gctsc.opts

Creates a control list for simulation and likelihood approximation in the
Gaussian copula model, including the random seed and Monte Carlo settings.

Gaussian copula models for count time series. Includes simulation utilities, likelihood approximation, maximum-likelihood estimation, residual diagnostics, and predictive inference. Implements the Time Series Minimax Exponential Tilting (TMET) method, an adaptation of Minimax Exponential Tilting (Botev, 2017) <doi:10.1111/rssb.12162> and the Vecchia-based tilting framework of Cao and Katzfuss (2025) <doi:10.1080/01621459.2025.2546586>. Also provides a linear-cost implementation of the Geweke–Hajivassiliou–Keane (GHK) simulator inspired by Masarotto and Varin (2012) <doi:10.1214/12-EJS721>, and the Continuous Extension (CE) approximation of Nguyen and De Oliveira (2025) <doi:10.1080/02664763.2025.2498502>. The package follows the S3 structure of 'gcmr', but all code in 'gctsc' was developed independently.

Quynh Nguyen

gctsc

Modeling Count Time Series Data via Gaussian Copula Models

Victor De Oliveira

gctsc.opts function

<dl><dt>seed</dt>
<dd>Integer. Random seed for reproducibility (default: a random integer between 1 and 100000).</dd>
<dt>M</dt>
<dd>Integer. Number of Monte Carlo samples used in the likelihood approximation (default: 1000).</dd>
<dt>...</dt>
<dd>Ignored. Included for S3 method compatibility.</dd></dl>

Arguments

Set Options for Gaussian Copula Time Series Model — gctsc.opts

<dl>

<dt>seed</dt>
<dd>Integer. Random seed for reproducibility (default: a random integer between 1 and 100000).</dd>


<dt>M</dt>
<dd>Integer. Number of Monte Carlo samples used in the likelihood approximation (default: 1000).</dd>


<dt>...</dt>
<dd>Ignored. Included for S3 method compatibility.</dd>

</dl>

gctsc.opts: Set Options for Gaussian Copula Time Series Model

Description

Usage

Value

Arguments