y=X*b+uwhere y is an
n-vector regressand, X is a [n,k] matrix whose k columns are called
regressors, b is k-vector of regression parameters and u is an n-vector
of error terms or residuals.
The notation is quite standard in literature,
cf eg Davidson and MacKinnon, Econometrics Theory and Methods, 2004.
| Modifier and Type | Method and Description |
|---|---|
| public double | Returns: The double representing the variance of yReturns the variance of the regressand, ie Var(y). |
| public double[] | Returns: The [k,1] array representing bEstimates the regression parameters b. |
| public double[] | Returns: standard errors of estimated regression parametersReturns the standard errors of the regression parameters. |
| public double[][] | Returns: The [k,k] array representing the variance of bEstimates the variance of the regression parameters, ie Var(b). |
| public double[] | Returns: The [n,1] array representing the residualsEstimates the residuals, ie u = y - X*b. |
| estimateRegressandVariance | back to summary |
|---|---|
| public double estimateRegressandVariance() Returns the variance of the regressand, ie Var(y).
| |
| estimateRegressionParameters | back to summary |
|---|---|
| public double[] estimateRegressionParameters() Estimates the regression parameters b.
| |
| estimateRegressionParametersStandardErrors | back to summary |
|---|---|
| public double[] estimateRegressionParametersStandardErrors() Returns the standard errors of the regression parameters.
| |
| estimateRegressionParametersVariance | back to summary |
|---|---|
| public double[][] estimateRegressionParametersVariance() Estimates the variance of the regression parameters, ie Var(b).
| |
| estimateResiduals | back to summary |
|---|---|
| public double[] estimateResiduals() Estimates the residuals, ie u = y - X*b.
| |