idio.seg


  a <code><a rd-options="" href="/link/get.factor.model?package=factorcpt&version=0.1.2" data-mini-rdoc="factorcpt::get.factor.model">get.factor.model</a></code> object with estimates of the factor structure
  


  see <code>scales</code> in <code><a rd-options="" href="/link/factor.seg.alg?package=factorcpt&version=0.1.2" data-mini-rdoc="factorcpt::factor.seg.alg">factor.seg.alg</a></code>
  

scales


  see <code>idio.diag</code> in <code><a rd-options="" href="/link/factor.seg.alg?package=factorcpt&version=0.1.2" data-mini-rdoc="factorcpt::factor.seg.alg">factor.seg.alg</a></code>
  

diag


  see <code>sig.lev</code> in <code><a rd-options="" href="/link/factor.seg.alg?package=factorcpt&version=0.1.2" data-mini-rdoc="factorcpt::factor.seg.alg">factor.seg.alg</a></code>
  

sig.lev


  the height of a binary tree for change-point analysis,
  see the Appendix of Barigozzi, Cho & Fryzlewicz (2016)
  

rule


  see <code>p</code> in <code><a rd-options="" href="/link/factor.seg.alg?package=factorcpt&version=0.1.2" data-mini-rdoc="factorcpt::factor.seg.alg">factor.seg.alg</a></code>
  


  see <code>dw</code> in <code><a rd-options="" href="/link/factor.seg.alg?package=factorcpt&version=0.1.2" data-mini-rdoc="factorcpt::factor.seg.alg">factor.seg.alg</a></code>
  


  see <code>dmby</code> in <code><a rd-options="" href="/link/func_dc_by?package=factorcpt&version=0.1.2" data-mini-rdoc="factorcpt::func_dc_by">func_dc_by</a></code>
  


  see <code>dtby</code> in <code><a rd-options="" href="/link/func_dc_by?package=factorcpt&version=0.1.2" data-mini-rdoc="factorcpt::func_dc_by">func_dc_by</a></code>
  


  see <code>do.parallel</code> in <code><a rd-options="" href="/link/factor.seg.alg?package=factorcpt&version=0.1.2" data-mini-rdoc="factorcpt::factor.seg.alg">factor.seg.alg</a></code>
  

do.parallel


First generating the panel of statistics via wavelet-based filtering of the estimated
idiosyncratic components, it applies the Double CUSUM Binary Segmentation in combination
with the bootstrap generated thresholds
to estimate the multiple change-points in the idiosyncratic components.


internal

Identifies change-points in the common and the idiosyncratic components via factor modelling.

Haeran Cho

factorcpt

Simultaneous Change-Point and Factor Analysis

idio.seg function


  a <code><a rd-options='' href='get.factor.model'>get.factor.model</a></code> object with estimates of the factor structure
  


  see <code>scales</code> in <code><a rd-options='' href='factor.seg.alg'>factor.seg.alg</a></code>
  


  see <code>idio.diag</code> in <code><a rd-options='' href='factor.seg.alg'>factor.seg.alg</a></code>
  


  see <code>sig.lev</code> in <code><a rd-options='' href='factor.seg.alg'>factor.seg.alg</a></code>
  


  see <code>p</code> in <code><a rd-options='' href='factor.seg.alg'>factor.seg.alg</a></code>
  


  see <code>dw</code> in <code><a rd-options='' href='factor.seg.alg'>factor.seg.alg</a></code>
  


  see <code>dmby</code> in <code><a rd-options='' href='func_dc_by'>func_dc_by</a></code>
  


  see <code>dtby</code> in <code><a rd-options='' href='func_dc_by'>func_dc_by</a></code>
  


  see <code>do.parallel</code> in <code><a rd-options='' href='factor.seg.alg'>factor.seg.alg</a></code>
  

idio.seg: Multiple change-point detection for the idiosyncratic components

Description

Usage

Arguments

Value

References