• Medientyp: E-Book
  • Titel: High Dimensional Threshold Regression with Common Stochastic Trends
  • Beteiligte: Massacci, Daniele [VerfasserIn]; Trapani, Lorenzo [VerfasserIn]
  • Erschienen: [S.l.]: SSRN, 2022
  • Umfang: 1 Online-Ressource (90 p)
  • Sprache: Englisch
  • DOI: 10.2139/ssrn.4133488
  • Identifikator:
  • Entstehung:
  • Anmerkungen: Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments June 10, 2022 erstellt
  • Beschreibung: We study inference for threshold regression in the context of a large panel factor model with common stochastic trends. We develop a Least Squares estimator for the threshold level, deriving almost sure rates of convergence and proposing a novel, testing based, way of constructing confidence intervals. We also investigate the properties of the PC estimator for the loadings and common factors in both regimes, and develop a procedure to estimate the number of common trends in each regime. Our theoretical findings are corroborated through a comprehensive set of Monte Carlo experiments and an application to US mortality data
  • Zugangsstatus: Freier Zugang