View source: R/regression_models.R

Constrained least squares | R Documentation |

Constrained least squares.

```
cls(y, x, R, ca)
```

`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, |

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.

A list including:

`bols` |
The OLS (Ordinary Least Squares) beta coefficients. |

`bcls` |
The CLS (Constrained Least Squares) beta coefficients. |

Michail Tsagris.

R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.

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

```
gee.reg, bic.regs, ztp.reg
```

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

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