National Parks are significant markers in the tourism attraction system and represent an important supply of recreation opportunities for the clients of the nature-based tourism industry. In this study, we analyze income elasticities among visitors from two major nationalities at Fulufjället National Park (FNP)—a cross-boundary park between Sweden and Norway—to see if this tourism product is a luxury or not. Modeling demand with a Tobit model, we find that visiting this National Park is close to a luxury, but results also show that elasticities differ across both income and nationality: FNP is more likely to be a luxury good among low-income Germans and high-income Swedes. The article concludes with a discussion on policy and management implications from these results.
Abstract. A couple a years ago William H. Greene introduced the so called 'True fixed e↵ects estimator' (TFE), which is intended to separate between heterogeneity and e ciency in stochastic frontier analysis. We would say that it has had huge impact on applied stochastic frontier analysis. One problem with the original TFE estimator, is that it is biased in cases with finite time observations. For the normal-half-normal model this problem was solved by Chen et al. (2014) based on maximum likelihood estimation of the within-transformed model. In this study we show the possibilities with method of moment estimation. This approach is more straightforward computational and is more flexible than maximum likelihood estimation since the estimators are not as dependent on the distributional assumptions and do not hinges on an explicit distribution of the random error. We only assume symmetry and for more complicated models also a fixed fourth order cumulant.
Abstract. Abstract. We consider a method-of-moments fixed effects (FE) estimator of technical inefficiency. When dealing with a large number of cross-sectional observations, N , it is possible to obtain consistent central moments of the population distribution of the inefficiencies. It is well known that the classical FE estimator may be seriously upward biased when N is large and T , the number of time observations, is small. Based on the second central moment and assuming a single-parameter distribution of inefficiencies, we obtain unbiased technical inefficiencies in large-N settings. The proposed methodology bridges classical FE and maximum likelihood estimation, leading to a reduction in bias without making an assumption about random effects.
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