A Simple Adaptive Measure of Core Inflation

By Timothy Cogley. 2002

272 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2b1a3ba5308f5162ff40f51a974b9ff11451a1ad

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In Economics and Econometrics

Citations in the corpus

Advances in Forecasting Under Instability (2012, unknown): “37Alternative approaches to proxy a slowly evolving in ation rate include the simple exponential smoothing method by Cogley (2002) and the autoregressive model with a shifting mean which evolves smoothly over time according to an exponential function, proposed by Gonzales, Hubrich and Terasvirta…”
Real-Time Density Forecasts From Bayesian Vector Autoregressions With Stochastic Volatility (2011, background): “As emphasized by Cogley (2002), exponential smoothing offers a simple and computationally convenient approach to capturing gradual changes in means.”
Inflation Persistence: Facts or Artefacts? (2004, unknown): “This series has been analysed among others by Taylor (2000), Cogley and Sargent (2001), Pivetta and Reis (2003) and Levin and Piger (2004). Let us start by focussing on the mean of inflation.”
A Core Inflation Indicator for the Euro Area (2005, background): “…the dynamic links existing among prices and real activity to extract a core or “long run” component of the inflation process or, more recently, Cogley (2002) who proposes an adaptive measure of inflation to track persistent movements in inflation.2 Our core inflation measure is constructed…”
Central banks and financial crises (2008, background): “…domains, i cluding ‘trimmed mean’ measures and ‘approximate band pass filters’ will not be considered (see e.g. Bryan and Cecchetti (1994), Quah and Vahey (1995), Baxter and King (1999), Cogley (2002), Cogley and Sargent (2001, 2005)), Dolmas (2005), Rich and Steindel (2007), Kiley (2008)).”
Expected Returns in Treasury Bonds (2015, unknown): “This view is a recurring theme in Cogley and Sargent (2005a,b), Cogley, Primiceri, and Sargent (2010), Erceg and Levin (2003), Kozicki and Tinsley (1998), Ireland (2007), among others, and follows from the idea of adaptive models of policy making introduced in Sargent (1999) and motivated by Sims (1”
Adaptive models and heavy tails with an application to inflation forecasting (2017, background): “Cogley, 2002). Our model features time variation in location and scale, and Student-t errors. This implies a non-linear filtering process with a weighting pattern that cannot be replicated by the procedures proposed in the literature. The benefit of this approach is that observations that are percei”
A new core inflation indicator for New Zealand (2012, unknown): “Table 2 compares the core inflation indicator with weighted-median inflation; trimmed-mean inflation (with a 10 percent trim); the CPI excluding food, administration changes, and gasoline; and the exponentially smoothed measure of core inflation proposed by Cogley (2002).”
Comparing forecast accuracy: A Monte Carlo investigation (2013, methodology): “Since a constant term will always be included in the set of predictors, without loss of generality we set μy = μx = 0 in the data generating process (9), and (10).”
Bayesian Fan Charts for U.K. Inflation: Forecasting and Sources of Uncertainty in an Evolving Monetary System (2005, methodology): “(32) To set the scale matrix ¯ Q, we assume ¯ Q = γ 2 ¯ P , (33) and we calibrate γ so that ¯ Q is comparable to the value used by Cogley and Sargent (2002).”
Averaging Forecasts from Vars with Uncertain Instabilities (2006, unknown): “For tractability in real time forecasting, we follow Cogley (2002) in estimating the inflation target or trend with exponential smoothing, with a smoothing parameter of .05.9 Table 1 provides additional detail on all of these model specifications.”
Are gold and silver a hedge against inflation? A two century perspective (2015, methodology): “…the expected consumer price level.12 We employ two different estimates for core CPI: the exponentially smoothed core inflation estimator proposed by Cogley (2002) and a wavelet method proposed more recently by Dowd et al. (2011).13 The later compare the wavelet-based core measures against a number…”