High-dimensional macroeconomic forecasting and variable selection via penalized regression

Yoshimasa Uematsu, Shinya Tanaka

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


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 pe-nalization. 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.

Original languageEnglish
Pages (from-to)34-56
Number of pages23
JournalEconometrics Journal
Issue number1
Publication statusPublished - 2019


  • Macroeconomic forecasting
  • Mixed data sampling (MIDAS)
  • Oracle inequality
  • Penalized regression
  • Portfolio selection
  • Ultra-high-dimensional time series

ASJC Scopus subject areas

  • Economics and Econometrics


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