hfDummy

Create dummy variables for high-frequency intraday seasonality.

Simulation, estimation, prediction procedure, and model identification methods for nonlinear time series analysis, including threshold autoregressive models, Markov-switching models, convolutional functional autoregressive models, nonlinearity tests, Kalman filters and various sequential Monte Carlo methods. More examples and details about this package can be found in the book "Nonlinear Time Series Analysis" by Ruey S. Tsay and Rong Chen, John Wiley & Sons, 2018 (ISBN: 978-1-119-26407-1).

Xialu Liu

Nonlinear Time Series Analysis

Ruey Tsay

Rong Chen

hfDummy function

<dl><dt>int</dt>
<dd>length of time interval in minutes.</dd>
<dt>Fopen</dt>
<dd>number of dummies/intervals from the market open.</dd>
<dt>Tend</dt>
<dd>number of dummies/intervals to the market close.</dd>
<dt>days</dt>
<dd>number of trading days in the data.</dd>
<dt>pooled</dt>
<dd>a logical value indicating whether the data are pooled.</dd>
<dt>skipmin</dt>
<dd>the number of minites omitted from the opening.</dd></dl>

Arguments

Create Dummy Variables for High-Frequency Intraday Seasonality — hfDummy

<dl>

<dt>int</dt>
<dd>length of time interval in minutes.</dd>


<dt>Fopen</dt>
<dd>number of dummies/intervals from the market open.</dd>


<dt>Tend</dt>
<dd>number of dummies/intervals to the market close.</dd>


<dt>days</dt>
<dd>number of trading days in the data.</dd>


<dt>pooled</dt>
<dd>a logical value indicating whether the data are pooled.</dd>


<dt>skipmin</dt>
<dd>the number of minites omitted from the opening.</dd>

</dl>

hfDummy: Create Dummy Variables for High-Frequency Intraday Seasonality

Description

Usage

Arguments

Examples