hamilton_filter: Time Series Filtering Using the Hamilton Filter

In deseats: Data-Driven Locally Weighted Regression for Trend and Seasonality in TS

Time Series Filtering Using the Hamilton Filter

Description

A stationary remainder is obtained from a univariate time series using the filter proposed by Hamilton. The filter is capable of estimating the trend together with the seasonality in a series.

Usage

hamilton_filter(yt, h = NULL, p = NULL)

Arguments

yt	a time series object of class ts or an object that can be transformed to that class using as.ts.
h	the backwards time skip for the first regressor; the default is the seasonal period in yt multiplied by 2.
p	the number of regressors; the default is the seasonal period in yt.

Details

Implement the filter by Hamilton (2018) to decompose a time series.

Value

A list with the following elements is returned.

decomp

an object of class "mts" that consists of the decomposed time series data.

ts_name

the object name of the initially provided time series object.

frequency

the frequency of the time series.

regression_output

an object of class "lm", i.e. basic regression output.

References

Hamilton, J. D. (2018). Why You Should Never Use the Hodrick-Prescott Filter. The Review of Economics and Statistics, 100(5): 831–843. DOI: 10.1162/rest_a_00706.

Examples

est <- hamilton_filter(log(EXPENDITURES))
est

deseats documentation built on Sept. 11, 2024, 8:24 p.m.