adaptive.lasso: Hybrid Approach to Bridge Change Point Model with Fixed...

In soichiroy/BridgeChange: Bayesian Change-point models with Bridge prior for analyzing time-series and panel data.

Description

Hybrid Approach to Bridge Change Point Model with Fixed Effect

Usage

adaptive.lasso(y1, x1, beta.hat)

Arguments

fomula	Inherited from lm. For example, Y ~ X + Z.
data	Data.frame object.
index	String vector for unit and time index variables. For example, index = c("unit", "year").
model	Model (c("within","between", "pooling")).
effect	Effect (c("individual", "time", "twoways")).
standardize	If TRUE, all covariates are standardized.
interaction	If interaction = 1, no interaciton. If interaction = 2, only two-way interaciton. Interaction can be up to K, which is the rank of the model matrix.
n.break	Number of breaks. If n.break = 0, it simply runs fixed effect model with shrinkage prior on coefficients.
ols.weight	If TRUE, OLS estimates are used for adpative lasso weight vector.
mcmc	MCMC iteration.
burn	Burn-in period.
verbose	Verbose.
sparse.only	If TRUE, skip MCMC and fit a sparse regressiong using adaptive lasso. This works only for n.break=0.
thin	Thinning.
c0	Hyperparam
d0	= 0.1
nu.shape	=2.0
nu.rate	=2.0
alpha	= 1

Author(s)

Jong Hee Park, and Soichiro Yamauchi [e-mail]

soichiroy/BridgeChange documentation built on Feb. 14, 2022, 11:49 p.m.