regsc_ic {regsc} | R Documentation |
Estimate the regression with structural changes, using information criterion (IC) to determine the tuning parameter on the group-fused-Lasso penalty.
regsc_ic(y,x,z=numeric(0),S=20,h=1,weight=rep(1,length(y)-1),XTol=1e-6,maxIter=1000)
y |
An |
x |
An |
z |
An optional n-by-q numeric matrix, the regressors with time-invariant effect on |
S |
An optional positive integer, the number of grids for the search for the optimal tuning parameter. |
h |
An optional positive number, used in the determination of break dates. |
weight |
An optional |
XTol |
An optional small number, the level of error tolerance |
maxIter |
An optional integer, the maximum number of iterations allowed |
A list of post-Lasso estimation result: regime
,alpha
, Sigma
, ssr
, R2
, resid
, lambda
, L
, IC
, K
, listRegime
.
regime |
a |
alpha |
a |
Sigma |
the estimated covariance matrix for |
ssr |
the sum of squared residuals |
R2 |
the overall goodness-of-fit |
resid |
an |
lambda |
the selected tuning parameter that minimizes IC. |
L |
a numeric vector in ascending order, each element of which is a candidate for the tuning parameter |
IC |
a numeric vector of calculated information criteria corresponding to different values of the tuning parameter in |
K |
a vector of integers, each of which is the number of breaks corresponding to different values of the tuning parameter in |
listRegime |
a list of vectors, each of which is the estimated |
Note that the repetitive elements of L
, IC
, K
, listRegime
are deleted.
Junhui Qian and Liangjun Su
Qian, J., L. Su, 2016, "Shrinkage estimation of regression models with multiple structural changes", Econometric Theory, 32 (6), 1376-1433.