Norges Bank paper outlines forecasting method for big data
Researchers produce forecasts using a Bayesian non-parametric model
A group of economists have outlined a method of forecasting the US stock market and economy using a large dataset, delivering what they say are "substantial" improvements, in a working paper published today (August 5) by Norges Bank.
In the paper Dynamic predictive density combinations for large data sets in economics and finance, authors Roberto Casarin, Stefano Grassi, Francesco Ravazzolo and Herman van Dijk build a Bayesian non-parametric predictive model, into which they plug more than 7,000
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