tvAR

Estimate time-varying coefficient AR models.

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

tvAR function

<dl><dt>x</dt>
<dd>a time series of data.</dd>
<dt>lags</dt>
<dd>the lagged variables used, e.g. lags=c(1,3) means lag-1 and lag-3 are used as regressors.
It is more flexible than specifying an order.</dd>
<dt>include.mean</dt>
<dd>a logical value indicating whether the constant terms are included.</dd></dl>

Arguments

Estimate Time-Varying Coefficient AR Models — tvAR

<dl>

<dt>x</dt>
<dd>a time series of data.</dd>


<dt>lags</dt>
<dd>the lagged variables used, e.g. lags=c(1,3) means lag-1 and lag-3 are used as regressors.
It is more flexible than specifying an order.</dd>


<dt>include.mean</dt>
<dd>a logical value indicating whether the constant terms are included.</dd>

</dl>

tvAR: Estimate Time-Varying Coefficient AR Models

Description

Usage

Value

Arguments

Examples