fix.pacf.test

fix.pacf.test() generates a test of lags for AR Approximations.

We provide functions for estimation and inference of locally-stationary time series using the sieve methods and bootstrapping procedure. In addition, it also contains functions to generate Daubechies and Coiflet wavelet by Cascade algorithm and to process data visualization.

Xiucai Ding

Sie2nts

Sieve Methods for Non-Stationary Time Series

Chen Qian

fix.pacf.test function

<dl><dt>ts</dt>
<dd>ts is the data set which is a time series data typically</dd>
<dt>c</dt>
<dd>c indicates the number of basis used to estimate (For wavelet, the number of basis is 2^c. If
Cspli is chosen, the real number of basis is c-2+or)</dd>
<dt>type</dt>
<dd>type indicates which type of basis is used. There are 31 types in this package</dd>
<dt>or</dt>
<dd>or indicates the order of spline and only used in Cspli type, default is 4 which indicates cubic spline</dd>
<dt>lag</dt>
<dd>lag determine the lag of AR Approximations.The default is 3</dd>
<dt>b</dt>
<dd>the largest lag for auto-regressive model, the default value is 8, this parameter must be
larger than lag</dd>
<dt>B.s</dt>
<dd>the number of statistics used in multiplier bootstrap, the default value is 1000</dd>
<dt>m</dt>
<dd>the number of window size used in multiplier bootstrap, the default value is 0 which uses the
minimum volatility method to determine the number</dd></dl>

Arguments

Testing Lag of Auto-Regressive (AR) Model — fix.pacf.test

<dl>

<dt>ts</dt>
<dd>ts is the data set which is a time series data typically</dd>


<dt>c</dt>
<dd>c indicates the number of basis used to estimate (For wavelet, the number of basis is 2^c. If
Cspli is chosen, the real number of basis is c-2+or)</dd>


<dt>type</dt>
<dd>type indicates which type of basis is used. There are 31 types in this package</dd>


<dt>or</dt>
<dd>or indicates the order of spline and only used in Cspli type, default is 4 which indicates cubic spline</dd>


<dt>lag</dt>
<dd>lag determine the lag of AR Approximations.The default is 3</dd>


<dt>b</dt>
<dd>the largest lag for auto-regressive model, the default value is 8, this parameter must be
larger than lag</dd>


<dt>B.s</dt>
<dd>the number of statistics used in multiplier bootstrap, the default value is 1000</dd>


<dt>m</dt>
<dd>the number of window size used in multiplier bootstrap, the default value is 0 which uses the
minimum volatility method to determine the number</dd>

</dl>

fix.pacf.test: Testing Lag of Auto-Regressive (AR) Model

Description

Usage

Value

Arguments