We consider the identification of a Markov process 
when only {Wt} is observed. In structural dynamic models, Wt includes the choice variables and observed state variables of an optimizing agent, while 
denotes time-varying serially correlated unobserved state variables (or agent-specific unobserved heterogeneity). In the non-stationary case, we show that the Markov law of motion 
is identified from five periods of data Wt+1,Wt,Wt−1,Wt−2,Wt−3. In the stationary case, only four observations Wt+1,Wt,Wt−1,Wt−2 are required. Identification of 
is a crucial input in methodologies for estimating Markovian dynamic models based on the “conditional-choice-probability (CCP)” approach pioneered by Hotz and Miller.
Centre for microdata methods and practice