cultivars.diagnostics.breaks#
Structural-break tests: whether one regression held over the whole sample.
A break at a known date is a Chow test and needs nothing here. The
tests in this module are for the case that matters in practice, a break
at a date the data must locate, where the break date is a nuisance
parameter present only under the alternative and the usual chi-squared
limit does not apply. sup_wald() scans the trimmed window
\([\pi_0 T, (1 - \pi_0) T]\), computes at each candidate date the
Wald statistic for equality of the coefficients on either side, and
reports the largest (Andrews 1993) together with the exponential and
average functionals of Andrews and Ploberger (1994), all three of which
converge under the null to functionals of
with \(BB_q\) a \(q\)-dimensional Brownian bridge. That limit is
simulated here at the trimming actually used, so the p-values are the
asymptotic ones for the window rather than an interpolation of a printed
table. bai_perron() extends the search to several breaks with the
dynamic-programming global minimizer of Bai and Perron (2003), which
tables the residual sum of every admissible segment once and finds the
best partition for each count from that table; it selects the number of
breaks by BIC, by the Liu-Wu-Zidek criterion, or by the sequential
\(\sup F(l + 1 \mid l)\) procedure whose p-values follow from the
single-break limit as \(1 - G(x)^{l + 1}\), and reports every
partition considered. cusum() is the recursive-residual diagnostic
of Brown, Durbin and Evans (1975): the standardized one-step forecast
errors of the regression re-estimated as each observation arrives are
cumulated and compared with a boundary, and a path that crosses says the
coefficients drifted, without a date to name; the CUSUM of squares does
the same for the variance, with its finite-sample boundary simulated
exactly.
Two commitments shape the surface. First, the record shows its evidence.
A BreakTest carries the Wald path over candidate dates or the
CUSUM path with its boundary, and a MultipleBreakTest carries
the partition, residual sum, and both criteria for every count together
with each sequential test taken, so a verdict can be read against the
shape that produced it and a selection against the alternatives it beat.
Second, the null distribution is simulated, not tabulated. Every p-value
in the module comes from draws of the limit process, or of the exact
finite-sample path for the CUSUM of squares, at the trimming and sample
size in hand; the one exception is the CUSUM proper, whose boundary
multipliers are Brown, Durbin and Evans’ and whose record therefore
carries critical values without a p-value.
Each function takes a target and, optionally, regressors; with none, the
regression is on the deterministic terms alone, so the test is for a
break in the mean or trend. Every coefficient is allowed to break at
once, and serially correlated or heteroskedastic errors are not
corrected for: include the lags the dynamics call for in exog, or
read the result as a diagnostic rather than a test.
Layout. BreakTest is the single-break record, a
_TabulatedTest that reads its statistic
against simulated or tabulated critical values and carries the path;
MultipleBreakTest is the Bai-Perron record with its own summary
table over counts. sup_wald(), bai_perron(), and
cusum() are the producers. The numerics live in _core:
_bridge_functionals simulates the sup, exp, and ave functionals of
the Brownian-bridge limit and _simulated_critical_values turns the
draws into a p-value and critical values; _segment_ssr tables the
segment residual sums and _bai_perron_partition runs the dynamic
program over them; _recursive_residuals produces the standardized
forecast errors, _cusum_squares_quantiles simulates the
finite-sample boundary, and _CUSUM_BOUNDARY holds the tabulated
multipliers; _validate_regression assembles the design from the
deterministic terms and the regressors.
References
Andrews, D. W. K. (1993). Tests for parameter instability and structural change with unknown change point. Econometrica, 61(4), 821-856.
Andrews, D. W. K., & Ploberger, W. (1994). Optimal tests when a nuisance parameter is present only under the alternative. Econometrica, 62(6), 1383-1414.
Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47-78.
Bai, J., & Perron, P. (2003). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18(1), 1-22.
Brown, R. L., Durbin, J., & Evans, J. M. (1975). Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society B, 37(2), 149-192.
Perron, P. (2006). Dealing with structural breaks. In Palgrave Handbook of Econometrics, Volume 1: Econometric Theory (pp. 278-352). Palgrave Macmillan.
Example
A mean shift at observation 100: the sup-Wald dates it, the Bai-Perron selection counts it, and the CUSUM path crosses its band after it:
>>> import numpy as np
>>> rng = np.random.default_rng(0)
>>> y = np.concatenate([rng.standard_normal(100), 2.0 + rng.standard_normal(100)])
>>> test = sup_wald(y, seed=0)
>>> test.reject(), test.break_index
(True, 100)
>>> bai_perron(y, max_breaks=3, seed=0).break_indices
(100,)
>>> cusum(y).reject(alpha=0.05)
True
Classes#
Verdict of a test for one structural break at an unknown date. |
|
Bai and Perron's (1998, 2003) multiple-break analysis of a linear regression. |
Functions#
|
Bai-Perron estimation of multiple breaks in a linear regression. |
|
CUSUM and CUSUM-of-squares tests of coefficient constancy. |
|
Andrews' sup-Wald test for one break at an unknown date, with exp- and ave-Wald. |