MAMATZAKIS, emmanuel and MAMATZAKIS, E (2022): Understanding the impact of travel on wellbeing: evidence for Great Britain during the pandemic.
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Abstract
This study examines the impact of the COVID-19 on the wellbeing of individuals in Great Britain, as measured by life satisfaction and happiness, by analysing the dramatic drop in travel during this time. The Bayesian VAR model considers a range of exogenous and endogenous variables, including COVID-19, modes of transportation, and wellbeing variables. Results indicate that shocks in COVID-19 have a negative impact on travel, which subsequently affects wellbeing. However, there is limited evidence to suggest that COVID-19 responses to shocks in various forms of transportation have a significant impact on COVID-19 outcomes. Additionally, the study provides forecasts for key endogenous variables, which can inform evidence-based policymaking during the pandemic. The study emphasizes the importance of considering the relationship between travel and wellbeing amidst the pandemic and highlights the need for policies that balance the public health risks of travelling with the benefits of mobility and travel for wellbeing.
Item Type: | MPRA Paper |
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Original Title: | Understanding the impact of travel on wellbeing: evidence for Great Britain during the pandemic. |
Language: | English |
Keywords: | Wellbeing; Travel in Great Britain; Covid 19; Bayesian VAR. |
Subjects: | I - Health, Education, and Welfare > I0 - General M - Business Administration and Business Economics ; Marketing ; Accounting ; Personnel Economics > M0 - General Z - Other Special Topics > Z0 - General |
Item ID: | 121782 |
Depositing User: | Professor Emmanuel Mamatzakis |
Date Deposited: | 22 Aug 2024 19:17 |
Last Modified: | 22 Aug 2024 19:17 |
References: | Anyu Liu, Yoo Ri Kim, John Frankie O'Connell, (2021). COVID-19 and the aviation industry: The interrelationship between the spread of the COVID-19 pandemic and the frequency of flights on the EU market, Annals of Tourism Research, Volume 91. Banbura, M., D. Giannone, and L. Reichlin. 2008. Large Bayesian VARs. Working Paper Series 966, European Central Bank. Dieppe, A., R. Legrand, and B. van Roye. 2016. The BEAR toolbox. Working Paper Series 1934, European Central Bank. https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1934.en.pdf. Doan, T., R. B. Litterman, and C. Sims. 1984. Forecasting and conditional projection using realistic prior distributions. Econometric Reviews 1: 1–100. Filep, S. and M. Deery, (2010). Towards a picture of tourists' happiness, Tourism Analysis, 15 (4), pp. 399-410. Gilbert, D. and J. Abdullah (2004). Holiday taking and the sense of well-being, Annals of Tourism Research, 31 (1) (2004), pp. 103-121. Ghysels, E., Santa-Clara, P., Valkanov, R. (2004). The MIDAS Touch: Mixed Data Sampling Regression Models. Ghysels, E., P. Santa-Clara, and R. Valkanov (2006) Predicting volatility: getting the most out of return data sampled at different frequencies, Journal of Econometrics, 131, 59–95. Ghysels, E., and J. Wright (2009). Forecasting professional forecasters, Journal of Business and Economic Statistics, 27, 504–516. Hornik, K., M. Stinchcombe, H. White (1989). Multi-layer feedforward networks are universal approximators, Neural Networks 2, 359–366. ICAO (2022). 2021 global air passenger totals show improvement from 2020, but still only half pre-pandemic levels. ICAO, News Release, March. Kadiyala, K. R., and S. Karlsson. 1997. Numerical methods for estimation and inference in Bayesian VAR-models. Journal of Applied Econometrics 12: 99–132. Karlsson, S. 2013. Forecasting with Bayesian vector autoregression. In Handbook of Economic Forecasting, vol. 2B, ed. G. Elliott and A. Timmermann, 791–897. Amsterdam: North-Holland. https://doi.org/10.1016/B978-0-444-62731- 5.00015-4. Kock, F., Assaf, A. G., Tsionas, M. G. (2020). Developing Courageous Research Ideas. Journal of Travel Research 59:1140–46. Kock, F., Nørfelt, A., Josiassen, A., Assaf, A. G., Tsionas, M. G. (2020). Understanding the COVID-19 Tourist Psyche: The Evolutionary Tourism Paradigm. Annals of Tourism Research 85:103053. Kwon, Jangwook and Hoon Lee, (2020). Why travel prolongs happiness: Longitudinal analysis using a latent growth model, Tourism Management, Volume 76. Litterman, R. B. 1980. A Bayesian procedure for forecasting with vector autoregressions. MIT working paper, Massachusetts Institute of Technology. 1984. Lutkepohl, H. 2005. ¨ New Introduction to Multiple Time Series Analysis. New York: Springer. Oh, Dong Hwan and Patton, Andrew J., Better the Devil You Know: Improved Forecasts from Imperfect Models (August 31, 2021). Available at SSRN: Sun, X., S. Wandelt, A. Zhang. How dis COVID-19 impact air transportation? A first peek through the lens of complex networks. Journal of Air Transport Management, 89 (2020), Article 101928. UNTWO (2020). Impact assessment of the COVID-19 outbreak on international tourism Available at https://www.unwto.org/impact-assessment-of-the-covid-19-outbreak-on-international-tourism. White, H. (1989). Learning in artificial neural networks: A statistical perspective. Neural Computation, 1, 425–464. White, H. (1990). Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappings, Neural Networks 3, 535–549. Zabai Anna, (2020). How are household finances holding up against the Covid-19 shock? BIS Bulletin, No 22, June. Zellner, A. (1971). Introduction to Bayesian inference in econometrics. Wiley, New York. Zenker, S., Kock, F. (2020). The Coronavirus Pandemic—A Critical Discussion of a Tourism Research Agenda. Tourism Management 81:104164. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/121782 |