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EVIEWS FORUM SERIES
This series fluctuates around 1, making the retrending stage easy: multiply the trend of the reference series by the standardized/averaged set of leading indicator components. This detrended ratio-to-trend series is used in the second application of the HP filter (smoothing, smaller paramter) and one is left with a smoothed, detrended ratio-to-trend series. Of the seasonal component appear to vary proportionally to the level of the series" (. Multiplicative (either pure multiplicative or log-additive), because in most economic time series, the magnitudes Multiplicative methods seem toto be the most popular and the Bank of Spain explains why in its TRAMO/SEATS literature, which has lots of useful information regarding additive versus multiplicate approaches: "Usually, the decomposition scheme is The original series is detrended by dividing the original series by this trend component, thus implying a multiplicative approach. After the first application of the HP filter (detrending, larger parameter), one is left with a cyclical and a trend component. OECD literature ( ) suggests first detrending and then smoothing (using the larger and then smaller smoothing parameter, respectively). So the λ values above correspond to τ=120 months and τ=12 months." The two parameters are related through ω0=2π/τ.
EVIEWS FORUM FULL
Whereas ω0 is the frequency expressed in radians, and τ denotes the number of periods it takes to complete a full cycle. Going from frequencies to λ parameter is achieved by substituting into the formula: They are equivalent of setting λ = 133107.94 and λ = 13.93 respectively. "In the OECD CLI methodology, the default settings allow to remove cyclical components that have a cycle length longer than 120 month and those that have a cycle length shorter than 12 months. After some more searching I found the OECD offers an alternative guideline for choosing the smoothing parameter (lambda), which allows users to approximate a window:
EVIEWS FORUM HOW TO
While the FD filter makes it easy to set a "window" for the cycle-lengths of interest, it was unclear how to apply the HP filter with such an equivalent window. I'm still investigating the proper procedures for detrending using either the HP or Corbae-Ouliaris FD filter with the ultimate goal of a side-by-side comparison of their empirical accuracy in forecasting applications.
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Thank you, trubador!Ī brief update for fellow users interested in the HP filter: This easy-to-read and instructional paper comes from the Monetary Authority of Singapore. Other interested readers may appreciate an empirical application of the FD filter in a business cycle/forecasting scenario. Is this also the case with the FD filter? 0625 (2/36 quarters).ģ) The HP filter requires seasonally adjusted data series. Is there a good users’ guide I am somehow overlooking?ġ) Does the FD filter automatically detect and use the periodicity of the workfile? I know this is the case for other band pass filters in EViews, but when using the FD filter in monthly and quarterly settings, the default cycle periods are the same which makes me think it must be manually adjusted to fit the data at hand.Ģ) Similarly, how does one calculate the cycle periods? Is it simply 2 divided by the number of periods? The default values for quarterly data are. However, I'm still having some issues using the FD filter. Thanks for the excellent suggestion, Trubador! The Frequency-Domain (FD) filter by Corbae and Ouliaris seems to perform much better than an HP/BK/CF filter, all of which suffer from end-point problems. Any advice on this topic would also be appreciated. Yet, there are no references to what a reasonably large or small value of lambda might be - no rule of thumb, if you will. Any suggestions?Īdditionally, some of the literature references the double HP filter in which a large and then small value of lambda are used (to detrend and then smooth, respectively). It is unclear to me how to change this in EViews, at least using the menu commands (I'm afraid I'm not terribly experienced in using code). However, research suggests using a wider window of 12-120 months (see the OECD Methodology for Cycle Extraction).
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If I understand correctly, the HP filter uses default cut-off values of 18-96. I struggle to understand how to determine the ideal window-length, however. Harvey and Trimbur (2008) explain the risk in using a too-small smoothing parameter (lambda), though I have yet to find research explaining the risk of using too-large of a smooth parameter, other than the trend becomes increasingly linear and less sensitive to recent data. There is additional research that suggests using a power of 4 instead of 2. Lambda = 100*(number of periods in a year)^2