fnets - Factor-Adjusted Network Estimation and Forecasting for High-Dimensional Time Series

Implements methods for network estimation and forecasting of high-dimensional time series exhibiting strong serial and cross-sectional correlations under a factor-adjusted vector autoregressive model. See Barigozzi, Cho and Owens (2024) <doi:10.1080/07350015.2023.2257270> for further descriptions of FNETS methodology and Owens, Cho and Barigozzi (2024) <arXiv:2301.11675> accompanying the R package.

Last updated 3 days ago

factor-modelsforecastinghigh-dimensionalnetwork-estimationtime-seriesvector-autoregression

5.36 score 7 stars 27 scripts 253 downloads

hdbinseg - Change-Point Analysis of High-Dimensional Time Series via Binary Segmentation

Binary segmentation methods for detecting and estimating multiple change-points in the mean or second-order structure of high-dimensional time series as described in Cho and Fryzlewicz (2014) <doi:10.1111/rssb.12079> and Cho (2016) <doi:10.1214/16-EJS1155>.

Last updated 1 years ago

1.78 score 2 stars 1 packages 9 scripts 149 downloads

mosum - Moving Sum Based Procedures for Changes in the Mean

Implementations of MOSUM-based statistical procedures and algorithms for detecting multiple changes in the mean. This comprises the MOSUM procedure for estimating multiple mean changes from Eichinger and Kirch (2018) <doi:10.3150/16-BEJ887> and the multiscale algorithmic extension from Cho and Kirch (2022) <doi:10.1007/s10463-021-00811-5>, as well as the bootstrap procedure for generating confidence intervals about the locations of change points as proposed in Cho and Kirch (2022) <doi:10.1016/j.csda.2022.107552>. See also Meier, Kirch and Cho (2021) <doi:10.18637/jss.v097.i08> which accompanies the R package.

Last updated 2 years ago

1.78 score 2 stars 1 packages 8 scripts 425 downloads

unsystation - Stationarity Test Based on Unsystematic Sub-Sampling

Performs a test for second-order stationarity of time series based on unsystematic sub-samples.

Last updated 7 years ago

1.00 score 1 scripts 130 downloads

tilting - Variable Selection via Tilted Correlation Screening Algorithm

Implements an algorithm for variable selection in high-dimensional linear regression using the "tilted correlation", a new way of measuring the contribution of each variable to the response which takes into account high correlations among the variables in a data-driven way.

Last updated 8 years ago

1.00 score 7 scripts 120 downloads