fpostest {regsc}R Documentation

Post-Lasso estimation of the regression with structural changes

Description

After estimating the set of break dates, fpostest estimates the regression using the least squares. The procedure also works for the case where there is no structural change in the model.

Usage

fpostest(y,x,regime,z)

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

regime

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

z

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

Value

A list containing the following components:

alpha

a (p*(m+1)+q)-by-1 vector of estimated coefficients.The first p*(m+1) elements of alpha correspond to the time-varying effects of x on y. For example, alpha[1:p] is the estimated coefficient on x in the first regime.

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

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

Examples

n=120;p=2;q=3;sigma=0.5

x=rnorm(n*p)
dim(x)<-c(n,p)
z=rnorm(n*q)
dim(z)<-c(n,q)

beta0=c(rep(1,n/2),rep(0.5,n/2))
beta0=rep(beta0,p)
dim(beta0)<-c(n,p)
gamma0=rep(1,q)
dim(gamma0)<-c(q,1)

y = rowSums(x*beta0) + z %*% gamma0 + sigma*rnorm(n)

regime=c(1,61,121)
res=fpostest(y,x,regime,z)

[Package regsc version 0.3 Index]