General Model-Based Filters for Extracting Cycles and Trends in Economic Time Series

By Andrew S. Harvey, T. Trimbur. Review of Economics and Statistics, 2003

295 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/3e07715a6868ed08108b53e45523d42c8eed73c1

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In Control and Systems Engineering

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Citations in the corpus

Trends and cycles in economic time series: A Bayesian approach (2005, methodology): “The same is true of the more recent band-pass lter of Baxter and King (1999); see Harvey and Trimbur (2003) and Murray (2003).”
The relationship between the Beveridge–Nelson decomposition and other permanent–transitory decompositions that are popular in economics (2008, methodology): “We also computed the smoothed estimates from a second order stochastic trend/cycle model described in Harvey and Trimbur (2003).”
1 Trend Filtering (2009, background): “Suppose that the time series data y has autoregressive (AR) components in addition to the trend x and the irregular component z: yt = xt + a1yt−1 + · · ·+ aryt−r + zt, where ai are model coefficients.”
Let's take a break: Trends and cycles in US real GDP (2009, methodology): “…unconstrained ARIMA model (Campbell and Mankiw, 1987, Watson, 1986, Cochrane, 1986), Unobserved Components (UC) models (Clark, 1987), the Hodrick-Prescott (1997) (HP) filter, the Band- Pass (BP) filter (Baxter and King, 1999) and the generalized Butterworth filters (e.g., Harvey and Trimbur, 2003).”
A Frequency Selective Filter for Short-Length Time Series (2005, methodology): “By means of the formula (16) or (17), which allows the tuning of the cutoff frequency, a Hodrick‐Prescott bandpass filter can be obtained as the difference between two highpass filters with different appropriate λ values, as shown in Gomez (2001) and Harvey and Trimbur (2003a) for Butterworth filt”
Business, Housing and Credit Cycles (2018, background): “Huber (2016) provides further evidence on this relation based on turning point analysis for a sample of 18 OECD countries showing that homeownership rates are more important for explaining cross-country differences than financing conditions (see also Cerutti et al.”