The only people who have anything to fear from free software are those whose products are worth even less. David Emery 1 OVERVIEW Recently, there has been great interest in applying parallel computation routines to standard econometric procedures. This interest has arisen for at least three reasons. As the size of datasets increases, computational demands to implement even standard econometric procedures increase; many applied researchers are moving towards more computationally demanding econometric methods; and multiple processors have become standard on desktop and laptop computers. Many computationally demanding econometric procedures are ‘embarrassingly parallel’ problems, meaning that they require a large number of independent calculations. Common econometric examples of ‘embarrassingly parallel’ problems include the bootstrap, Monte Carlo simulations or nonlinear optimization in which one wants to optimize multiple times based on different starting values (i.e. ‘multistart’) to avoid local optima. To address these needs, a variety of parallel libraries in R have recently become available. Examples include the multicore (Urbanek, ), snow (Tierney et al ., ), snowfall (Knaus, ) and parallel packages ( parallel is available in base R and calls functions from both multicore and snow ). Each is freely available, and all are relatively simple to combine
Journal of Applied Econometrics – Wiley
Published: Nov 1, 2013
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