The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Hodrick-Prescott filter with automatically selected jumps

This R package implements our novel method to supplement the classical HP filter with jumps and, possibly, regressors. The method is based on the following state-space representation

\[y_t = x_t^\top \beta + \mu_t + \varepsilon_t\]

\[\mu_{t+1} = \mu_t + \nu_t\]

\[\nu_{t+1} = \nu_t + \zeta_t,\]

where \(y_t\) is the observable time series, \(\mu_t\) is the level component, \(\nu_t\) is the slope component, \(\varepsilon_t\) and \(\zeta_t\) are white noise sequences with variances \(\sigma^2_\varepsilon\) and \(\sigma^2_\zeta\), respectively. The smoother, that is, the linear projection of \(\mu_t\) on the span of the observations \(\{y_1,\ldots,y_n\}\), coincides with the HP filter, where the smoothing constant \(\lambda\) is given by \(\sigma^2_\varepsilon / \sigma^2_\zeta\). Finally, \(x_t\) is a vector of regressors, and \(\beta\) is a vector of regression coefficients. These regressors are mainly used to model seasonal patterns in the data and should have a zero mean to not alter the interpretation of the HP filter as a trend extractor.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.