scholarly journals Bayesian Inference for Dynamic Choice Models Without the Need for Dynamic Programming

1996 ◽  
Author(s):  
John Geweke ◽  
Michael P. Keane
10.3982/qe643 ◽  
2017 ◽  
Vol 8 (2) ◽  
pp. 317-365 ◽  
Author(s):  
Fedor Iskhakov ◽  
Thomas H. Jørgensen ◽  
John Rust ◽  
Bertel Schjerning

1990 ◽  
Vol 22 (2) ◽  
pp. 309-331 ◽  
Author(s):  
Sidney Resnick ◽  
Rishin Roy

Let (Y(t), t > 0) be a d-dimensional non-homogeneous multivariate extremal process. We suppose the ith component of Y describes time-dependent behaviour of random utilities associated with the ith choice. At time t we choose the ith alternative if the ith component of Y(t) is the largest of all the components. Let J(t) be the index of the largest component at time t so J has range {1, …, d} and call {J(t)} the leader process. Let Z(t) be the value of the largest component at time t. Then the bivariate process (J(t), Z(t)} is Markov. We discuss when J(t) and Z(t) are independent, when {J(s), 0<s≦t} and Z(t) are independent and when J(t) and {Z(s), 0<s≦t} are independent. In usual circumstances, {J(t)} is Markov and particular properties are given when the underlying distribution is max-stable. In the max-stable time-homogeneous case, {J(et)} is a stationary Markov chain with stationary transition probabilities.


Econometrica ◽  
1979 ◽  
Vol 47 (1) ◽  
pp. 91 ◽  
Author(s):  
David M. Kreps ◽  
Evan L. Porteus

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