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The evolution of happiness pre and peri-COVID-19: A Markov Switching Dynamic Regression Model
- Source :
- PLoS ONE, PLoS ONE, Vol 16, Iss 12 (2021)
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 2021.
-
Abstract
- Happiness levels often fluctuate from one day to the next, and an exogenous shock such as a pandemic can likely disrupt pre-existing happiness dynamics. This paper fits a Marko Switching Dynamic Regression Model (MSDR) to better understand the dynamic patterns of happiness levels before and during a pandemic. The estimated parameters from the MSDR model include each state’s mean and duration, volatility and transition probabilities. Once these parameters have been estimated, we use the one-step method to predict the unobserved states’ evolution over time. This gives us unique insights into the evolution of happiness. Furthermore, as maximising happiness is a policy priority, we determine the factors that can contribute to the probability of increasing happiness levels. We empirically test these models using New Zealand’s daily happiness data for May 2019 –November 2020. The results show that New Zealand seems to have two regimes, an unhappy and happy regime. In 2019 the happy regime dominated; thus, the probability of being unhappy in the next time period (day) occurred less frequently, whereas the opposite is true for 2020. The higher frequency of time periods with a probability of being unhappy in 2020 mostly correspond to pandemic events. Lastly, we find the factors positively and significantly related to the probability of being happy after lockdown to be jobseeker support payments and international travel. On the other hand, lack of mobility is significantly and negatively related to the probability of being happy.
- Subjects :
- Viral Diseases
Computer and Information Sciences
Markov Models
Epidemiology
Science
Happiness
Statistics as Topic
Emotions
Oceania
Twitter
Social Sciences
Geographical locations
Medical Conditions
Sociology
Diagnostic Medicine
Medicine and Health Sciences
Humans
Psychology
Pandemics
Virus Testing
Multidisciplinary
COVID-19
Biology and Life Sciences
Social Communication
Covid 19
Probability Theory
Markov Chains
Communications
Infectious Diseases
Nonlinear Dynamics
Social Networks
Physical Sciences
Medicine
Regression Analysis
People and places
Social Media
Network Analysis
Mathematics
New Zealand
Research Article
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 16
- Database :
- OpenAIRE
- Journal :
- PLOS ONE
- Accession number :
- edsair.doi.dedup.....d9f3872b96fadb1941763395d60d872f