Blake, A., Liu, A. and Song, H., 2018. Modelling Productivity Shocks and Economic Growth Using the Bayesian Dynamic Stochastic General Equilibrium Approach. International Journal of Contemporary Hospitality Management, 30 (11), 3229-3249.
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DOI: 10.1108/IJCHM-10-2017-0686
Abstract
Most existing studies on the impact of tourism on economic growth adopt an econometric approach that is insufficient to confirm that tourism actually leads to economic growth. Moreover, it cannot explain the causalities of different variables. Taking Mauritius as an example, this study uses the dynamic stochastic general equilibrium approach to investigate the contribution of tourism to economic growth when there is a productivity shock in the tourism sector. A two-sector, small, open economy is modelled under the Dynamic Stochastic General Equilibrium framework. The model is estimated using the Bayesian method based on real tourism and macroeconomic data from Mauritius for the period from 1999 to 2014. The impulse response functions are used to simulate the contribution of tourism to economic growth when there is a productivity shock in the tourism sector. The simulation results show that the Mauritian GDP would increase by 0.09% if the productivity of tourism improved by 1%, indicating that tourism could lead to economic growth. Considering the average annual growth rate of the Mauritian GDP, the contribution of tourism to its economic growth is significant. Furthermore, the effects of tourism on economic growth are moderated by price elasticities in international tourism demand. This is the first study that estimates the dynamic stochastic general equilibrium model using the Bayesian method. By correcting the prior information with real tourism and macroeconomic data, the estimation and simulation results are more robust compared with the calibration method, which has been used frequently in tourism studies.
Item Type: | Article |
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ISSN: | 0959-6119 |
Uncontrolled Keywords: | tourism; economic growth; dynamic stochastic general equilibrium; Bayesian Model |
Group: | Bournemouth University Business School |
ID Code: | 30428 |
Deposited By: | Symplectic RT2 |
Deposited On: | 27 Feb 2018 14:58 |
Last Modified: | 14 Mar 2022 14:09 |
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