Economic predictions with big data: The illusion of sparsity - Domenico Giannone; Michele Lenza; Giorgio E. Primiceri (Federal Reserve Bank of New York, 2018)
We compare sparse and dense representations of predictive models in macroeconomics, microeconomics, and finance. To deal with a large number of possible predictors, we specify a prior that allows for both variable selection and shrinkage. The posterior distribution does not typically concentrate on a single sparse or dense model, but on a wide set of models. A clearer pattern of sparsity can only emerge when models of very low dimension are strongly favored a priori.
Keywords: C11; C53; C55; 330; model selection; shrinkage; high dimensional data; Prognoseverfahren; Wirtschaftsmodell; Big Data; Modellierung; Theorie