The impact of low cost carriers on Korean Island tourism

Given the mixed findings of extant research on the impact of low-cost carriers (hereafter LCC) on aviation markets (with some studies showing stimulation of new demand, other studies showing LCCs encroaching on the turf of full-service carriers), the emergence of LCCs in Korea raised an interesting question as to whether or not they actually contribute to overall growth in domestic air traffic. The literature has paid limited attention to this issue so far. Employing a multivariate regression analysis with monthly data from 2000 to 2009, the impact of LCCs on tourism demand to a popular destination in Korea, Jeju Island, is examined, focusing on two specific questions: Have LCCs generated new tourism demand and brought more tourism revenue into the island’s economy? Have LCCs mitigated tourism seasonality on the island? Controlling for the effects of a number of factors, results showed that LCCs have generated new demand in addition to existing tourist flows to the island. Korean LCCs accounted for 35% of total passengers in 2009, which indicates an average growth rate of 161.7% over the last 4 years, compared to a −0.3% growth rate for all full-service carriers in Korea. However, LCCs seem to have had little impact on reducing seasonal fluctuations in passenger traffic to Jeju Island. The findings of this study will be of interest to researchers, policy makers, and a variety of stakeholders in the tourism industry interested in the relationship between no-frills airlines and island economies.     

Benchmarking by State Space Models

We have a monthly series of observations which are obtained from sample surveys and are therefore subject to survey errors. We also have a series of annual values, called benchmarks, which are either exact or are substantially more accurate than the survey observations; these can be either annual totals or accurate values of the underlying variable at a particular month. The benchmarking problem is the problem of adjusting the monthly series to be consistent with the annual values. We provide two solutions to this problem. The first of these is a two-stage method in which we first fit a state space model to the monthly data alone and then combine the results obtained at this stage with the benchmark data. In the second solution we construct a single series from the monthly and annual values together and fit a state space model to this series in a single stage. The treatment is extended to series which behave multiplicatively. The methods are illustrated by applying them to Canadian retail sales sereis.     

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.     

The Optimism Cycle: Sell in May

Summary  On average, stocks deliver close to zero returns from May through October. We hypothesize that this seasonal pattern is caused by an optimism cycle. With year end approaching, investors start to look towards next year, often with overly optimistic expectations. Several months into the year, the initial optimism becomes hard to maintain and the stock market experiences a summer lull. A global sector-rotation strategy based on this theory appears to be highly profitable. Global earnings growth revisions follow a seasonal pattern parallel to that of the stock market. Investors’ optimism as measured by the initial returns on IPOs almost completely captures the results of the sector-rotation strategy in a separate analysis for the US stock market. All these findings support the optimism-cycle hypothesis. The author is grateful to Steef Bergakker for all the useful discussions about market dynamics through the years, which have contributed to the theory that an optimism cycle might exist. Special thanks go to Marno Verbeek for his very helpful suggestions. This paper also benefited from the remarks and suggestions from two anonymous referees.     

Forecasting monthly riverflow time series

Mean monthly flows from thirty rivers in North and South America are used to test the short-term forecasting ability of seasonal ARIMA, deseasonalized ARMA, and periodic autoregressive models. The series were split into two sections and models were calibrated to the first portion of the data. The models were then used to generate one-step-ahead forecasts for the second portion of the data. The forecast performance is compared using various measures of accuracy. The results suggest that a periodic autoregressive model, identified by using the partial autocorrelation function, provided the most accurate forecasts