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Matlab package for learning to specify, compute, and estimate dynamic discrete choice models

License: MIT License

MATLAB 100.00%
dynamic-discrete-choice ddc estimation maximum-likelihood nested-fixed-point-method nfxp mpec firm-dynamics matlab exercises

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dynamic-discrete-choice's Issues

Revisions

  • Update reference Iskhakov et al. (2015)

new question(s) on static vs dynamic models

  • idea: compare U1-U0 to u1-u0; if they don't differ much then estimating a static model should be fine too, and the inner loop tolerance should not matter much
  • for the current parameter values the difference is small
  • AP: try to find parameter values such that the difference is bigger (call this parameters 2)
  • ask students to assess dependence of importance of inner loop tolerance on parameter values
  • ask students to estimate a static model and check when this gives good estimates

CE5

The following Item in CE5

\item Extend |negLogLik| (or add functions) so that it optionally takes nonparametric estimates of $\Delta U$ as inputs, computes the corresponding estimates of the entry and exit rules, and uses these estimates and the model to forwardly simulate $U_0(x,a)$ and $U_1(x,a)$, and then $\Delta U(x,a)$, for each point $(x,a)$ in the sample. As possible objectives to be minimized, both implement a weighted distance between the nonparametric estimates of $\Delta U$ and the simulated values of $\Delta U$, and minus a log pseudo-likelihood based on the choice probabilities implied by the simulated values of $\Delta U$.

incorrectly suggests that forward simulations should be rerun for each trial parameter value. We should instruct students to code up the forward simulations and value evaluation separately instead.

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