Abstract
Empirical economists often filter data prior to analysis to remove features that are a nuisance from the point of view of their theoretical models. Examples include trends and seasonals. This article describes how data filters work and the rationale that lies behind them. It focuses on the Baxter–King and Hodrick–Prescott filters, which are popular for measuring business cycles.
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Bibliography
Baxter, M., and R. King. 1999. Measuring business cycles: Approximate band-pass filters for economic time series. Review of Economics and Statistics 81: 575–593.
Benati, L. 2001. Band-pass filtering, cointegration, and business cycle analysis. Working Paper No. 142, Bank of England.
Beveridge, S., and C. Nelson. 1981. A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the business cycle. Journal of Monetary Economics 7: 151–174.
Blanchard, O., and D. Quah. 1989. The dynamic effects of aggregate demand and supply disturbances. American Economic Review 79: 655–673.
Christiano, L., and T. Fitzgerald. 2003. The band pass filter. International Economic Review 44: 435–465.
Cochrane, J. 1994. Permanent and transitory components of GNP and stock prices. Quarterly Journal of Economics 109: 241–265.
Cogley, T. 2001. Estimating and testing rational expectations models when the trend specification is uncertain. Journal of Economic Dynamics and Control 25: 1485–1525.
Cogley, T., and J. Nason. 1995. Effects of the Hodrick–Prescott filter on trend and difference stationary time series: Implications for business cycle research. Journal of Economic Dynamics and Control 19: 253–278.
Engle, R. 1974. Band-spectrum regression. International Economic Review 15: 1–11.
Fok, D., P. Franses, and R. Paap. 2006. Comparing seasonal adjustment methods. In Palgrave handbook of econometrics: Volume1; Econometric theory, ed. T. Mills and K. Patterson. Basingstoke: Palgrave Macmillan.
Granger, C., and P. Newbold. 1986. Forecasting economic time series. New York: Academic Press.
Hansen, L., and T. Sargent. 1993. Seasonality and approximation errors in rational expectations models. Journal of Econometrics 55: 21–55.
Hodrick, R., and E. Prescott. 1997. Postwar U.S. business cycles: An empirical investigation. Journal of Money Credit and Banking 29: 1–16.
Kaiser, R., and A. Maravall. 2001. Measuring business cycles in economic time series. New York: Springer.
King, R., and S. Rebelo. 1993. Low-frequency filtering and real business cycles. Journal of Economic Dynamics and Control 17: 207–231.
Murray, C. 2003. Cyclical properties of Baxter–King filtered time series. Review of Economics and Statistics 85: 472–476.
Osborn, D. 1995. Moving average detrending and the analysis of business cycles. Oxford Bulletin of Economics and Statistics 57: 547–558.
Sims, C. 1993. Rational expectations modeling with seasonally adjusted data. Journal of Econometrics 55: 9–19.
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Cogley, T. (2018). Data Filters. In: The New Palgrave Dictionary of Economics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95189-5_2150
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DOI: https://doi.org/10.1057/978-1-349-95189-5_2150
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Publisher Name: Palgrave Macmillan, London
Print ISBN: 978-1-349-95188-8
Online ISBN: 978-1-349-95189-5
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