Progress and prospects for event tourism research

This paper examines event tourism as a field of study and area of professional practice updating the previous review article published in 2008. In this substantially extended review, a deeper analysis of the field’s evolution and development is presented, charting the growth of the literature, focusing both chronologically and thematically. A framework for understanding and creating knowledge about events and tourism is presented, forming the basis which signposts established research themes and concepts and outlines future directions for research. In addition, the review article focuses on constraining and propelling forces, ontological advances, contributions from key journals, and emerging themes and issues. It also presents a roadmap for research activity in event tourism

An Integer-Valued Time Series Model for Hotels that Accounts for Constrained Capacity

Many service industry firms strive hard to fill free capacity in order to cover their costs for a fixed capital stock. This paper presents a time series model where the capacity constraint is an integral part. The integer-valued autoregressive model builds on a simple idea of how daily time series arise for hotels and other similar establishments. Measures that follow naturally from the time series model are the occupancy probability and the duration of stay for the visitor. Empirically, we study the effects of price changes and a large festival, on these measures.

Conditional skewness modelling for stock returns

Two approaches to modelling conditional skewness in a nonlinear model for stock returns are studied. It is found that a normal distribution can be rejected. A log-generalized gamma distribution with one time-varying density parameter, and a Pearson IV specification with three parameters are better supported by data. While the log-generalized gamma indicates that time-varying skewness is an important feature of the daily composite returns of NYSE, the Pearson IV model suggests that excess kurtosis rather than skewness should be accounted for.

Discretized time and conditional duration modelling for stock transaction data

This article considers conditional duration models in which durations are in continuous time, but measured in grouped or discretized form. This feature of recorded durations in combination with a frequently traded stock is expected to negatively influence the performance of conventional estimators for intra-day duration models. A few estimators that account for the discreteness are discussed and compared in a Monte Carlo experiment. An EM-algorithm accounting for the discrete data performs better than those that do not. Empirical results are reported for trading durations in Ericsson B at Stockholmsborsen for a 3-week period of July 2002. The incorporation of level variables for past trading is rejected in favour of change variables. This enables an interpretation in terms of news effects. No evidence of asymmetric responses to news about prices and spreads is found.