An Exploration of Trend-Cycle Decomposition Methodologies in Simulated Data

51 Pages Posted: 18 Feb 2020 Last revised: 4 Aug 2024

See all articles by Robert J. Hodrick

Robert J. Hodrick

Columbia University - Columbia Business School, Finance; National Bureau of Economic Research (NBER)

Date Written: February 2020

Abstract

This paper uses simulations to explore the properties of the HP filter of Hodrick and Prescott (1997), the BK filter of Baxter and King (1999), and the H filter of Hamilton (2018) that are designed to decompose a univariate time series into trend and cyclical components. Each simulated time series approximates the natural logarithms of U.S. Real GDP, and they are a random walk, an ARIMA model, two unobserved components models, and models with slowly changing nonstationary stochastic trends and definitive cyclical components. In basic time series, the H filter dominates the HP and BK filters in more closely characterizing the underlying framework, but in more complex models, the reverse is true.

Suggested Citation

Hodrick, Robert J., An Exploration of Trend-Cycle Decomposition Methodologies in Simulated Data (February 2020). NBER Working Paper No. w26750, Available at SSRN: https://ssrn.com/abstract=3539317

Robert J. Hodrick (Contact Author)

Columbia University - Columbia Business School, Finance ( email )

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