The adjusted daily closing price of the SPY ETF.

datasets

Unobserved components time series model using the linear innovations state space representation (single source of error) with choice of error distributions and option for dynamic variance. Methods for estimation using automatic differentiation, automatic model selection and ensembling, prediction, filtering, simulation and backtesting. Based on the model described in Hyndman et al (2012) <doi:10.1198/jasa.2011.tm09771>.

Alexios Ghalanos

tsissm

Linear Innovations State Space Unobserved Components Model

Alexios Galanos

spy function

## `spy`
A data.frame containing 7597 observations<dl>
 <dt>date</dt>
<dd>The YYYY-MM-DD date</dd> <dt>spy</dt>
<dd>The adjusted stock closing price.</dd>
</dl>

Format

SPY ETF Adjusted Close — spy

## `spy`
A data.frame containing 7597 observations<dl>
 <dt>date</dt>
<dd>The YYYY-MM-DD date</dd>

 <dt>spy</dt>
<dd>The adjusted stock closing price.</dd>


</dl>

spy: SPY ETF Adjusted Close

Description

Usage

Arguments

Format