Comparison of hypothesis testing techniques for Markov processes estimated from micro versus macro data

The authors estimate the parameters of a Markov chain model using two types of simulated data: micro, or actual interstate transition counts, and macro aggregate frequency. They compare, by means of Monte Carlo experiments, the validity and power for micro likelihood ratio tests with their macro counterparts, previously developed by the authors to complement standard least-squares point estimates. They consider five specific null hypotheses, including parameter stationarity, entity homogeneity, a zero-order process, a specified probability value, and equal diagonal probabilities. The results from these micro-macro comparisons should help to indicate whether micro panel data collection is justified over the use of simpler state frequency counts.