Data Smoothing¶
Function |
Description |
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|---|---|---|
Computes moving average of a series. |
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Computes exponentially weighted moving average of a series. |
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Computes weighted moving average of a series |
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Computes coefficients of locally weighted regression. |
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Computes a one-dimensional smoothing curve. |
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Computes a two-dimensional interpolatory spline. |
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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);