Ordinates are translated to the time domain and are

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ordinates are translated to the time domain and are used for synthesising the trend/cycle sequence.) IDEOLOG.PAS: Ideal Filters and their Approximations 9. Frequency-Domain Filters 9 SPECIFY AND APPLY THE FREQUENCY-DOMAIN FILTER 1. Lowpass Filter 1 SPECIFY THE FREQUENCY-DOMAIN LOWPASS FILTER B. A lowpass filter can be realised by subtracting a highpass component from the data. The differencing factor that is contained within the highpass filter can be cancelled with the summation operator to avoid the need for reinflating the filtered sequence.

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B Specify the cut-off point in degrees 22.5 EXTRACT LOWPASS TREND COMPONENT Before estimating the trend, you may require to reduce the data to stationarity a differencing operation Do you wish to take centralised second differences? Y/N Y <<A trend comprising Fourier components of frequencies less than 22.5 degrees per period interpolated through 60 points of an unidentified data series>> <RETURN> <<The residual sequence from detrending 160 points of an unidentified data sequence via the Fourier Method>> <RETURN> {++++++++++++++++++++++++++++++++++++++++++++} TRACK C estimate the trend/cycle component by method C. (This method reduces the data to stationarity by taking residual deviations of the data from a polynomial trend interpolated by least-squares regression. A linear trend is appropriate to logarithmic data that is to be subjected to twofold differencing. The residual sequence is subjected to a lowpass filter to extract the cyclical component. To generate the trend-cycle component, the cyclical component is added to the polynomial tend.) IDEOLOG.PAS: Ideal Filters and their Approximations 5. Polynomial Regression 5
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