Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods. Kim, H. H. & Swanson, N. R. International Journal of Forecasting.
Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods [link]Paper  doi  abstract   bibtex   
A number of recent studies in the economics literature have focused on the usefulness of factor models in the context of prediction using “big data” (see Bai and Ng, 2008; Dufour and Stevanovic, 2010; Forni, Hallin, Lippi, & Reichlin, 2000; Forni et al., 2005; Kim and Swanson, 2014a; Stock and Watson, 2002b, 2006, 2012, and the references cited therein). We add to this literature by analyzing whether “big data” are useful for modelling low frequency macroeconomic variables, such as unemployment, inflation and GDP. In particular, we analyze the predictive benefits associated with the use of principal component analysis (PCA), independent component analysis (ICA), and sparse principal component analysis (SPCA). We also evaluate machine learning, variable selection and shrinkage methods, including bagging, boosting, ridge regression, least angle regression, the elastic net, and the non-negative garotte. Our approach is to carry out a forecasting “horse-race” using prediction models that are constructed based on a variety of model specification approaches, factor estimation methods, and data windowing methods, in the context of predicting 11 macroeconomic variables that are relevant to monetary policy assessment. In many instances, we find that various of our benchmark models, including autoregressive (AR) models, AR models with exogenous variables, and (Bayesian) model averaging, do not dominate specifications based on factor-type dimension reduction combined with various machine learning, variable selection, and shrinkage methods (called “combination” models). We find that forecast combination methods are mean square forecast error (MSFE) “best” for only three variables out of 11 for a forecast horizon of h = 1 , and for four variables when h = 3 or 12 . In addition, non-PCA type factor estimation methods yield MSFE-best predictions for nine variables out of 11 for h = 1 , although PCA dominates at longer horizons. Interestingly, we also find evidence of the usefulness of combination models for approximately half of our variables when h > 1 . Most importantly, we present strong new evidence of the usefulness of factor-based dimension reduction when utilizing “big data” for macroeconometric forecasting.
@article{kim_mining_????,
	title = {Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods},
	issn = {0169-2070},
	url = {http://www.sciencedirect.com/science/article/pii/S0169207016300668},
	doi = {10.1016/j.ijforecast.2016.02.012},
	abstract = {A number of recent studies in the economics literature have focused on the usefulness of factor models in the context of prediction using “big data” (see Bai and Ng, 2008; Dufour and Stevanovic, 2010; Forni, Hallin, Lippi, \& Reichlin, 2000; Forni et al., 2005; Kim and Swanson, 2014a; Stock and Watson, 2002b, 2006, 2012, and the references cited therein). We add to this literature by analyzing whether “big data” are useful for modelling low frequency macroeconomic variables, such as unemployment, inflation and GDP. In particular, we analyze the predictive benefits associated with the use of principal component analysis (PCA), independent component analysis (ICA), and sparse principal component analysis (SPCA). We also evaluate machine learning, variable selection and shrinkage methods, including bagging, boosting, ridge regression, least angle regression, the elastic net, and the non-negative garotte. Our approach is to carry out a forecasting “horse-race” using prediction models that are constructed based on a variety of model specification approaches, factor estimation methods, and data windowing methods, in the context of predicting 11 macroeconomic variables that are relevant to monetary policy assessment. In many instances, we find that various of our benchmark models, including autoregressive (AR) models, AR models with exogenous variables, and (Bayesian) model averaging, do not dominate specifications based on factor-type dimension reduction combined with various machine learning, variable selection, and shrinkage methods (called “combination” models). We find that forecast combination methods are mean square forecast error (MSFE) “best” for only three variables out of 11 for a forecast horizon of h = 1 , and for four variables when h = 3 or 12 . In addition, non-PCA type factor estimation methods yield MSFE-best predictions for nine variables out of 11 for h = 1 , although PCA dominates at longer horizons. Interestingly, we also find evidence of the usefulness of combination models for approximately half of our variables when h \> 1 . Most importantly, we present strong new evidence of the usefulness of factor-based dimension reduction when utilizing “big data” for macroeconometric forecasting.},
	urldate = {2016-08-29},
	journal = {International Journal of Forecasting},
	author = {Kim, Hyun Hak and Swanson, Norman R.},
	keywords = {Bagging, Bayesian model averaging, Boosting, Elastic net and non-negative garotte, Independent component analysis, Least angle regression, Prediction, Ridge regression, Sparse principal component analysis},
	file = {ScienceDirect Full Text PDF:files/56434/Kim and Swanson - Mining big data using parsimonious factor, machine.pdf:application/pdf;ScienceDirect Snapshot:files/56435/S0169207016300668.html:text/html}
}
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