hansen#

Purpose#

Test for stability of all parameters using a cumulative sums of weighted full sample residuals. The test employs the locally best invariant tests in a Lagrange multiplier format.

Format#

{ ny, prob } = hansen(yt, xt, print_out)#

Parameters:

yt (Matrix) – Tx1 numerical vector of panel series data.
xt (Matrix) – TxK numerical matrix of estimation regressors.
print_out (Scalar) – Optional input, 1 to print output to the screen; 0 to suppress output. Default = 1.

Returns:

ny (Matrix) – Hansen test statistic in order: Hansen test for parameter stability, Hansen test for variance constancy, Hansen test for joint stability.
crit (Vector) – 1%, 2.5%, 5%, 7.5%, 10%, and 20% critical values.

Example#

new;
cls;
library tsmt;

// This generates 400 observations of a
// linear time series with a break in the constant
// at observations 120

b1 = { 1.2, -2, 0.75 };
b2 = { 5, -2, 0.75 };

n1 = 120;
n_tot = 400;
xt = ones(n_tot, 1)~rndn(n_tot, 2);
et = rndn(n_tot, 1);

// Create series with break
y1 = xt[1:n1, .]*b1 + et[1:n1, .];
y2 = xt[n1+1:n_tot, .]*b2 + et[n1+1:n_tot, .];
yt_break = y1|y2;

/********************************************/
// Run example including printOut of results
{ ny, crit } = hansen(yt_break, xt);

Test:                                   Hansen-Nyblom (1989)
Test Variable:                                            Y1
Timespan:                                            Unknown
Ho:                                                Stability
Model:                                  No constant or trend
N. Obs:                                                  400
============================================================

Beta1                                                 21.998
Beta2                                                  0.263
Beta3                                                  0.091
Variance                                              11.161
Joint                                                 -9.182

Critical Values:
                            1%             5%            10%
                         2.120          1.680          1.490
============================================================

Reject the null hypothesis of constancy of Beta1 at the 1% level.

Cannot reject the null hypothesis of constancy of Beta2.

Cannot reject the null hypothesis of constancy of Beta3.

Reject the null hypothesis of constancy of variance at the 1% level.

Reject the null hypothesis of joint constancy of all parameters at the 1% level.

Reference#

Nyblom, J. (1989). Testing for the constancy of parameters over time, Journal of American Statistical Association, 84(405), 223-230.
Hansen, B.E. (1992). Testing for parameter instability in linear models, Journal of Policy Modeling, 14(4): 517-533.

Library#

tsmt

Source#

hansen.src