Inf_criteria

Returns the Akaike and Bayesian information criteria.

internal

Adds the MIxing-Data Sampling (MIDAS, Ghysels et al. (2007) <doi:10.1080/07474930600972467>) components to a variety of GARCH and MEM (Engle (2002) <doi:10.1002/jae.683>, Engle and Gallo (2006) <doi:10.1016/j.jeconom.2005.01.018>, and Amendola et al. (2024) <doi:10.1016/j.seps.2023.101764>) models, with the aim of predicting the volatility with additional low-frequency (that is, MIDAS) terms. The estimation takes place through simple functions, which provide in-sample and (if present) and out-of-sample evaluations. 'rumidas' also offers a summary tool, which synthesizes the main information of the estimated model. There is also the possibility of generating one-step-ahead and multi-step-ahead forecasts.

Vincenzo Candila

rumidas

Univariate GARCH-MIDAS, Double-Asymmetric GARCH-MIDAS and
MEM-MIDAS

Inf_criteria function

<dl><dt>est</dt>
<dd>The output of the estimation process.</dd></dl>

Arguments

Information Criteria — Inf_criteria

<dl>

<dt>est</dt>
<dd>The output of the estimation process.</dd>

</dl>

Inf_criteria: Information Criteria

Description

Usage

Value

Arguments