K
Kenneth L. Judd
Researcher at Stanford University
Publications - 197
Citations - 16480
Kenneth L. Judd is an academic researcher from Stanford University. The author has contributed to research in topics: General equilibrium theory & Dynamic programming. The author has an hindex of 50, co-authored 197 publications receiving 15931 citations. Previous affiliations of Kenneth L. Judd include Saint Petersburg State University & National Bureau of Economic Research.
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Solving Large Scale Rational Expectations Models
TL;DR: In this article, the authors explore alternative approaches to numerical solutions of large rational expectations models, focussing on the tradeoffs in accuracy, space, and speed, and show that these methods are capable of analyzing moderately large models even when they use only elementary, general purpose numerical methods.
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An alternative to steady-state comparisons in perfect foresight models
TL;DR: In this paper, a technique for the computation of the welfare impact of a pertubation of the steady state in a typical perfect foresight model is presented, where the major innovation is the ability to analyze non-stationary perturbations.
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Solving Large Scale Rational Expectations Models
Jess Gaspar,Kenneth L. Judd +1 more
TL;DR: In this article, the authors explore alternative approaches to numerical solutions of large rational-expectations models, focusing on the trade-offs in accuracy, space, and speed, and show that these methods are capable of analyzing moderately large models even when they use only elementary, general-purpose numerical methods.
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Capital-Income Taxation with Imperfect Competition
TL;DR: In this article, the authors argue that imperfect competition is an essential feature of innovation and growth in the "new economy" and that it has particularly striking implications for the taxation of capital.
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Smolyak Method for Solving Dynamic Economic Models: Lagrange Interpolation, Anisotropic Grid and Adaptive Domain
TL;DR: In this paper, the authors propose a more efficient implementation of the Smolyak method for interpolation, namely, they show how to avoid costly evaluations of repeated basis functions in the conventional SMolyak formula, and they extend the SMOLYAK method to include anisotropic constructions that allow to target higher quality of approximation in some dimensions than in others.