Forecasting U.S. Macroeconomic and Financial Time Series with Noncausal and Causal AR Models: A Comparison
In this paper, we compare the forecasting performance of univariate noncausal and conventional causal autoregressive models for a comprehensive data set consisting of 170 monthly U.S. macroeconomic and financial time series. The noncausal models consistently outperform the causal models in terms of the mean square and mean absolute forecast errors. For a set of 18 quarterly time series, the improvement in forecast accuracy due to allowing for noncausality is found even greater.
|Date of creation:||05 Apr 2011|
|Date of revision:|
|Contact details of provider:|| Postal: Ludwigstraße 33, D-80539 Munich, Germany|
Web page: https://mpra.ub.uni-muenchen.de
More information through EDIRC
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Markku Lanne & Arto Luoma & Jani Luoto, 2012.
"Bayesian Model Selection And Forecasting In Noncausal Autoregressive Models,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 27(5), pages 812-830, 08.
- Lanne, Markku & Luoma, Arto & Luoto, Jani, 2009. "Bayesian Model Selection and Forecasting in Noncausal Autoregressive Models," MPRA Paper 23646, University Library of Munich, Germany.
- Lanne, Markku & Saikkonen, Pentti, 2010.
"Noncausal autoregressions for economic time series,"
32943, University Library of Munich, Germany.
- Lanne Markku & Saikkonen Pentti, 2011. "Noncausal Autoregressions for Economic Time Series," Journal of Time Series Econometrics, De Gruyter, vol. 3(3), pages 1-32, October.
- Marcellino, Massimiliano & Stock, James H & Watson, Mark W, 2005.
"A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series,"
CEPR Discussion Papers
4976, C.E.P.R. Discussion Papers.
- Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
- Massimiliano Marcellino & James Stock & Mark Watson, 2005. "A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series," Working Papers 285, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
- Lanne, Markku & Saikkonen, Pentti, 2009.
"GMM Estimation with Noncausal Instruments,"
23649, University Library of Munich, Germany.
- Lanne, Markku & Luoto, Jani & Saikkonen, Pentti, 2012.
"Optimal forecasting of noncausal autoregressive time series,"
International Journal of Forecasting,
Elsevier, vol. 28(3), pages 623-631.
- Lanne, Markku & Luoto, Jani & Saikkonen, Pentti, 2010. "Optimal Forecasting of Noncausal Autoregressive Time Series," MPRA Paper 23648, University Library of Munich, Germany.
- Breid, F. Jay & Davis, Richard A. & Lh, Keh-Shin & Rosenblatt, Murray, 1991. "Maximum likelihood estimation for noncausal autoregressive processes," Journal of Multivariate Analysis, Elsevier, vol. 36(2), pages 175-198, February.
When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:30254. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Joachim Winter)
If references are entirely missing, you can add them using this form.