"Facebook Likes" in Tourism Demand Forecasting

16.09.2020

WIFO Research Seminar with Ulrich Gunter (Modul University Vienna)

Ulrich Gunter from Modul University Vienna spoke at the WIFO Research Seminar on 15 September 2020 about the predictive ability of "Facebook likes" in tourism demand forecasting. WIFO economist Oliver Fritz contributed a commentary.

Please find the slides of the presentation here.

Further information about Ulrich Gunter is available here.
 

Event

WIFO Research Seminar, 15.9.2020 13:30
Commentary: Oliver Fritz (WIFO)
Please send – up to one day before the event – an informal registration to veranstaltungen@wifo.ac.at, if you are interested in attending this lecture.
For online participation: GoToMeeting link will be submitted after registration (veranstaltungen@wifo.ac.at).
Organised by: Austrian Institute of Economic Research
Using data for the period 2010M06-2017M02, this study investigates the possibility of predicting total tourist arrivals to four Austrian cities (Graz, Innsbruck, Salzburg, and Vienna) from LIKES of posts on the Facebook pages of the destination management organisations of these cities. Google Trends data are also incorporated in investigating whether forecast models with LIKES and/or with Google Trends deliver more accurate forecasts. To capture the dynamics in the data, the autoregressive distributed lag (ADL) model class is employed. Taking into account the daily frequency of the original LIKES data, the mixed data sampling (MIDAS) model class is employed as well. While time-series benchmarks from the naive, error-trend-seasonal, and autoregressive moving average model classes perform best for Graz and Innsbruck across forecast horizons and forecast accuracy measures, ADL models incorporating only LIKES or both LIKES and Google Trends generally outperform their competitors for Salzburg. For Vienna, the MIDAS model including both LIKES and Google Trends produces the smallest forecast accuracy measure values for most forecast horizons.
Please contact

Oliver Fritz

Research groups: Structural Change and Regional Development
© Artem Beliaikin/Unsplash
© Artem Beliaikin/Unsplash