crossqreg.max.partial

DATA1

DATA2

A vector of probability values at which sample quantiles are estimated

vecA

Kmax

The partial cross-quantilograms from 1 to a given lag order.

Estimation and inference methods for the cross-quantilogram.
The cross-quantilogram is a measure of nonlinear dependence between
two variables, based on either unconditional or conditional quantile
functions. The cross-quantilogram can be considered as an extension
of the correlogram, which is a correlation function over multiple lag
periods and mainly focuses on linear dependency. One can use the
cross-quantilogram to detect the presence of directional
predictability from one time series to another. This package provides
a statistical inference method based on the stationary bootstrap. See
Linton and Whang (2007) <doi:10.1016/j.jeconom.2007.01.004> for
univariate time series analysis and Han, Linton, Oka and Whang (2016)
<doi:10.1016/j.jeconom.2016.03.001> for multivariate time series
analysis.

Tatsushi Oka

quantilogram

Cross-Quantilogram

Heejon Han

Oliver Linton

Yoon-Jae Whang

crossqreg.max.partial function

<dl><dt>DATA1</dt>
<dd>An input matrix (T x p1)</dd>
<dt>DATA2</dt>
<dd>An input matrix (T x p2)</dd>
<dt>vecA</dt>
<dd>A vector of probability values at which sample quantiles are estimated</dd>
<dt>Kmax</dt>
<dd>The maximum lag order (integer)</dd></dl>

Arguments

Heejoon Han, Oliver Linton, Tatsushi Oka and Yoon-Jae Whang

Author

Partial Corss-Quantilogram upto a given lag order — crossqreg.max.partial

<dl>

<dt>DATA1</dt>
<dd>An input matrix (T x p1)</dd>


<dt>DATA2</dt>
<dd>An input matrix (T x p2)</dd>


<dt>vecA</dt>
<dd>A vector of probability values at which sample quantiles are estimated</dd>


<dt>Kmax</dt>
<dd>The maximum lag order (integer)</dd>

</dl>

crossqreg.max.partial: Partial Corss-Quantilogram upto a given lag order

Description

Usage

Value

Arguments

Author

Details

References