Time Series Filters

In time series analysis, it is essential to remove values that are going out of a decided range and causing unusual fluctuation in the time series. A good filter must be able to remove unit roots and the cyclic components, or the filter must be capable of isolating fluctuations of the data at a certain frequency.

Time series filtering is a vital technique in econometrics and time series analysis, often employed to decompose a time series into trend and cyclical components. This is especially important in macroeconomics and finance, where identifying business cycle fluctuations or long-term economic trends is essential.

The widely used time series filters are Hodrick-Prescott (HP) filter, Baxter-King (BK) filter, and Christiano-Fitzgerald (CF) filter.

Obtaining Time Series Filters

Time Series Filters are available as an extension procedure with Python plug-in. Therefore, install the extension module to run the procedure.

complete the following steps to run the procedure for Time Series Filters.

  1. Go to Data > Define date and time to generate the date variables. See Define Dates for more information.
  2. After you define the date and time, click Analyze > Forcasting > Time Series Filters (TSF).
  3. Select the time series filter that you want to apply on the data set.
  4. Select the variables and move them to the variable list for the appropriate time series filter.
  5. Set the value for Lambda for Hodrick-Prescott (HP) filter or set the values for Low, High, and K for Baxter-King (BK) filter and Christiano-Fitzgerald (CF) filter.

    Optionally, you can select Drift for Christiano-Fitzgerald (CF) filter.

  6. Click Run.