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The difficulty in modelling inflation and the significance in discovering the underlying data generating process of inflation is expressed in an ample literature regarding inflation forecasting. In this paper we evaluate nonlinear machine learning and econometric methodologies in forecasting the...
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modifies a wage–price-setting (WPS) model to forecast U.S. inflation over one- to three-year horizons. The out …-of-sample forecast results show that productivity growth is a useful predictor of inflation, in the sense that the modified WPS model … improves upon some univariate benchmark models during the 1990Q1–2020Q2 period. Since the early 2000s, forecast accuracy can be …
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, introducing slack into models estimated using headline PCE inflation data or conventional core inflation data causes forecast …
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Analysis of monthly disaggregated data from 1978 to 2016 on US household in ation expectations reveals that exposure to news on in ation and monetary policy helps to explain in ation expectations. This remains true when controlling for household personal characteristics, their perceptions of the...
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