cls: Constrained least squares

In Rfast2: A Collection of Efficient and Extremely Fast R Functions II

Constrained least squares

Description

Constrained least squares.

Usage

cls(y, x, R, ca) 

Arguments

y	The response variables, a numerical vector with observations.
x	A matrix with independent variables, the design matrix.
R	The R vector that contains the values that will multiply the beta coefficients. See details and examples.
ca	The value of the constraint, R^T \beta = c. See details and examples.

Details

This is described in Chapter 8.2 of Hansen (2019). The idea is to inimise the sum of squares of the residuals under the constraint R^T \beta = c. As mentioned above, be careful with the input you give in the x matrix and the R vector.

Value

A list including:

bols	The OLS (Ordinary Least Squares) beta coefficients.
bcls	The CLS (Constrained Least Squares) beta coefficients.

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris [e-mail].

References

Hansen, B. E. (2022). Econometrics, Princeton University Press.

See Also

gee.reg, bic.regs, ztp.reg

Examples

x <- as.matrix( iris[1:50, 1:4] )
y <- rnorm(50)
R <- c(1, 1, 1, 1)
cls(y, x, R, 1)

Rfast2 documentation built on May 29, 2024, 8:45 a.m.