fmbqml 0.1.1

Structural-break detection in high-dimensional factor models using QML methods.

fmbqml: Structural Break Analysis in High-Dimensional Factor Models

fmbqml is a Python package for quasi-maximum-likelihood analysis of structural breaks in high-dimensional factor models. It provides tools for single-break testing and estimation, joint multiple-break estimation, sup-LR testing, factor-number selection, break-type classification, and diagnostic plots.

Links

• Source code
• Issue tracker

Installation

Install the package from PyPI with:

python -m pip install fmbqml

For development from a source checkout, use:

python -m pip install -e .

Input data

The input data should be a finite two-dimensional NumPy array or pandas DataFrame with observations in rows and variables in columns. Missing values should be imputed before constructing a model.

Single-break analysis

Assuming that panel_data contains the input panel dataset:

from fmbqml import SingleBreakQML

model = SingleBreakQML(
    panel_data,
    trim_ratio=0.15,
    max_factors=10,
    factor_criterion="IC2",
)

result = model.lr_test(
    alpha=0.05,
    classify=True,
    n_sim=5000,
    random_state=12345,
)

print(result["reject_null"])
print(result["break_point"])
print(result.get("break_type"))

Use estimate_breakpoint() when a break is assumed to exist and significance testing is not required. In lr_test(), break_point is returned when the no-break null is rejected, while estimated_break_candidate records the profile optimizer.

Multiple-break analysis

from fmbqml import MultiBreakQML

model = MultiBreakQML(
    panel_data,
    max_break=5,
    max_factors=8,
    factor_criterion="IC2",
    trim_ratio=0.10,
)

result = model.estimate_breaks_jointly(
    min_break=0,
    classify=True,
)

print(result["n_breaks"])
print(result["breakpoints"])
print(result.get("break_types"))

The option min_break=0 permits selection of a no-break model. Use joint_sup_lr_test(n_breaks=...) to test no structural change against an exact number of breaks.

Factor-number selection

factor_criterion accepts IC1, IC2, IC3, PC1, PC2, PC3, AIC3, or BIC3. Integer indices 0 through 7 are also accepted in that order. The argument max_factors is a search bound and should be smaller than min(T, N).

Results and model state

Result dictionaries contain NumPy arrays for available profiles and nested structures for breakpoint classifications and information-criterion paths. For example, single-break procedures store candidate break locations together with LR or QML profile values, while multiple-break joint estimation stores the selected breakpoints and the information-criterion path used to choose the number of breaks.

Public result methods return independent copies of the cached results, so changing a returned object does not modify the model's internal state. Calling set_data() or set_parameters() validates the new values and clears previous estimates. Retrieve results with get_test_results(), get_joint_results(), or get_joint_test_results() after running the corresponding procedure.

Reproducibility

All stochastic procedures accept an explicit random seed. For reproducible critical values, pass random_state to lr_test(), joint_sup_lr_test(), and profile plotting routines that simulate critical values.

Example scripts demonstrating the main functionality of fmbqml are available in the project repository:

• Examples

Dependencies

fmbqml depends on NumPy, SciPy, pandas, matplotlib, and joblib. Python 3.8 or newer is required.

License

fmbqml is distributed under the MIT License. See LICENSE.txt.