Lanne, Markku and Luoto, Jani (2010): Has U.S. Inflation Really Become Harder to Forecast?
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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.
|Item Type:||MPRA Paper|
|Original Title:||Has U.S. Inflation Really Become Harder to Forecast?|
|Keywords:||Inflation forecast; Noncausal time series; Phillips curve|
|Subjects:||C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods; Simulation Methods
E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E31 - Price Level; Inflation; Deflation
C - Mathematical and Quantitative Methods > C2 - Single Equation Models; Single Variables > C23 - Models with Panel Data; Longitudinal Data; Spatial Time Series
|Depositing User:||Markku Lanne|
|Date Deposited:||09. Apr 2011 00:17|
|Last Modified:||14. Feb 2013 21:22|
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Lanne, M., and P. Saikkonen (2008). Modeling Expectations with Noncausal Autoregressions. HECER Discussion Paper 212.
Lanne, M., A. Luoma, and J. Luoto (2009). Bayesian Model Selection and Forecasting in Noncausal Autoregressive Models. Journal of Applied Econometrics (forthcoming).
Lanne, M., J. Luoto, and P. Saikkonen (2010). Optimal Forecasting of Noncausal Autoregressive Time Series. HECER Discussion Paper 286.
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