Methoden vergelijken

	Baxter-King Band-Pass Filter	STL-decompositie: Seizoensgebonden-Trenddecompositie met Loess
Vakgebied	Econometrie	Econometrie
Familie	Process / pipeline	Process / pipeline
Jaar van ontstaan	1999	1990
Grondlegger	Marianne Baxter & Robert King	Cleveland, Cleveland, McRae & Terpenning
Type	Linear symmetric moving-average filter	nonparametric iterative smoother
Oorspronkelijke bron	Baxter, M., & King, R. G. (1999). Measuring business cycles: Approximate band-pass filters for economic time series. Review of Economics and Statistics, 81(4), 575–593.	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.
Aliassen	Baxter-King Filter, Band-Pass Filter (Baxter-King), BK Band-Pass Filter, Bant Geçiren Süzgeç	Seasonal-Trend Decomposition using Loess, STL filtering, Loess-based seasonal decomposition, Mevsimsel-Trend Ayrıştırma (STL)
Verwant	3	3
Samenvatting	The Baxter-King (BK) band-pass filter, introduced by Marianne Baxter and Robert King in 1999, is a linear symmetric moving-average filter designed to isolate cyclical fluctuations in macroeconomic time series that fall within a specified range of periodicities. It removes both very low-frequency trends and very high-frequency noise, retaining only the business-cycle component—typically oscillations with a period of six to thirty-two quarters for quarterly data.	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.