Skip to content
logo
arch v7.2.1 (+35)
arch.univariate.GeneralizedError.constraints
Initializing search
    arch
    arch
    • Univariate Volatility Models
      • Introduction
      • Examples
      • Forecasting
      • Volatility Forecasting
      • Value-at-Risk Forecasting
      • Forecasting Scenarios
      • Forecasting with Exogenous Variables
      • Mean Models
      • Volatility Processes
      • Using the Fixed Variance Process
      • Distributions
        • arch.univariate.Normal
        • arch.univariate.StudentsT
        • arch.univariate.SkewStudent
        • arch.univariate.GeneralizedError
          • C arch.univariate.GeneralizedError
            • arch.univariate.GeneralizedError.bounds
            • arch.univariate.GeneralizedError.cdf
            • arch.univariate.GeneralizedError.constraints
              • M GeneralizedError.constraints
                • Returns
            • arch.univariate.GeneralizedError.loglikelihood
            • arch.univariate.GeneralizedError.moment
            • arch.univariate.GeneralizedError.parameter_names
            • arch.univariate.GeneralizedError.partial_moment
            • arch.univariate.GeneralizedError.ppf
            • arch.univariate.GeneralizedError.simulate
            • arch.univariate.GeneralizedError.starting_values
            • arch.univariate.GeneralizedError.generator
            • arch.univariate.GeneralizedError.name
            • arch.univariate.GeneralizedError.random_state
        • Writing New Distributions
      • Results
      • Utilities
      • Background and References
    • Bootstrapping
    • Multiple Comparison Problems
    • Unit Root Testing
    • Cointegration Analysis
    • Long-run Covariance Estimation
    • API Reference
    • Common Type Definitions
    • Change Log
    • M GeneralizedError.constraints
      • Returns

    arch.univariate.GeneralizedError.constraints¶

    GeneralizedError.constraints() → tuple[ndarray[tuple[int, ...], dtype[float64]], ndarray[tuple[int, ...], dtype[float64]]][source]¶

    Construct arrays to use in constrained optimization.

    Returns:¶

    • A (numpy.ndarray) – Constraint loadings

    • b (numpy.ndarray) – Constraint values

    Notes

    Parameters satisfy the constraints A.dot(parameters)-b >= 0

    © Copyright 2021, Kevin Sheppard.
    Created using Sphinx 8.1.3. and Sphinx-Immaterial