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UserFriendly Parallel Computations with Econometric Examples
 Computational Economics
"... This paper shows how a high level matrix programming language may be used to perform Monte Carlo simulation, bootstrapping, estimation by maximum likelihood and GMM, and kernel regression in parallel on symmetric multiprocessor computers or clusters of workstations. The implementation of paralleliz ..."
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Cited by 11 (7 self)
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This paper shows how a high level matrix programming language may be used to perform Monte Carlo simulation, bootstrapping, estimation by maximum likelihood and GMM, and kernel regression in parallel on symmetric multiprocessor computers or clusters of workstations. The implementation of parallelization is done in a way such that an investigator may use the programs without any knowledge of parallel programming. A bootable CD that allows rapid creation of a cluster for parallel computing is introduced. Examples show that parallelization can lead to important reductions in computational time. Detailed discussion of how the Monte Carlo problem was parallelized is included as an example for learning to write parallel programs for Octave.
Three Essays on Econometrics: Asymmetric Exponential Power Distribution, Econometric Computation, and . . .
, 2011
"... The dissertation consists of three independent essays, and they are put in as three chapters. The goal of the first chapter is to develop an estimation procedure for financial time series models with the error terms following the Asymmetric Exponential Power Distribution (AEPD). The AEPD is the most ..."
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Cited by 1 (0 self)
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The dissertation consists of three independent essays, and they are put in as three chapters. The goal of the first chapter is to develop an estimation procedure for financial time series models with the error terms following the Asymmetric Exponential Power Distribution (AEPD). The AEPD is the most general class of unimodal distributions. In addition to the usual location and scale parameters, it has skewness and kurtosis parameters. The kurtosis parameter is hard to estimate when the sample size is small and skewness is large. We show that when the skewness parameter is either close to zero or close to one the estimation of the kurtosis parameters are virtually unidentifiable unless the sample size is large. We analyze the nonlinear GARCH model (NGARCH) and an asset pricing model known as CKLS. We devise Bayesian Markov chain Monte Carlo (MCMC) algorithms. In chapter 2, we focus on econometric computation and develop a method to speed up intensive computation. The combination of MATLAB, C/C++ and Graphic Processing Unit (GPU) is a method to put convenience and speed together. MATLAB is a
UserFriendly Parallel Computations with Econometric Examples
, 2005
"... Abstract. This paper shows how a highlevel matrix programming language may be used to perform Monte Carlo simulation, bootstrapping, estimation by maximum likelihood and GMM, and kernel regression in parallel on symmetric multiprocessor computers or clusters of workstations. The implementation of p ..."
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Abstract. This paper shows how a highlevel matrix programming language may be used to perform Monte Carlo simulation, bootstrapping, estimation by maximum likelihood and GMM, and kernel regression in parallel on symmetric multiprocessor computers or clusters of workstations. The implementation of parallelization is done in a way such that an investigator may use the programs without any knowledge of parallel programming. A bootable CD that allows rapid creation of a cluster for parallel computing is introduced. Examples show that parallelization can lead to important reductions in computational time. Detailed discussion of how the Monte Carlo problem was parallelized is included as an example for learning to write parallel programs for Octave.
and
, 2007
"... The nature of computing is changing and it poses both challenges and opportunities for economists. Instead of increasing clock speed, future microprocessors will have “multicores ” with separate execution units. “Threads ” or other multiprocessing techniques that are rarely used today are required ..."
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The nature of computing is changing and it poses both challenges and opportunities for economists. Instead of increasing clock speed, future microprocessors will have “multicores ” with separate execution units. “Threads ” or other multiprocessing techniques that are rarely used today are required to take full advantage of them. Beyond one machine, it has become easy to harness multiple computers to work in clusters. Besides dedicated clusters, they can be made up of unused lab computers or even your colleagues ’ machines. Finally, grids of computers spanning the Internet are now becoming a reality.