2019
DOI: 10.21144/wp19-15
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Efficient Computation with Taste Shocks

Abstract: Taste shocks result in nondegenerate choice probabilities, smooth policy functions, continuous demand correspondences, and reduced computational errors. They also cause significant computational cost when the number of choices is large. However, I show that, in many economic models, a numerically equivalent approximation may be obtained extremely efficiently. If the objective function has increasing differences (a condition closely tied to policy function monotonicity) or is concave in a discrete sense, the pr… Show more

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Cited by 14 publications
(19 citation statements)
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“…In this appendix, we first augment the original problem with taste shocks on borrowing B , following Dvorkin, Sanchez, Sapriza, and Yurdagul (2018) and Gordon (2019). We then describe our computation algorithm, which works backwards for value function and policy functions.…”
Section: A Definition Of Epidemic Equilibriummentioning
confidence: 99%
“…In this appendix, we first augment the original problem with taste shocks on borrowing B , following Dvorkin, Sanchez, Sapriza, and Yurdagul (2018) and Gordon (2019). We then describe our computation algorithm, which works backwards for value function and policy functions.…”
Section: A Definition Of Epidemic Equilibriummentioning
confidence: 99%
“…For the calibration and simulation of the model we abstract from monetary shocks, but we incorporate unexpected (zero probability) monetary shocks to analyze their associated impulse response functions and to construct counterfactual monetary policy for our Brazil event study. For numerical stability purposes we augment the model with taste shocks, in the discrete choice tradition, following Dvorkin et al (2018) and Gordon (2018). Appendix A details the structure of these shocks, their numerical properties, and their role in robust convergence of models with long-term debt, such as ours.…”
Section: Parameterizationmentioning
confidence: 99%
“…In this alternative solution technique, we modify the approach of Dvorkin, Sánchez, Sapriza, and Yurdagul (2019), who introduce a vector of i.i.d. shocks, drawn from a generalized extreme value distribution, in a way that avoids the need for an additional state variable (see also Gordon, 2019). In Table 17, we compare the resulting statistics from the two different solution methods for our benchmark model.…”
Section: A7 Numerical Solution Methodsmentioning
confidence: 99%