The Hodrick–Prescott filter (also known as Hodrick–Prescott decomposition) is a mathematical tool used in macroeconomics, especially in real business cycle theory, to remove the cyclical component of a time series from raw data. It is used to obtain a smoothed-curve representation of a time series, one that is more sensitive to long-term than to short-term fluctuations. The adjustment of the sensitivity of the trend to short-term fluctuations is achieved by modifying a multiplier . The filter was popularized in the field of economics in the 1990s by economists Robert J. Hodrick and Nobel Memorial Prize winner Edward C. Prescott. However, it was first proposed much earlier by E. T. Whittaker in 1923.
The reasoning for the methodology uses ideas related to the decomposition of time series. Let for denote the logarithms of a time series variable. The series is made up of a trend component, denoted by and a cyclical component, denoted by such that . Given an adequately chosen, positive value of , there is a trend component that will solve
The first term of the equation is the sum of the squared deviations which penalizes the cyclical component. The second term is a multiple of the sum of the squares of the trend component's second differences. This second term penalizes variations in the growth rate of the trend component. The larger the value of , the higher is the penalty. Hodrick and Prescott suggest 1600 as a value for for quarterly data. Ravn and Uhlig (2002) state that should vary by the fourth power of the frequency observation ratio; thus, should equal 6.25 for annual data and 129,600 for monthly data.
Drawbacks to the Hodrick–Prescott filter
The Hodrick–Prescott filter will only be optimal when:
- Data exists in a I(2) trend.
- If one-time permanent shocks or split growth rates occur, the filter will generate shifts in the trend that do not actually exist.
- Noise in data is approximately normally distributed.
- Analysis is purely historical and static (closed domain). The filter causes misleading predictions when used dynamically since the algorithm changes (during iteration for minimization) the past state (unlike a moving average) of the time series to adjust for the current state regardless of the size of used.
The standard two-sided Hodrick–Prescott filter is non-causal as it is not purely backward looking. Hence, it should not be used when estimating DSGE models based on recursive state-space representations (e.g., likelihood-based methods that make use of the Kalman filter). The reason is that the Hodrick–Prescott filter uses observations at to construct the current time point , while the recursive setting assumes that only current and past states influence the current observation. One way around this is to use the one-sided Hodrick–Prescott filter.
Exact algebraic formulas are available for the two-sided Hodrick–Prescott filter in terms of its signal-to-noise ratio .
- Hodrick, Robert; Prescott, Edward C. (1997). "Postwar U.S. Business Cycles: An Empirical Investigation". Journal of Money, Credit, and Banking 29 (1): 1–16. JSTOR 2953682.
- Whittaker, E. T. (1923). "On a New Method of Graduation". Proceedings of the Edinburgh Mathematical Association 41: 63–75. doi:10.1017/S001309150000359X. - as quoted in Philips 2010
- Kim, Hyeongwoo. "Hodrick–Prescott Filter" March 12, 2004
- Ravn, Morten; Uhlig, Harald (2002). "On adjusting the Hodrick–Prescott filter for the frequency of observations". The Review of Economics and Statistics 84 (2): 37.
- French, Mark W. (2001). "Estimating Changes in Trend Growth of Total Factor Productivity: Kalman and H-P Filters versus a Markov-Switching Framework". FEDS Working Paper No. 2001-44. SSRN 293105.
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- Enders, Walter (2010). "Trends and Univariate Decompositions". Applied Econometric Time Series (Third ed.). New York: Wiley. pp. 247–7. ISBN 978-0470-50539-7.
- Favero, Carlo A. (2001). Applied Macroeconometrics. New York: Oxford University Press. pp. 54–5. ISBN 0-19-829685-1.
- Mills, Terence C. (2003). "Filtering Economic Time Series". Modelling Trends and Cycles in Economic Time Series. New York: Palgrave McMillan. pp. 75–102. ISBN 1-4039-0209-7.