Forecasting Bordeaux wine prices using state-space methods

Generic Bordeaux red wine (basic claret) can be regarded as being similar to an agricultural commodity. Production volumes are substantial, they are traded at high frequency and the quality of the product is relatively homogeneous. Unlike other commodities and the top-end wines (which represent only 3% of the traded volume), there is no futures market for generic Bordeaux wine. Reliable forecasts of prices can to large extent replace this information deficiency and improve the functioning of the market. We use state-space methods with monthly data to obtain a univariate forecasting model for the average price. The estimates highlight the stochastic trend and the seasonality present in the evolution of the price over the period 1999 to 2016. The model predicts the path of wine prices out of sample reasonably well, suggesting that this approach is useful for making reasonably accurate forecasts of future price movements.     

Correction to: Optimal and robust combination of forecasts via constrained optimization and shrinkage

We provide a correction to Proposition 1 in Optimal and robust combination of forecasts via constrained optimization and shrinkage, published in the International Journal of Forecasting 38(1):97-116 (2021). This correction has no impact on any other result (neither theoretical nor empirical) provided in the above paper.     

煤炭企业加强产业工人队伍建设方法的探讨

产业工人队伍在企业高质量发展中,发挥着主力军的作用。论文从产业工人队伍建设方法方面进行了探讨和研究。     

What prevents consumers that are aware of Airbnb from using the platform? A mixed methods approach

The hospitality sector is now characterised by the co-existence of traditional providers and sharing economy enterprises. It is important to better understand what prevents the use of peer-to-peer accommodation rentals in the tourism marketplace. Adopting a mixed-methods approach, this study first examines travellers’ Airbnb use constraints, and then profiles 252 Airbnb non-users based on their constraints and characteristics. The findings suggest that travellers do not use Airbnb because of distrust in the providers, in the platform and in other users, as well as perceived risk and unfamiliarity. Loss of service quality, lack of local experience, legal and regulatory issues, and disinterest also prevented consumers to book Airbnb accommodations. A cluster analysis divided non-users into three segments: Traditional travellers, Sharing economy misbelievers and Airbnb prospective users. Significant differences based on age confirmed that young travellers are more confident in using peer-to-peer accommodation platforms and more interested in the philosophy.     

Regime switching rough Heston model

This model combines two important stylized features of volatility, the rough behavior consistent with a Hurst parameter less than , and the regime switching property consistent with more long-term economic considerations. It is nevertheless highly tractable in the sense of semianalytic formulae for European options, and permits a partial Monte Carlo method of similar computational speed as the semianalytic formula (at an appropriate number of Monte Carlo simulations). While option prices are relatively insensitive to the choice of Hurst parameter, introducing rough volatility allows for a better fit to the at-the-money skew.     

The behaviour of betting and currency markets on the night of the EU referendum

We study the behaviours of the Betfair betting market and the sterling/dollar exchange rate (futures price) during 24 June 2016, the night of the EU referendum. We investigate how the two markets responded to the announcement of the voting results by employing a Bayesian updating methodology to update prior opinion about the likelihood of the final outcome of the vote. We then relate the voting model to the real-time evolution of the market-determined prices as the results were announced. We find that, although both markets appear to be inefficient in absorbing the new information contained in the vote outcomes, the betting market seems less inefficient than the FX market. The different rates of convergence to the fundamental value between the two markets lead to highly profitable arbitrage opportunities.     

Gaps in tourism education and workforce needs: attracting and educating the right people

The intense development of the tourism industry requires a skilled and qualified workforce. It is important to attract the right candidates to the tourism education, and subsequently provide relevant education to prepare students for the industry after graduation. While work placements may be useful and important to some extent, they are not the ideal solution. Hence, alternative approaches such as simulations and flipped classrooms may be applied. To provide realistic expectations, a closer collaboration between industry operators and education providers is needed. Education providers should assume a more proactive role by inviting tourism operators and integrated them in the tourism education programmes as guest lecturers.     

Exploring the use of content analysis methodology in consumer research

Content analysis is a viable way to thematise consumer experiences. The purpose of this review study is to examine the use of content analysis in consumer research (1977–2017). The authors explore how content analysis has been used. The reviewed studies address consumer experiences. The results show that qualitative content analysis is not used as a method in its own right; it is more often applied as a supplement to quantitative testing. There is also a lack of rigorous reporting of methodologies in many studies. The systematic review provides four propositions, content analysis studies: 1) vary in execution and reporting; 2) have a tendency towards methodological vagueness; 3) do not apply content analysis as a sole method; 4) are versatile. The study can also serve as point of departure for novice researchers wishing to engage with content analysis research. We suggest that further research is needed to explore the use of qualitative methodologies in consumer research.     

Self-service technology in supermarkets – Do frontline staff still matter?

This paper uses the concept of psychological distance under construal level theory to explore the differences in the customers’ evaluations of overall store quality, satisfaction and loyalty, based on their experiences with the traditional staff-checkout method and the relatively new self-checkout machines. Two empirical studies, a field survey with retail shoppers in UK (N1 = 313) and an online survey with members of a consumer panel in Australia (N2 = 474), show that the perceived quality of staff-checkout has a stronger positive impact on the overall store quality, satisfaction and loyalty, than the quality of self-checkout. Similarly, satisfaction with staff-checkout has a stronger positive effect on store satisfaction and loyalty, than the satisfaction with self-checkout. Finally, loyalty to staff-checkout also has a stronger positive influence on store loyalty, than the loyalty towards self-checkout. These results show that despite growing use of self-service technology, frontline staff continue to be important for overall store evaluations.     

Combining prediction intervals in the M4 competition

The 2018 M4 Forecasting Competition was the first M Competition to elicit prediction intervals in addition to point estimates. We take a closer look at the twenty valid interval submissions by examining the calibration and accuracy of the prediction intervals and evaluating their performances over different time horizons. Overall, the submissions fail to estimate the uncertainty properly. Importantly, we investigate the benefits of interval combination using six recently-proposed heuristics that can be applied prior to learning about the realizations of the quantities. Our results suggest that interval aggregation offers improvements in terms of both calibration and accuracy. While averaging interval endpoints maintains its practical appeal as being simple to implement and performs quite well when data sets are large, the median and the interior trimmed average are found to be robust aggregators for the prediction interval submissions across all 100,000 time series.