High‐dimensional macroeconomic forecasting and variable selection via penalized regression
By Yoshimasa Uematsu, Shinya Tanaka. Econometrics Journal, 2019
60 citations recorded by the source corpus.
This study examines high-dimensional forecasting and variable selection via folded-concave penalized regressions. The penalized regression approach leads to sparse estimates of the regression coefficients and allows the dimensionality of the model to be much larger than the sample size. First, we discuss the theoretical aspects of a penalized regression in a time series setting. Specifically, we show the oracle inequality with ultra-high-dimensional time-dependent regressors. Then we show the validity of the penalized regression using two empirical applications. First, we forecast quarterly US gross domestic product data using a high-dimensional monthly data set and the mixed data sampling (MIDAS) framework with penalization. Second, we examine how well the penalized regression screens a hidden portfolio based on a large New York Stock Exchange stock price data set. Both applications show that a penalized regression provides remarkable results in terms of forecasting performance and variable selection.
View Paper: https://www.semanticscholar.org/paper/8dea8b6494160250a6b7ccdafe8909d61ceadce3
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- Ricardo P. Masini
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Citations in the corpus
- Machine Learning Advances for Time Series Forecasting (2020, background): “Uematsu and Tanaka (2019) consider high-dimensional forecasting and variable selection via folded-concave penalized regressions.”
- The VIF and MSE in Raise Regression (2020, background): “These methods are applied not only for the treatment of multicollinearity but also for the selection of variables (see, for example, Dupuis and Victoria-Feser [8], Li and Yang [9] Liu et al. [10], or Uematsu and Tanaka [11]), which is a crucial issue in many areas of science when the number of varia”