4.8.10. statsmodels.tsa.varma_process¶
Helper and filter functions for VAR and VARMA, and basic VAR class
Created on Mon Jan 11 11:04:23 2010 Author: josef-pktd License: BSD
This is a new version, I didn’t look at the old version again, but similar ideas.
- not copied/cleaned yet:
- fftn based filtering, creating samples with fft
- Tests: I ran examples but did not convert them to tests examples look good for parameter estimate and forecast, and filter functions
main TODOs: * result statistics * see whether Bayesian dummy observation can be included without changing
the single call to linalg.lstsq
- impulse response function does not treat correlation, see Hamilton and jplv
Extensions * constraints, Bayesian priors/penalization * Error Correction Form and Cointegration * Factor Models Stock-Watson, ???
see also VAR section in Notes.txt
4.8.10.1. Functions¶
ar2full(ar) |
make reduced lagpolynomial into a right side lagpoly array |
ar2lhs(ar) |
convert full (rhs) lagpolynomial into a reduced, left side lagpoly array |
lagmat(x, maxlag[, trim, original]) |
create 2d array of lags |
padone(x[, front, back, axis, fillvalue]) |
pad with zeros along one axis, currently only axis=0 |
trimone(x[, front, back, axis]) |
trim number of array elements along one axis |
varfilter(x, a) |
apply an autoregressive filter to a series x |
vargenerate(ar, u[, initvalues]) |
generate an VAR process with errors u |
varinversefilter(ar, nobs[, version]) |
creates inverse ar filter (MA representation) recursively |