Measuring Pattern, Amplitude and Timing Differences between Monetary and Non-Monetary Seasonal Factors of Tourism — The Case of Aruba

Seasonality is a frequent and important occurrence in the tourism industry, with concurrent effects on both the financial and volume flows of tourism. The purpose of this study is to measure pattern, amplitude and timing differences between the seasonal factors of monetary and non-monetary indicators of tourism development in Aruba. The study contributes to filling the gap in the literature on the dynamics in the co-movement of these two types of seasonal factors, with the simultaneous incorporation of three measurement dimensions of this relationship. The methodology involves decomposing time series on both stay-over tourism and tourism expenditure using the Census X-12 technique, with the subsequent calculation of Pearson's correlation coefficients, ratios of amplitudes and timing differentials of peaks and troughs. The results show important differences in the pattern, amplitude and timing of the seasonal factors

What drives local government spending in Spain? A dynamic spatial panel approach

What drives local government spending in Spain? A dynamic spatial panel approach. Spatial Economic Analysis. This paper extends traditional spatial spillover models of government spending by including dynamic effects and exogenous interaction effects. Using annual data for a sample of 3032 Spanish municipalities during 2000-12, we estimate a dynamic spatial Durbin panel data model to quantify the relevance of spatial spillovers and diffusion effects over time as well as the impact of a variety of spending determinants. We find that government spending at the local level is mainly explained by economic factors, while demographic factors and political factors appear to be less relevant

Stability and Chaos in Input Pricing for a Service Facility with Adaptive Customer Response to Congestion

We consider the stability of the equilibrium arrival rate and equilibrium admission price at a service facility, using a generalization of an input-pricing model introduced by Dewan and Mendelson and further examined by Stidham. At the equilibrium, the marginal value of service equals the admission price, that is, the sum of the admission fee and the expected delay cost. Stability means (roughly) that the system returns to the equilibrium after a perturbation, assuming the customers base their join/balk decisions on previous prices. We extend the discrete-time, dynamic-system pricing model of Stidham to allow adaptive expectations in which customers predict the future price based on a convex combination of the current price and the previous prediction. We show that this can lead to chaotic behavior when the equilibrium is unstable. That is, the price and arrival rate can follow aperiodic orbits, which appear to be completely random. Our results suggest an alternative explanation for observed variations in the mean arrival rate to a queueing system, which are often modeled by means of a random exogenous (e.g., Markovian) environment process.Optimal Pricing, Service Facility, Optimal Design of Queues, Stable Equilibrium, Chaos