigm which rewards cooperation in the innovation of complex technologies seems to have emerged in recent years. Global reach and greater innovation speed are said to be key benefits of network-based complex innovation. By bringing together multiple sources of knowledge and experience, networks of innovative firms and other organizations increasingly appear to be able to absorb the combination of spatial and temporal uncertainty. But what is the empirical evidence underpinning this new paradigm? Beyond case studies and the experience of individual researchers, what do we know about cooperation and the pace and place of complex innovations? Examination of available empirical research fails to confirm the theory that cooperation enhances either the globalization of innovation or its speed.     

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.     

A study of the motivations for the environmental transformation of companies

This paper studies the relationship between the environmental motivations or beliefs prevailing in a company and the kind of environmental transformation the company is undertaking. In this sense, we distinguish four types of motivations: ethical, productive, commercial, and relational; and three areas where environmental transformation is typically conducted: the management system, the operations system, and the commercial system. The links between all these elements are analysed over a sample of 186 manufacturing companies from three industrial sectors. The results reveal that each environmental transformation responds to the presence or the predominance of certain motivations or environmental beliefs within the company.