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Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods
کاوش داده های بزرگ با استفاده از عامل پارسیمون، یادگیری ماشین، انتخاب متغیر و روش های انقباض-2018 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.
Keywords: Prediction ، Independent component analysis ، Sparse principal component analysis ، Bagging ، Boosting ، Bayesian model averaging ، Ridge regression ، Least angle regression ، Elastic net and non-negative garotte |
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