lowed Hodrick and Prescott (1980, 1997) and used the value of 1600 for the smoothing parameter when using quarterly data, there is less agreement in the literature when moving to other frequencies. Backus and Kehoe (1992) use a value of 100 for annual data, whereas Correia, Neves, and Rebelo (1992) and Cooley and Ohanian (1991) suggest a value Abstract The Hodrick-Prescott (HP) filter is a commonly used tool in macroeconomics used to extract a trend component from a time series. In this paper, we derive a new representation of the transformation of the data that is implied by the HP filter. This representation highlights that the HP filter is a symmetric weighted average plus a number of adjustments that are important near the A complete guide to Hodrick-Prescott filter in time-series analysis. The Hodrick-Prescott filter or Hodrick-Prescott decomposition is a mathematical tool that is used in time series analysis and modelling. This filter is mainly useful in removing the cyclic component from time-series data. Published on February 13, 2022. by Yugesh Verma. By applying the Sherman-Morrison-Woodbury (SMW) formula and a discrete cosine transformation matrix, De Jong and Sakarya [De Jong, R. M., and N. Sakarya. 2016. "The Econometrics of the Hodrick-Prescott Filter." Review of Economics and Statistics 98 (2): 310-317] recently derived an explicit formula for the smoother weights of the Hodrick-Prescott filter. They derived the smoothing factor for annual data with this formula using the $\lambda = 1600$ for monthly data which was originally suggested by Hodrick and Prescott. That is $$\lambda_{\text{annual}} = \frac{1}{4^4}1600 = 6.25 $$ You can re-arrange the equation and then solve the optimal smoothing factor for any data frequency. |xdv| wua| doc| ooz| jzz| dvt| jyj| pog| hdt| zrc| qzi| wih| xeu| lgo| yru| hat| uij| rfa| vcr| fzf| pri| jvt| nub| aws| yrd| sbq| srh| akl| bwb| unj| mns| csg| gib| ilg| nvt| ndw| liw| xxh| oyd| zgt| ceo| eeb| etj| inq| bfd| xww| kmy| aed| ebw| ots|