Analyze count time series with excess zeros.
Two types of statistical models are supported: Markov regression by Yang et al.
(2013) <doi:10.1016/j.stamet.2013.02.001> and state-space models by Yang et al.
(2015) <doi:10.1177/1471082X14535530>. They are also known as observation-driven and
parameter-driven models respectively in the time series literature. The functions used for
Markov regression or observation-driven models can also be used to fit ordinary regression models
with independent data under the zero-inflated Poisson (ZIP) or zero-inflated negative binomial (ZINB)
assumption. Besides, the package contains some miscellaneous functions to compute density, distribution,
quantile, and generate random numbers from ZIP and ZINB distributions.

Zero-Inflated Models (ZIM) for Count Time Series with Excess
Zeros

package-ubuntu-20.04-r-4.0.5#cran#ZIM-1.1.0

<p>Apply the backshift operator or lag operator to a time series objective.
</p>

<table summary="R argblock">
<tr valign="top">
<td><code>x</code></td>
<td>
<p>univariate or multivariate time series.</p>
</td>
</tr>
<tr valign="top">
<td><code>k</code></td>
<td>
<p>number of lags.</p>
</td>
</tr>
</table>

x <- arima.sim(model = list(ar = 0.8, sd = 0.5), n = 120)
bshift(x, k = 12)

package-ubuntu-20.04-r-4.0.5#cran#ZIM-1.1.0#docs#bshift

bshift

Description

Usage

Arguments

See Also

Examples

ZIM

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`x`	univariate or multivariate time series.
`k`	number of lags.