Phillips Curve Inflation Forecasts

By James H. Stock, Mark W. Watson. 2008

393 citations recorded by the source corpus.

This paper surveys the literature since 1993 on pseudo out-of-sample evaluation of inflation forecasts in the United States and conducts an extensive empirical analysis that recapitulates and clarifies this literature using a consistent data set and methodology. The literature review and empirical results are gloomy and indicate that Phillips curve forecasts (broadly interpreted as forecasts using an activity variable) are better than other multivariate forecasts, but their performance is episodic, sometimes better than and sometimes worse than a good (not naive) univariate benchmark. We provide some preliminary evidence characterizing successful forecasting episodes.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/06450b577c157893cdaed56fa4c3094ca10c56b4

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In Economics and Econometrics

Citations in the corpus

Weak Instrument Robust Tests in GMM and the New Keynesian Phillips Curve (2009, methodology): “We think factors may help to address the identification problem for the NKPC because they have been found to be useful in forecasting inflation (see Stock and Watson 1999) and this is precisely what is needed for instruments to be relevant for the NKPC. Bai and Ng (2008a) showed that the use of…”
DYNAMIC FACTOR MODELS (2018, background): “Stock and Watson (2009c) compare factor forecasts to other high-dimensional forecasts in U.S. data and also find mixed results.”
Generalized Shrinkage Methods for Forecasting Using Many Predictors (2012, background): “Banerjee, Marcellino, and Masten 2009 , Del Negro and Otrok 2008 , Stock and Watson 2009 , and Stevanovic 2010a , b ).”
Short-Term Inflation Projections: A Bayesian Vector Autoregressive Approach (2014, result): “This result is in line with Stock and Watson (2008) who suggest some forms of non-linearities that make the Phillips curve stronger when deviations of unemployment from its natural level are large.”
Time Varying Dimension Models (2012, neutral): “…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…”