RecursiveLSResults.plot_cusum_squares()

statsmodels.regression.recursive_ls.RecursiveLSResults.plot_cusum_squares

RecursiveLSResults.plot_cusum_squares(alpha=0.05, legend_loc='upper left', fig=None, figsize=None) [source]

Plot the CUSUM of squares statistic and significance bounds.

Parameters:

alpha : float, optional The plotted significance bounds are alpha %. legend_loc : string, optional The location of the legend in the plot. Default is upper left. fig : Matplotlib Figure instance, optional If given, subplots are created in this figure instead of in a new figure. Note that the grid will be created in the provided figure using fig.add_subplot(). figsize : tuple, optional If a figure is created, this argument allows specifying a size. The tuple is (width, height).

Notes

Evidence of parameter instability may be found if the CUSUM of squares statistic moves out of the significance bounds.

Critical values used in creating the significance bounds are computed using the approximate formula of [R47].

References

[R46]

Brown, R. L., J. Durbin, and J. M. Evans. 1975. “Techniques for Testing the Constancy of Regression Relationships over Time.” Journal of the Royal Statistical Society. Series B (Methodological) 37 (2): 149-92.

[R47]

(1, 2) Edgerton, David, and Curt Wells. 1994. “Critical Values for the Cusumsq Statistic in Medium and Large Sized Samples.” Oxford Bulletin of Economics and Statistics 56 (3): 355-65.