Variable selection and inference for multi-period forecasting problems

Arbeitspapier

This paper conducts a broad-based comparison of iterated and direct multi-step forecasting approaches applied to both univariate and multivariate models. Theoretical results and Monte Carlo simulations suggest that iterated forecasts dominate direct forecasts when estimation error is a first-order concern, i.e. in small samples and for long forecast horizons. Conversely, direct forecasts may dominate in the presence of dynamic model misspecification. Empirical analysis of the set of 170 variables studied by Marcellino, Stock and Watson (2006) shows that multivariate information, introduced through a parsimonious factor-augmented vector autoregression approach, improves forecasting performance for many variables, particularly at short horizons.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: CESifo Working Paper ; No. 2543

Classification and Topics

• Classification: Wirtschaft
• Subject: Zeitreihenanalyse ; Prognoseverfahren ; Autokorrelation ; Faktorenanalyse ; Multivariate Analyse ; Monte-Carlo-Methode ; Theorie ; Schätzung ; Konjunktur ; USA

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Pesaran, Mohammad Hashem ; Pick, Andreas ; Timmermann, Allan
• Event: Veröffentlichung
• (who): Center for Economic Studies and ifo Institute (CESifo)
• (where): Munich
• (when): 2009

Further information

• Handle: http://hdl.handle.net/10419/26588
• Last update: 10.03.2025, 11:43 AM CET