gdpUS

A dataset that contains the (seasonally adjusted) Gross
Domestic Product of the US from the first quarter of 1947 to the second
quarter of 2019

datasets

The nonparametric trend and its derivatives in equidistant time
series (TS) with short-memory stationary errors can be estimated. The
estimation is conducted via local polynomial regression using an
automatically selected bandwidth obtained by a built-in iterative plug-in
algorithm or a bandwidth fixed by the user. A Nadaraya-Watson kernel
smoother is also built-in as a comparison. With version 1.1.0, a linearity
test for the trend function, forecasting methods and backtesting
approaches are implemented as well.
The smoothing methods of the package are described in Feng, Y., Gries, T.,
and Fritz, M. (2020) <doi:10.1080/10485252.2020.1759598>.

Dominik Schulz

smoots

Nonparametric Estimation of the Trend and Its Derivatives in TS

Yuanhua Feng

Sebastian Letmathe

Thomas Gries

Marlon Fritz

gdpUS function

A data frame with 290 rows and 3 variables:<dl>
 <dt>Year</dt>
<dd>the observation year</dd> <dt>Quarter</dt>
<dd>the observation quarter in the given year</dd> <dt>GDP</dt>
<dd>the Gross Domestic Product of the US in billions of chained
 2012 US Dollars (annual rate)</dd>
</dl>

Format

Quarterly US GDP, Q1 1947 to Q2 2019 — gdpUS

A data frame with 290 rows and 3 variables:<dl>
 <dt>Year</dt>
<dd>the observation year</dd>

 <dt>Quarter</dt>
<dd>the observation quarter in the given year</dd>

 <dt>GDP</dt>
<dd>the Gross Domestic Product of the US in billions of chained
 2012 US Dollars (annual rate)</dd>


</dl>

gdpUS: Quarterly US GDP, Q1 1947 to Q2 2019

Description

Usage

Arguments

Format