• Media type: E-Book
  • Title: Conditional FAVAR and scenario analysis for a large data : case of Tunisia
  • Contributor: Romdhane, Hajer Ben [VerfasserIn]; Tanfous, Nahed Ben [VerfasserIn]
  • imprint: Geneva, Switzerland: Graduate Institute of International and Development Studies, International Economics Department, 2017
  • Published in: Institut de hautes études internationales et du développement: Working paper ; 2017,15
  • Extent: 1 Online-Ressource (circa 24 Seiten); Illustrationen
  • Language: English
  • Identifier:
  • Keywords: Arbeitspapier ; Graue Literatur
  • Origination:
  • Footnote:
  • Description: The aim of this paper is to compute the conditional forecasts of a set of variables of interest on future paths of some variables in dynamic systems. We build a large dynamic factor models for a quarterly data set of 30 macroeconomic and financial indicators. Results of forecasting suggest that conditional FAVAR models which incorporate more economic information outperform the unconditional FAVAR in terms of the forecast errors.
  • Access State: Open Access