Kenneth Judd - Academia.edu (original) (raw)

Papers by Kenneth Judd

Journal of Economic Dynamics and Control, Feb 1, 2011

RePEc: Research Papers in Economics, 2013

RePEc: Research Papers in Economics, Jul 1, 2002

Computing in Economics and Finance, 2006

ABSTRACT Economic analysis often leads to multidimensional numerical problems. The {\em Curse of ... more ABSTRACT Economic analysis often leads to multidimensional numerical problems. The {\em Curse of Dimensionality\/} often leads researchers to adopt methods designed for very high-dimension problems, but inefficient for problems of intermediate dimension. However, a little mathematics can greatly help dealing with the {\em Curse\/}. We will survey methods from approximation theory, numerical quadrature, and symbolic computation that have helped economists tackle high-dimensional problems, and current work that will further reduce the computational cost of multidimensional problems.

Journal of Economic Theory, Feb 1, 1985

Computing in Economics and Finance, 2004

ABSTRACT Numerical methods are increasingly used in graduate student research. I will discuss the... more ABSTRACT Numerical methods are increasingly used in graduate student research. I will discuss the problems of how to teach the necessary skills and the challenges of incorporating such teaching into a graduate program

Journal of Economic Behavior and Organization, Jun 1, 2007

ABSTRACT Prof. Mirowski has written a provocative discussion of new ways to model markets that fo... more ABSTRACT Prof. Mirowski has written a provocative discussion of new ways to model markets that focus on their algorithmic aspects. Many of his substantive points about the weaknesses of standard theory are widely accepted; in particular, we have little idea of how prices are formed, we agree that economic agents are not infinitely intelligent, and it is clear that the markets in any modem economy take many forms. Prof. Mirowski correctly notes that there has been some movement towards more detailed analyses of markets, such as in the literatures on mechanism design, auction design, "Zero-Intelligence Agent" models, market microstructure, engineering economics, and applications of artificial intelligence. All economists welcome further work on detailed analysis of how markets work and how they evolve over time. Prof. Mirowski helps the reader greatly by anchoring his presentation in the following definition of a market. For the purposes of our present argument, we shall define a market as a formal automaton, in the sense of the field of mathematics pioneered by John von Neumann, and now taught as basic computational theory. Intuitively, we shall characterize a particular market as a specialized piece of software, which both calculates and acts upon inputs, comprised of an integrated set of algorithms that perform the following functions: Data dissemination and communications, plus rules of exclusion. Order routing through time and space. Order queuing and execution. Price discovery and assignment. Custody and delivery arrangement. Clearing and settlement, including property rights assignment. Record-keeping.

Social Science Research Network, 2000

Journal of Public Economic Theory, May 1, 2004

SSRN Electronic Journal, 2019

Using constrained optimization, we develop a simple, efficient approach (applicable in both uncon... more Using constrained optimization, we develop a simple, efficient approach (applicable in both unconstrained and constrained maximum-likelihood estimation problems) to computing profile-likelihood confidence intervals. In contrast to Wald-type or score-based inference, the likelihood ratio confidence intervals use all the information encoded in the likelihood function concerning the parameters, which leads to improved statistical properties. In addition, the method does no suffer from the computational burdens inherent in the bootstrap. In an application to Rust's (1987) bus-engine replacement problem, our approach does better than either the Wald or the bootstrap methods, delivering very accurate estimates of the confidence intervals quickly and efficiently. An extensive Monte Carlo study reveals that in small samples, only likelihood ratio confidence intervals yield reasonable coverage properties, while at the same time discriminating implausible values.

Conquering Big Data with High Performance Computing, 2016

This chapter proposes a general approach for solving a broad class of difficult optimization prob... more This chapter proposes a general approach for solving a broad class of difficult optimization problems using big data techniques. We provide a general description of this approach as well as some examples. This approach is ideally suited for solving nonconvex optimization problems, multiobjective programming problems, models with a large degree of heterogeneity, rich policy structure, potential model uncertainty, and potential policy objective uncertainty. In our applications of this algorithm we use Hierarchical Database Format (HDF5) distributed storage and I/O as well as message passing interface (MPI) for parallel computation of a large number of small optimization problems.

RePEc: Research Papers in Economics, Jul 1, 2008

Computers & Operations Research, Apr 1, 2000

Computational Economics, Feb 2, 2014

© 1998 Massachusetts Institute of Technology All rights reserved. No part of this book may be rep... more © 1998 Massachusetts Institute of Technology All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the ...