Why Has U.S. Inflation Become Harder to Forecast

By James H. Stock, Mark W. Watson. 2007

854 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/167ddf0972ebf91fce7cd154870fc50e2cc70fe0

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In Economics and Econometrics

Citations in the corpus

Macroeconomic forecast accuracy in a data‐rich environment (2019, positive): “This is in line with Stock and Watson (2007) and Faust and Wright (2013).”
Monetary/Fiscal Policy Mix and Agents’ Beliefs (2017, background): “We will now show that the changes in the monetary/fiscal policy mix identified in this paper can account for the evolution of inflation expectations and the decline in the persistence and volatility of inflation that has been documented by Stock and Watson (2007).”
Real-Time Inflation Forecasting in a Changing World (2009, positive): “Given the results by Stock and Watson (2007, 2008), it appears that the UCSV model produces forecasts for U.S. inflation rates that are hard to beat by Phillips curve and naive time series models (including the RW model).”
The History of the Phillips Curve: Consensus and Bifurcation (2011, background): “In subsequent work, Staiger et al. (2001) and Stock and Watson (2007) abandoned the attempt to estimate the TV-NAIRU as a byproduct of the inflation equation, and Blinder and Rudd (2008) follow their lead.”
The Term Structure of Inflation Expectations (2012, result): “The wide confidence bounds for RMSE ratios that we compute are consistent with similar findings by Stock and Watson (2007).”
Learning in a Medium-Scale DSGE Model with Expectations Based on Small Forecasting Models (pp. 65-101) (2012, background): “In the literature, the attening of the Phillips curve has been observed in various contexts, cf. Atkeson (2001), Stock and Watson (2006), Borio and Filardo (2007), Kuttner and Robinson (2008), and others.”
Time Varying Dimension Models (2012, positive): “…that there have been many articles which use regression-based methods in recursive or rolling forecast exercises (e.g., Ang, Bekaert, and Wei 2007; Stock and Watson 2007; 2009) and that recently articles have been appearing using TVP models for forecasting (e.g., D’Agostino, Gambetti, and…”
Inflation-Gap Persistence in the U.S. (2008, background): “Stock and Watson (2007) examine GDP inflation.”
Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods (2019, neutral): “There are several reasons why our results differ from those of Stock and Watson (1999, 2007), and Atkeson and Oha-nian (2001).”
Forecast Error Variance Decompositions with Local Projections (2019, neutral): “As a result, the local projections (LP) method (Jordà 2005; Stock and Watson 2007) has gained prominence in applied macroeconomic research.”
Has the Fed Been a Failure (2012, neutral): “As people try to rebuild their money balances they 8 Concerning the difficulty of forecasting inflation in recent years especially see Stock and Watson (2007).”