  A novel copula-based multivariate panel ordinal model is developed to
estimate structural relations among components of well-being. Each ordinal
time-series is modelled using a copula-based Markov model to relate the
marginal distributions of the response at each time of observation and then, at
each observation time, the conditional distributions of each ordinal
time-series are joined using a multivariate t copula. Maximum simulated
likelihood based on evaluating the multidimensional integrals of the likelihood
with randomized quasi Monte Carlo methods is used for the estimation.
Asymptotic calculations show that our method is nearly as efficient as maximum
likelihood for fully specified multivariate copula models. Our findings
highlight the importance of one's relative position in evaluating their
well-being with no direct effects of socio-economic characteristics on
well-being but strong indirect effects through their impact on components of
well-being. Temporal resilience, habit formation and behavioural traits can
explain the dependence in the joint tails over time and across well-being
components.
