Description Usage Arguments Details Value Author(s) References Examples

View source: R/nlVar-methods.R

Log-Likelihood method for VAR models.

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`object` |
object of class |

`...` |
additional arguments to |

The Log-Likelihood is computed as in Luetkepohl (2006) equ. 3.4.5 (p. 89) and Juselius (2006) p. 56:

* LL = -(TK/2) \log(2π) - (T/2) \log|Σ| - (1/2) ∑^{T} ≤ft [
(y_t - A^{'}x_t)^{'} Σ^{-1} (y_t - A^{'}x_t) \right ] *

Where
*Σ* is the Variance matrix of residuals, and *x_t* is the matrix
stacking the regressors (lags and deterministic).

However, we use a computationally simpler version:

* LL = -(TK/2) \log(2π) - (T/2) \log|Σ| - (TK/2) *

See Juselius (2006), p. 57.

(Note that Hamilton (1994) 11.1.10, p. 293 gives *+ (T/2)
\log|Σ^{-1}|*, which is the same as *-(T/2) \log|Σ|)*.

Log-Likelihood value.

Matthieu Stigler

Hamilton (1994) *Time Series Analysis*, Princeton University
Press

Juselius (2006) *The Cointegrated VAR model: methodology and
Applications*, Oxford Univesity Press

Luetkepohl (2006) *New Introduction to Multiple Time Series Analysis*,
Springer

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tsDyn documentation built on June 4, 2018, 1:04 a.m.

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