trend_estimate: Trend estimation with controlled smoothing.

In TSsmoothing: Trend Estimation of Univariate and Bivariate Time Series with Controlled Smoothness

Description

This is the main function that estimates the trend for univariate or bivariate time series for a specified smoothing level.

Usage

trend_estimate(dat, smoothing_level = NULL, lambda = NULL,
  plot = TRUE, label = time(dat), jump = NULL, las = 2,
  bands = TRUE)

Arguments

dat	is a 2x2 matrix with the two time series. Each column correspond to the values at a given time.
smoothing_level	is a scalar between 0 and 1 that specifies the smoothing of the resulting time series tau.
lambda	Alternative, the function directly accepts the lambda value that corresponds to the desired smoothing level.
plot	is TRUE when we cant to plot of the original agaist the resulting series.
label	vector of characters that corresponds to the labels for each time point in the serie.
jump	is a vector of integers that specifies which values of labels should appear in the x labels.
las	is 1(2) if the x labels should be vertical (horizontal).
bands	is TRUE tolo include 95% confidence bands in the plots.

Value

The smoothed series tau.

The orginal data dat.

The estimation for sigma_eta, sigma.eta

The length of the time series N.

The lambda value corresponding to the smoothing level.

The diagonal values of the estimated variance of tau, diag.var.tau

A flag that indicates if data is a bivariate time series.

Examples

# Employment in agriculture (\% of total employment) (modeled ILO estimate) in OCDE members
data(emp_agr) #It is a ts object with one single time series
sts<-trend_estimate(emp_agr,0.70)
plot_trend(sts, title="Employment in agriculture in OCDE members", xlab = "Years")

# Data Trade (\% of GDP) for USA and Mexico downloaded from
data(trade) #It is a numeric matrix with two columns
sts<-trend_estimate(trade,0.7)
plot_trend(sts, title="Trade in% of GDP",xlab="years")

ts_trade<-ts(trade, start=1969,end=2017) #We transform tade to a ts object
sts<-trend_estimate(ts_trade,0.7)
plot_trend(sts, title="Trade in% of GDP",xlab="years")

TSsmoothing documentation built on July 15, 2019, 5:01 p.m.