Data Smoothing#

Function	Description
`movingave()`	Computes moving average of a series.
`movingaveExpWgt()`	Computes exponentially weighted moving average of a series.
`movingaveWgt()`	Computes weighted moving average of a series
`loessmt()`	Computes coefficients of locally weighted regression.
`curve()`	Computes a one-dimensional smoothing curve.
`spline()`	Computes a two-dimensional interpolatory spline.

Finding moving averages#

Three procedures are available for computing moving averages.

The movingave() procedure computes the moving average given a specified order of moving average.
The movingaveWgt() procedure computes the weighted moving average given a specified order and weights.
The movingaveExpWgt() procedure computes exponentially weighted moving average of a series given a specified order of moving average and a smoothing coefficient.

Example: Smoothing a random walk series#

// Load data
fname = getGAUSSHome("examples/tbill_3mo.xlsx");
y = loadd(fname, "date($obs_date, '%m/%d/%Y %T.%L') + tbill_3m");

// Find 3 month moving average
twentyMA = movingave(y[., "tbill_3m"], 3);

// Find 3 month exponenetial moving average
twentyExpWgtMA = movingaveExpwgt(y[., "tbill_3m"], 3, 0.8);

Locally weighted linear regression smoothing#

The loessmt() procedure smooths data using locally weighted linear regression. Because it relies on linear regression, the function requires both a dependent variable to be smoothed and a matrix of independent variables to be used in the weighted regression.

Example: Lowess smoother#

// Load dataset
fname = getGAUSSHome("examples/lowess1.dta");
data = loadd(fname, "h1 + depth");

// Define independent variable
depvar = data[., "h1"];

// Defined dependent variable
indvars = data[., "depth"];

{ yhat, ys, xs } = loessmt(depvar, indvars);