trend_estimate function

Trend estimation with controlled smoothing.

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

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.

Returns

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")