Determinants of transition in artificially discrete Markov chains using microdata

- An econometric procedure to model transitions in Markov chains is proposed.
- The model is applicable when the continuous classification variable is observed.
- Transition probabilities, marginal effects and discrete changes are calculated.
- The model might be useful in a number of situations and in several disciplines.

We describe an econometric procedure to model transitions in Markov chains whose state space is finite and classification stems from observed continuous variables. We show how stationary and non-stationary transition probabilities as well as the marginal effects of continuous and dichotomous variables determining transition can be estimated. The model resembles the ordered probit approach used in Epstein et al. (2006) but allows for the differences in the nature of the dependent variable and suggests some very important extensions pertaining to more meaningful representation of parameter estimates and the simultaneous construction of transition matrices.