On the Use of Expectations Data in Estimating Structural Dynamic Choice Models

2012 ◽  
Vol 30 (3) ◽  
pp. 521-554 ◽  
Author(s):  
Wilbert van der Klaauw
Keyword(s):  
Choice Models ◽  
Structural Dynamic ◽  
Dynamic Choice ◽  
Dynamic Choice Models

scholarly journals The endogenous grid method for discrete-continuous dynamic choice models with (or without) taste shocks

10.3982/qe643 ◽  
2017 ◽  
Vol 8 (2) ◽  
pp. 317-365 ◽  
Author(s):  
Fedor Iskhakov ◽  
Thomas H. Jørgensen ◽  
John Rust ◽  
Bertel Schjerning
Keyword(s):  
Grid Method ◽  
Choice Models ◽  
Dynamic Choice ◽  
Continuous Dynamic ◽  
Dynamic Choice Models

Multivariate extremal processes, leader processes and dynamic choice models

1990 ◽  
Vol 22 (2) ◽  
pp. 309-331 ◽  
Author(s):  
Sidney Resnick ◽  
Rishin Roy
Keyword(s):  
Transition Probabilities ◽  
Homogeneous Case ◽  
Dynamic Choice ◽  
Underlying Distribution ◽  
Extremal Process ◽  
Time Dependent Behaviour ◽  
Stationary Transition ◽  
Dynamic Choice Models ◽  
Dependent Behaviour ◽  
Leader Process

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.

Multivariate extremal processes, leader processes and dynamic choice models

1990 ◽  
Vol 22 (02) ◽  
pp. 309-331 ◽  
Author(s):  
Sidney Resnick ◽  
Rishin Roy
Keyword(s):  
Transition Probabilities ◽  
Homogeneous Case ◽  
Dynamic Choice ◽  
Underlying Distribution ◽  
Extremal Process ◽  
Time Dependent Behaviour ◽  
Stationary Transition ◽  
Dynamic Choice Models ◽  
Dependent Behaviour ◽  
Leader Process

Let ( Y (t), t &gt; 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&lt;s≦t} and Z(t) are independent and when J(t) and {Z(s), 0&lt;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.

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