us_retail_sales

The dataset represents the monthly, non-seasonally adjusted advance estimate
of sales from the retail trade based on data from a subsample of firms 
from the larger Monthly Retail Trade Survey.

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

us_retail_sales function

## `us_retail_sales`
A data.frame containing 398 observations<dl>
 <dt>date</dt>
<dd>The YYYY-MM-DD date</dd> <dt>co2</dt>
<dd>The sales value in millions of dollars.</dd>
</dl>

Format

Advance Retail Sales: Retail Trade (US) — us_retail_sales

## `us_retail_sales`
A data.frame containing 398 observations<dl>
 <dt>date</dt>
<dd>The YYYY-MM-DD date</dd>

 <dt>co2</dt>
<dd>The sales value in millions of dollars.</dd>


</dl>

us_retail_sales: Advance Retail Sales: Retail Trade (US)

Description

Usage

Arguments

Format