quantileFit¶

Purpose¶

Perform linear quantile regression.

Format¶

qOut = quantileFit(y, x[, tau[, w]][, qCtl])¶

qOut = quantileFit(dataset, formula[, tau[, w]][, qCtl])

Parameters

y (Nx1 vector) – dependent variable.
x (NxK matrix or sparse matrix or N-dimensional array) – independent variables.
dataset (string) – name of dataset.
formula (string) –
formula string of the model.

E.g "y ~ X1 + X2", ‘y’ is the name of dependent variable, ‘X1’ and ‘X2’ are names of independent variables;

E.g "y ~ .", ‘.’ means including all variables except dependent variable ‘y’;

E.g "y ~ -1 + X1 + X2", ‘-1’ means no intercept model.
tau (Mx1 vector) – Optional argument, quantile levels. Default = { 0.05, 0.5, 0.95 };
w (Nx1 vector) – Optional argument, containing weights. Default = uniform weights.

qCtl (struct) –

Optional argument, instance of the qfitControl structure containing the following members:

qCtl.bandwidth	scalar, the multiplicative factor of the bandwidth. Default = 1.
qCtl.varnames	Kx1 string array, variable names. Default = `{"X1", "X2", ..., "XK"}`. The name for the constant will be added automatically if it is included in the model.
qCtl.verbose	scalar, print results Default = 1. 1 Printing on. 0 No printing.
qCtl.const	scalar, include constant in regression. Default = 1. 1 a constant term will be added. 0 no constant term will be added.
qCtl.bootstrap	scalar, number of iterations for bootstrap standard errors and confidence intervals. Default = 0, for no bootstrap.
qCtl.alpha	scalar, alpha values for bootstrap confidence intervals. Ignored if qctl.bootstrap is set to 0.

Returns

qOut (struct) –

instance of qfitOut struct structure:

qOut.beta	Kx1 matrix, quantile parameter estimates.
qOut.u_plus	NxM matrix, positive part of residuals.
qOut.u_minus	NxM matrix, negative part of residuals.
qOut.ci	array, with a 2xK matrix containing bootstrapped lower and upper confidence intervals stored on separate planes for each tau specified.
qOut.se	matrix, with bootstrapped standard errors, stored in separate columns for each tau specified.

Examples¶

new;
cls;

// Set random number generator seed for
// repeatable random numbers
rndseed 4893;

N = 1000;
x = 10*rndu(N, 1) - 5;
y = 5 + 2*X + rndn(rows(x), 1)*10;

// Set up tau for regression
tau = 0.05|0.50|0.75|0.95;

// Call quantileFit
struct qfitOut qOut;
qOut = quantileFit(Y, X, tau);

Source¶

quantilefit.src