Porovnat metody

	Hodrick-Prescottův filtr: Rozklad trend-cyklus pro makroekonomické časové řady	STL dekompozice: Dekompozice sezónní složky a trendu pomocí Loess
Obor	Ekonometrie	Ekonometrie
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	1997	1990
Tvůrce	Robert Hodrick & Edward Prescott	Cleveland, Cleveland, McRae & Terpenning
Typ	Penalized least-squares smoother	nonparametric iterative smoother
Původní zdroj	Hodrick, R. J., & Prescott, E. C. (1997). Postwar U.S. business cycles: An empirical investigation. Journal of Money, Credit and Banking, 29(1), 1–16.	Cleveland, R. B., Cleveland, W. S., McRae, J. E., & Terpenning, I. (1990). STL: A seasonal-trend decomposition procedure based on loess. Journal of Official Statistics, 6(1), 3–73.
Další názvy	Hodrick-Prescott Filter, HP Decomposition, Trend-Cycle Filter, HP Filtresi	Seasonal-Trend Decomposition using Loess, STL filtering, Loess-based seasonal decomposition, Mevsimsel-Trend Ayrıştırma (STL)
Příbuzné	3	3
Shrnutí	The Hodrick-Prescott (HP) filter is a penalized least-squares technique used in macroeconomics and empirical finance to decompose a time series into a smooth long-run trend component and a short-run cyclical component. Introduced by Hodrick and Prescott (1997) using postwar U.S. business cycle data, it has become one of the most widely applied filters in business cycle analysis, monetary policy research, and applied econometrics.	STL Decomposition, introduced by Cleveland, Cleveland, McRae, and Terpenning (1990), is a nonparametric procedure that separates a time series into three additive components — trend, seasonal, and remainder — using iterative locally weighted regression (loess). Widely used in economics, meteorology, and data science, it handles time series of any periodicity and is robust to the presence of outliers, making it a highly flexible alternative to classical decomposition methods.