Modeling Functional Time Series and Mixed-Type Predictors With Partially Functional Autoregressions

Xiaofei Xu, Ying Chen*, Ge Zhang, Thorsten Koch


研究成果: Article査読


In many business and economics studies, researchers have sought to measure the dynamic dependence of curves with high-dimensional mixed-type predictors. We propose a partially functional autoregressive model (pFAR) where the serial dependence of curves is controlled by coefficient operators that are defined on a two-dimensional surface, and the individual and group effects of mixed-type predictors are estimated with a two-layer regularization. We develop an efficient estimation with the proven asymptotic properties of consistency and sparsity. We show how to choose the sieve and tuning parameters in regularization based on a forward-looking criterion. In addition to the asymptotic properties, numerical validation suggests that the dependence structure is accurately detected. The implementation of the pFAR within a real-world analysis of dependence in German daily natural gas flow curves, with seven lagged curves and 85 scalar predictors, produces superior forecast accuracy and an insightful understanding of the dynamics of natural gas supply and demand for the municipal, industry, and border nodes, respectively.

ジャーナルJournal of Business and Economic Statistics
出版ステータスAccepted/In press - 2022

ASJC Scopus subject areas

  • 統計学および確率
  • 社会科学(その他)
  • 経済学、計量経済学
  • 統計学、確率および不確実性


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