Abstract
The purpose of this article is to provide three new empirical perspectives on the validity of the value of the smoothing parameter in the Hodrick–Prescott filter (HP filter): Bayesian smoothness perspective, output gap perspective, and forecasting perspective. The quarterly time series of industrial production and capacity utilization for developed countries are analyzed. The empirical results suggest that (1) from the Bayesian smoothness perspective, the HP filter with 1600 as the value of the smoothing parameter (HP1600 filter) is mostly unable to provide a sufficiently smooth trend component; (2) from the output gap perspective, the HP1600 filter provides a poor cycle component; (3) from the forecasting perspective, the HP1600 filter is most suitable for eight-step or nine-step ahead forecasting.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Afonso A, Furceri D (2008) EMU enlargement, stabilization costs and insurance mechanisms. J Int Money Finance 27: 169–187
Akaike H (1980) Likelihood and the Bayes procedure. In: Bernardo JM, DeGroot MH, Lindley DV, Smith AFM (eds) Bayesian statistics. University Press, Valencia, pp 143–166
Ash JCK, Easaw JZ, Heravi SM, Smyth DJ (2002) Are Hodrick-Prescott forecasts rational?. Empir Econ 27: 631–643
Assenmacher-Wesche K, Gerlach S (2008) Money growth, output gaps and inflation at low and high frequency: spectral estimates for Switzerland. J Econ Dyn Control 32: 411–435
Camba-Mendez G, Rodriguez-Palenzuela D (2003) Assessment criteria for output gap estimates. Econ Model 20: 529–562
Canova F (1994) Detrending and turning-points. Eur Econ Rev 38: 614–623
Canova F (1998) Detrending and business cycle facts. J Monet Econ 41: 475–512
Elliott G, Timmermann A (2008) Economic forecasting. J Econ Lit 46: 3–56
Fuhrer J, Tootell G (2008) Eyes on the prize: How did the fed respond to the stock market?. J Monet Econ 55: 796–805
Fukuda K (2006) Age-period-cohort decomposition of aggregate data: an application to U.S. and Japanese household saving rates. J Appl Econom 21: 981–998
Harvey AC, Todd PHJ (1983) Forecasting economic time series with structural and Box-Jenkins models: a case study. J Bus Econ Stat 1: 299–315
Harvey AC, Jaeger A (1993) Detrending, stylized facts and the business cycle. J Appl Econom 8: 231–247
Harvey AC, Trimbur TM, Van Dijk HK (2007) Trends and cycles in economic time series: a Bayesian approach. J Econom 140: 618–649
Harvey AC, Monache DD (2009) Computing the mean square error of unobserved components extracted by misspecified time series models. J Econ Dyn Control 33: 283–295
Hodrick RJ, Prescott EC (1980) Postwar U.S. business cycles: an empirical investigation. Carnegie Mellon University Discussion Paper 451
Hodrick RJ, Prescott EC (1997) Postwar U.S. business cycles: an empirical investigation. J Money Credit Bank 29: 1–16
Koop G (2003) Bayesian econometrics. Wiley, England
Marcellino M, Stock JH, Watson MW (2006) A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series. J Econom 135: 499–526
Marcet A, Ravn MO (2003) The HP-filter in cross-country comparisons. CEPR Discussion Paper 4244
Nahuis NJ (2003) An alternative demand indicator: the non-accelerating inflation rate of capacity utilization. Appl Econ 35: 1339–1344
Nakamura T (1986) Bayesian cohort models for general cohort table analyses. Ann Inst Stat Math 38B: 353–370
Nelson CR, Plosser CI (1982) Trends and random walks in macroeconomic time series: some evidence and implications. J Monet Econ 10: 139–162
Parigi G, Siviero S (2001) An investment-function-based measure of capacity utilization: potential output and utilized capacity in the Bank of Italy’s quarterly model. Econ Model 18: 525–549
Pedersen TM (2001) The Hodrick-Prescott filter, the Slutzky effect, and the distortionary effect of filters. J Econ Dyn Control 25: 1081–1101
Ravn MO, Uhlig H (2002) On adjusting the Hodrick-Prescott filter for the frequency of observations. Rev Econ Stat 84: 371–380
Trimbur TM (2006) Detrending economic time series: a Bayesian generalization of the Hodrick-Prescott filter. J Forecast 25: 247–273
Young M (1996) Robust seasonal adjustment by Bayesian modelling. J Forecast 15: 355–367
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Fukuda, K. Three new empirical perspectives on the Hodrick–Prescott parameter. Empir Econ 39, 713–731 (2010). https://doi.org/10.1007/s00181-009-0332-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00181-009-0332-4
Keywords
- Bayesian smoothness solution
- Empirical perspective: Hodrick–Prescott filter
- Multistep ahead forecasting
- Output gap