regsc_rt {regsc}R Documentation

Estimate the regression with structural changes by the group fused Lasso

Description

Estimate the regression with structural changes, using a rule of thumb to determine the tuning parameter on the group-fused-Lasso penalty.

Usage

regsc_rt(y,x,z=numeric(0),h=1,weight=rep(1,length(y)-1),XTol=1e-6,maxIter=1000)

Arguments

y

An n-by-1 numeric matrix (vector), the dependent variable

x

An n-by-p numeric matrix, the regressors with possibly time-varying effect on y

z

An optional n-by-q numeric matrix, the regressors with time-invariant effect on y

h

An optional positive number, used in the determination of break dates.

weight

An optional (n-1)-by-1 numeric vector

XTol

An optional small number, the level of error tolerance

maxIter

An optional integer, the maximum number of iterations allowed

Value

A list containing the following components:

regime

a (m+2)-by-1 numeric vector, containing {1,T1,...,Tm,n+1}, where T1,...,Tm are m breaks

alpha

a (p*(m+1)+q)-by-1 vector of estimated coefficients

Sigma

the estimated covariance matrix for alpha

ssr

the sum of squared residuals

R2

the overall goodness-of-fit

resid

an n-by-1 vector of the fitted residual

lambda

the tuning parameter selected either by the information criterion (Qian & Su, 2016) or the rule of thumb

Author(s)

Junhui Qian and Liangjun Su

References

Qian, J., L. Su, 2016, "Shrinkage estimation of regression models with multiple structural changes", Econometric Theory, 32 (6), 1376-1433.

See Also

regsc


[Package regsc version 0.3 Index]