Has U.S. Inflation Really Become Harder to Forecast?
Recently Stock and Watson (2007) showed that since the mid-1980s it has been hard for backward-looking Phillips curve models to improve on simple univariate models in forecasting U.S. inflation. While this indeed is the case when the benchmark is a causal autoregression, little change in forecast accuracy is detected when a noncausal autoregression is taken as the benchmark. In this note, we argue that a noncausal autoregression indeed provides a better characterization of U.S. inflation dynamics than the conventional causal autoregression and it is, therefore, the appropriate univariate benchmark model.
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- 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.
- Andrew Atkeson & Lee E. Ohanian, 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Win, pages 2-11.
- Markku Lanne & Pentti Saikkonen, 2008.
"Modeling Expectations with Noncausal Autoregressions,"
Economics Working Papers
ECO2008/20, European University Institute.
- Lanne, Markku & Saikkonen, Pentti, 2008. "Modeling Expectations with Noncausal Autoregressions," MPRA Paper 8411, 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.
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