Rights statement: This is the peer reviewed version of the following article: Forni M, Giovannelli A, Lippi M, Soccorsi S. Dynamic factor model with infinite‐dimensional factor space: Forecasting. J Appl Econ. 2018;33:625–642. https://doi.org/10.1002/jae.2634 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/jae.2634/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
Accepted author manuscript, 1.16 MB, PDF document
Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Dynamic factor model with infinite-dimensional factor space
T2 - forecasting
AU - Forni, Mario
AU - Giovannelli, Alessandro
AU - Lippi, Marco
AU - Soccorsi, Stefano
N1 - This is the peer reviewed version of the following article: Forni M, Giovannelli A, Lippi M, Soccorsi S. Dynamic factor model with infinite‐dimensional factor space: Forecasting. J Appl Econ. 2018;33:625–642. https://doi.org/10.1002/jae.2634 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/jae.2634/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
PY - 2018/8/1
Y1 - 2018/8/1
N2 - The paper compares the pseudo real-time forecasting performance of three Dynamic Factor Models: (i) The standard principal-component model introduced by Stock and Watson in 2002, (ii) The model based on generalized principal components, introduced by Forni, Hallin, Lippi and Reichlin in 2005, (iii) The model recently proposed by Forni, Hallin, Lippi and Zaffaroni in 2015. We employ a large monthly dataset of macroeconomic and financial time series for the U.S. economy, which includes the Great Moderation, the Great Recession and the subsequent recovery (an update of the so-called Stock and Watson dataset). Using a rolling window for estimation and prediction, we find that (iii) significantly outperforms (i) and (ii) in the Great Moderation period for both Industrial Production and Inflation, that (iii) is also the best method for Inflation over the full sample. However, (iii) is outperformed by (ii) and (i) over the full sample for Industrial Production.
AB - The paper compares the pseudo real-time forecasting performance of three Dynamic Factor Models: (i) The standard principal-component model introduced by Stock and Watson in 2002, (ii) The model based on generalized principal components, introduced by Forni, Hallin, Lippi and Reichlin in 2005, (iii) The model recently proposed by Forni, Hallin, Lippi and Zaffaroni in 2015. We employ a large monthly dataset of macroeconomic and financial time series for the U.S. economy, which includes the Great Moderation, the Great Recession and the subsequent recovery (an update of the so-called Stock and Watson dataset). Using a rolling window for estimation and prediction, we find that (iii) significantly outperforms (i) and (ii) in the Great Moderation period for both Industrial Production and Inflation, that (iii) is also the best method for Inflation over the full sample. However, (iii) is outperformed by (ii) and (i) over the full sample for Industrial Production.
U2 - 10.1002/jae.2634
DO - 10.1002/jae.2634
M3 - Journal article
VL - 33
SP - 625
EP - 642
JO - Journal of Applied Econometrics
JF - Journal of Applied Econometrics
SN - 0883-7252
IS - 5
ER -