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Approximating High-Dimensional Dynamic Models: Sieve Value Function Iteration. (2012). Bugni, Federico ; Bayer, Patrick ; Arcidiacono, Peter ; James, Jonathan.
In: NBER Working Papers.
RePEc:nbr:nberwo:17890.

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  1. Effects of Parental Leave Policies on Female Career and Fertility Choices. (2016). Yamaguchi, Shintaro.
    In: Department of Economics Working Papers.
    RePEc:mcm:deptwp:2016-10.

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  2. The costs of free entry: an empirical study of real estate agents in Greater Boston. (2015). Pathak, Parag ; Barwick, Panle.
    In: RAND Journal of Economics.
    RePEc:bla:randje:v:46:y:2015:i:1:p:103-145.

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  3. Market-Based Emissions Regulation and Industry Dynamics. (2012). Ryan, Stephen ; Reguant, Mar ; Fowlie, Meredith.
    In: NBER Working Papers.
    RePEc:nbr:nberwo:18645.

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References

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  5. Arcidiacono, P., P. Bayer, F. Bugni, and J. James (2011): “Sieve Value Function Iteration for Large State Space Dynamic Games’,” Mimeo: Duke University. B
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  7. Chen, X. (2007): “Large Sample Sieve Estimation of Semi-Nonparametric Models,” vol. 6 of Handbook of Econometrics.

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  11. It is straight-forward to verify that it is a contraction mapping (in the sup-norm) by using the Blackwell (1965) sufficient conditions for a contraction (see Stokey and Lucas (1989), Theorem 3.3). This is an obvious object of interest, as it allows us to deduce optimal decision rules in the following way: f(x, ε) = arg max a∈A(x) {u(x, a) + ε(a) + βE[V (x , ε )|x, ε, a]}, (B.4) with a related conditional choice probability (CCP): P(a = a|x) = ε arg max a∈A(x) {u(x, a) + ε(a) + βE[V (x , ε )|x, ε, a]} = a dP(ε|x). (B.5) We now verify that this formulation is a particular case of the unified formulation. This is shown in
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  12. Keane, M. P., and K. I. Wolpin (1994): “The Solution and Estimation of Discrete Choice Dynamic Programming Models by Simulation and Interpolation: Monte Carlo Evidence,” The Review of Economics and Statistics, 76(4), 648–672.

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    In: NBER Working Papers.
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