The computational difficulty of econometric problems has increased dramatically in recent years as econometricians examine more complicated models and utilize more sophisticated estimation techniques. Many problems in econometrics are `embarrassingly parallel' and can take advantage of parallel computing to reduce the wall clock time it takes to solve a problem. In this paper I demonstrate a method that can be used to solve a maximum likelihood problem using the MPI message passing library. The econometric problem is a simple multinomial logit model that does not require parallel computing but illustrates many of the problems one would confront when estimating more complicated models
In the above raport the usage of the statistical methods to predict the efficiency of the parallel a...
This paper presents the parallel computing implementation of the MitISEM algorithm, labeled Parallel...
Typically, parallel algorithms are developed to leverage the processing power of multiple processors...
The computational difficulty of econometric problems has increased dramatically in recent years as e...
Many econometric problems can benefit from the application of parallel computing techniques, and rec...
Parallel computation has a long history in econometric computing, but is not at all wide spread. We ...
This paper shows how a high level matrix programming language may be used to perform Monte Carlo sim...
This paper shows how a high level matrix programming language may be used to perform Monte Carlo sim...
Abstract. Kernel density estimation is nowadays a very popular tool for nonparametric probabilistic ...
This paper presents the parallel computing implementation of the MitISEM algorithm, labeled Parallel...
textabstractThis paper presents the parallel computing implementation of the MitISEM algorithm, labe...
As technology progresses, the processors used for statistical computation are not getting faster: th...
The solution of large and sparse models presents in many ways a suitable structure for implementatio...
Stable distributions have a wide sphere of application: probability theory, physics, electronics, ec...
The main part of this licentiate thesis concerns parallelization of recursive estimation methods, bo...
In the above raport the usage of the statistical methods to predict the efficiency of the parallel a...
This paper presents the parallel computing implementation of the MitISEM algorithm, labeled Parallel...
Typically, parallel algorithms are developed to leverage the processing power of multiple processors...
The computational difficulty of econometric problems has increased dramatically in recent years as e...
Many econometric problems can benefit from the application of parallel computing techniques, and rec...
Parallel computation has a long history in econometric computing, but is not at all wide spread. We ...
This paper shows how a high level matrix programming language may be used to perform Monte Carlo sim...
This paper shows how a high level matrix programming language may be used to perform Monte Carlo sim...
Abstract. Kernel density estimation is nowadays a very popular tool for nonparametric probabilistic ...
This paper presents the parallel computing implementation of the MitISEM algorithm, labeled Parallel...
textabstractThis paper presents the parallel computing implementation of the MitISEM algorithm, labe...
As technology progresses, the processors used for statistical computation are not getting faster: th...
The solution of large and sparse models presents in many ways a suitable structure for implementatio...
Stable distributions have a wide sphere of application: probability theory, physics, electronics, ec...
The main part of this licentiate thesis concerns parallelization of recursive estimation methods, bo...
In the above raport the usage of the statistical methods to predict the efficiency of the parallel a...
This paper presents the parallel computing implementation of the MitISEM algorithm, labeled Parallel...
Typically, parallel algorithms are developed to leverage the processing power of multiple processors...