Abstract
This chapter reviews vector autoregressive (VAR) modelling and its applications in tourism demand research. Since the 1980s, VAR models have been a popular tool in macroeconomic analysis, in which endogeneity is of particular concern. This chapter revisits the classic VAR model. Then it introduces a recent advancement called the global VAR (GVAR) model, which is well suited to modelling large high-dimensional systems with multiple cross-sections. In addition, this chapter touches on the Bayesian approaches to VAR modelling. In the context of tourism demand research, VAR models can be used to capture the interrelations between tourism variables and economic variables and to simulate impulse responses to economic shocks. Using global tourism demand data for 24 major economies, this chapter demonstrates the applications of the classic VAR, GVAR, Bayesian VAR (BVAR) and Bayesian GVAR (BGVAR) models and compares their forecast accuracy with the accuracy of commonly used univariate time series models.
| Original language | English |
|---|---|
| Title of host publication | Econometric Modelling and Forecasting of Tourism Demand |
| Subtitle of host publication | Methods and Applications |
| Editors | Doris Chenguang Wu, Gang Li, Haiyan Song |
| Chapter | 5 |
| Pages | 95-125 |
| Number of pages | 31 |
| ISBN (Electronic) | 9781003269366 |
| DOIs | |
| Publication status | Published - 27 Oct 2022 |
Bibliographical note
This is an Accepted Manuscript of a book chapter published by Routledge/CRC Press in Econometric Modelling and Forecasting of Tourism Demand: Methods and Applications on [27/10/2022], available online: http://www.routledge.com/9781003269366Keywords
- Vector autoregressive models
- Global VAR
- Bayesian VAR
- Forecasting
- Tourism demand