tsmltest

The function `<code>tsmltest</code>` applies the McLeod-Li test to examine whether ARCH effect exists in the squared residuals of an ARIMA model.

Fundamental time series forecasting models such as autoregressive integrated moving average (ARIMA), exponential smoothing, and simple moving average are included. For ARIMA models, the output follows the traditional parameterisation by Box and Jenkins (1970, ISBN: 0816210942, 9780816210947). Furthermore, there are functions for detailed time series exploration and decomposition, respectively. All data and result visualisations are generated by 'ggplot2' instead of conventional R graphical output. For more details regarding the theoretical background of the models see Hyndman, R.J. and Athanasopoulos, G. (2021) <https://otexts.com/fpp3/>.

Ka Karl Wu

tsforecast

Time Series Forecasting Functions

Ka Yui Karl Wu

tsmltest function

<dl><dt>object</dt>
<dd>a univariate time series object or a numeric vector or matrix.</dd>
<dt>lag.max</dt>
<dd>maximum lag at which to examine the ARCH effect. Default is <code>NULL</code>. Will be automatically calculated using the formula \(10\cdot \log_{10}(n)\), where \(n\) is the series length, if the parameter is omitted.</dd></dl>

Arguments

Author

McLeod-Li Test for ARCH Effect — tsmltest

<dl>

<dt>object</dt>
<dd>a univariate time series object or a numeric vector or matrix.</dd>


<dt>lag.max</dt>
<dd>maximum lag at which to examine the ARCH effect. Default is <code>NULL</code>. Will be automatically calculated using the formula \(10\cdot \log_{10}(n)\), where \(n\) is the series length, if the parameter is omitted.</dd>

</dl>

tsmltest: McLeod-Li Test for ARCH Effect

Description

Usage

Value

Arguments

Author

References

Examples