Working Paper: NBER ID: w26750
Authors: Robert J. Hodrick
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.
Keywords: No keywords provided
JEL Codes: E32
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Model Complexity (C60) | Performance of H filter (C51) |
| Model Complexity (C60) | Performance of HP filter (C22) |
| Model Complexity (C60) | Performance of BK filter (C52) |
| Performance of H filter (C51) | Performance of HP filter (C22) |
| Performance of H filter (C51) | Performance of BK filter (C52) |