Description Usage Arguments Value Examples

This function computes the Single-equation Penalized Error Correction Selector as described in Smeekes and Wijler (2020) based on data that is already in the form of a conditional error-correction model.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 |

`y_d` |
A vector containing the differences of the dependent variable. |

`z_l` |
A matrix containing the lagged levels. |

`w` |
A matrix containing the required difference |

`deterministics` |
Indicates which deterministic variables should be added (0 = none, 1=constant, 2=constant and linear trend). |

`ADL` |
Boolean indicating whether an ADL model without error-correction term should be estimated. Default is FALSE. |

`weights` |
Choice of penalty weights. The weights can be automatically generated by ridge regression (default) or ols. Alternatively, a conformable vector of non-negative weights can be supplied. |

`k_delta` |
The power to which the weights for delta should be raised, if weights are set to "ridge" or "ols". |

`k_pi` |
The power to which the weights for pi should be raised, if weights are set to "ridge" or "ols". |

`lambda_g` |
An optional user-specified grid for the group penalty may be supplied. If left empty, a 10-dimensional grid containing 0 as the minimum value is generated. |

`lambda_i` |
An optional user-specified grid for the individual penalty may be supplied. If left empty, a 10-dimensional grid containing 0 as the minimum value is generated. |

`thresh` |
The treshold for convergence. |

`max_iter_delta` |
Maximum number of updates for delta. Defaults is 1e5. |

`max_iter_pi` |
Maximum number of updates for pi. Defaults is 1e5. |

`max_iter_gamma` |
Maximum number of updates for gamma. Defaults is 1e5. |

`D` |
A matrix containing the deterministic variables included in the model. |

`gammas` |
A matrix containing the estimated coefficients of the stochastic variables in the conditional error-correction model. |

`gamma_opt` |
A vector containing the estimated coefficients corresponding to the optimal model. |

`lambda_g` |
The grid of group penalties. |

`lambda_i` |
The grid of individual penalties. |

`theta` |
The estimated coefficients for the constant and trend. If a deterministic component is excluded, its coefficient is set to zero. |

`theta_opt` |
The estimated coefficients for the constant and trend in the optimal model. |

`weights` |
The vector of penalty weights. |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
#Estimate a conditional error-correction model on pre-transformed data with a constant
#Organize data
y <- Unempl_GT[,1]
index_GT <- sample(c(2:ncol(Unempl_GT)),10)
x <- Unempl_GT[,index_GT]
y_d <- y[-1]-y[-100]
z_l <- cbind(y[-100],x[-100,])
w <- x[-1,]-x[-100,] #This w corresponds to a cecm with p=0 lagged differences
my_specs <- specs_tr(y_d,z_l,w,deterministics="constant")
#Estimate an ADL model on pre-transformed data with a constant
my_specs <- specs_tr(y_d,NULL,w,ADL=TRUE,deterministics="constant")
``` |

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