Maximum Likelihood MT (maxlikmt)¶

A maximum likelihood package for GAUSS.

Description¶

MaxlikMT provides a suite of flexible, efficient and trusted tools for the solution of the maximum likelihood problem with bounds on the parameters.

Installation¶

Please contact us with to request pricing and installation information.

If you already own maxlikMT, you can use the GAUSS Package Manager to install maxlikMT as well.

Key Features¶

Statistical Inference¶

maxlikmt provides methods for statistical inference of weighted or unweighted, bounded or unbounded maximum likelihood models.

Inverted Hessian covariance matrix
Heteroskedastic-consistent covariance matrix
Wald confidence limits
Likelihood ratio statistics
Bootstrap
Likelihood profile and Profile ‘t’ traces

Descent methods¶

BFGS (Broyden, Fletcher, Goldfarb and Powell)
BHHH (Berndt, Hall, Hall, and Hausman)
DFP (Davidon, Fletcher and Powell)
Newton

Line search methods¶

STEPBT
Brent’s method
Half
Strong Wolfe’s conditions

Advantages¶

Flexible¶

Bounded parameters.
Specify fixed and free parameters.
Dynamic algorithm switching.
Compute all, a subset, or none of the derivatives numerically.
Easily pass data other than the model parameters as extra input arguments. New!
Methods to simply create matrices needed for log-likelihood computation from subsets of the parameters.

Efficient¶

Threaded and thread-safe.
Option to avoid computations that are the same for the log-likelihood function and derivatives.
The tremendous speed of user-defined procedures in GAUSS speeds up your estimation.

Trusted¶

For more than 30 years, leading researchers have trusted the efficient and numerically sound code in the GAUSS maximum likelihood estimation tools to keep them at the forefront of their fields.

Available Optimization Controls¶

Optimization controls are set to default values that few users ever need to change. However, maxlikmt is fully customizable and the flexible optimization options can be a great help when tackling more difficult problems.

Control Options¶

:widths: auto¶
Parameter bounds	Simple parameter bounds of the type: lower_bd ≤ x_i ≤ upper_bd.
Descent algorithms	BFGS, DFP, Newton, and BHHH.
Algorithm switching	Specify descent algorithms to switch between based upon the number of elapsed iterations, a minimum change in the objective function, or line search step size.
Weights	Observation weights.
Covariance matrix type	Compute a ML covariance matrix, a QML covariance matrix, or none.
Alpha	Probability level for statistical tests.
Line search method	STEPBT (quadratic and cubic curve fit), Brent’s method, BHHHStep, half-step or Strong Wolfe’s Conditions.
Active parameters	Control which parameters are active (to be estimated) and which should be fixed to their start value.
Gradient Method	Either compute an analytical gradient, or have maxlikmt compute a numerical gradient using the forward, central, or backwards difference method.
Hessian Method	Either compute an analytical Hessian, or have maxlikmt compute a numerical Hessian using the forward, central, or backwards difference method.
Gradient check	Compares the analytical gradient computed by the user-supplied function with the numerical gradient to check the analytical gradient for correctness.
Random seed	Starting seed value used by the random line search method to allow for repeatable code.
Print output	Controls whether (or how often) iteration output is printed and whether a final report is printed.
Gradient step	Advanced feature: Controls the increment size for computing the step size for numerical first and second derivatives.
Random search radius	The radius of the random search if attempted.
Maximum iterations	Maximum iterations to converge.
Maximum elapsed time	Maximum number of minutes to converge.
Maximum random search attempts	Maximum allowed number of random line search attempts.
Convergence tolerance	Convergence is achieved when the direction vector changes less than this amount.

trainTestSplit

User Guide