| By: |
Andrea Saayman and Ilse Botha |
| Abstract: |
Quantitative methods to forecasting tourist arrivals can be sub-divided into
causal methods and non-causal methods. Non-causal time series methods remain
popular tourism forecasting tools due to the accuracy of their forecasting
ability and general ease of use. Since tourist arrivals exhibit seasonality,
SARIMA models are often found to be the most accurate. However, these models
assume that the time-series is linear. This paper compares the baseline
seasonal Naïve and SARIMA forecasts of a seasonal tourist destination faced
with a structural break in the data, with alternative non-linear methods, with
the aim to determine the accuracy of the various methods. These methods
include the unobserved components model, smooth transition autoregressive
model (STAR) and singular spectrum analysis (SSA). The results show that the
non-linear forecasts outperform the other methods. The linear methods show
some superiority in short-term forecasts when there are no structural changes
in the time series. |
| Keywords: |
forecasting, tourism demand, SARIMA, STAR, Spectrum analysis, basic structural model (BSM) |
| Date: |
2015 |
| URL: |
http://d.repec.org/n?u=RePEc:rza:wpaper:492&r=tur |