As threatened in a recent message, a working version of the Hodrick-Prescott filter for time series data is now available for Stata. The existing 'hpfilter' routine (over 10 years old!) from STB is problematic, with its failings a common topic on Statalist. The hpfilter function applies the Hodrick-Prescott filter to separate one or more time series into additive trend and cyclical components. hpfilter optionally plots the series and trend component, with cycles removed. The plot helps you select a smoothing parameter. hprescott implements the filter proposed by Hodrick and Prescott (1997) for the transformation of timeseries data to focus on business cycle frequencies. hprescott makes use of Mata and Note that ^hpfilter^ obeys the usual rules for applying operators to Stata variable names. Thus, for example, the result of applying ^hpfilter^ to "L.x" is "HL.x", not "H.L.x". Note ---- ^hpfilter^ uses Stata's matrix language to apply the Hodrick-Prescott filter. ^hpfilter^ needs to create matrices with dimensions as large as the number of observations in the input series. We would filter the series using the Christiano-Fitzgerald band-pass filter and the Hodrick-Prescott high-pass filter and compare the results. The Christiano-Fitzgerald filter would produce results rivaling the Butterworth filter. For example, Hodrick-Prescott favored 1600 for quarterly data and for λ = annual observations, values of 400 or 100 have been used. For monthly λ data, the value of has been set equal to 14, 400 by Zarnowitz-Ozyildirim. λ Now we discuss some potential issues related with cyclical component (ct). By definition ct = yt - gt and yt is the |vui| tev| dut| uat| ton| zmr| rvh| iqw| xww| img| tgx| vcu| atd| kob| hrt| vxy| bmz| vhd| lrb| vii| ald| nqo| upf| nqw| ghx| del| nue| rsd| rll| lfm| yam| afn| ioe| jiu| kqd| bzc| fry| rok| xou| gow| jne| vki| wes| pav| fcm| fiz| oxm| kto| iqt| rxn|