Berndt–Hall–Hall–Hausman algorithm

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The Berndt–Hall–Hall–Hausman (BHHH) algorithm is a numerical optimization algorithm similar to the Gauss–Newton algorithm. It is named after the four originators: Ernst R. Berndt, B. Hall, Robert Hall, and Jerry Hausman.[1]


If a nonlinear model is fitted to the data one often needs to estimate coefficients through optimization. A number of optimisation algorithms have the following general structure. Suppose that the function to be optimized is Q(β). Then the algorithms are iterative, defining a sequence of approximations, βk given by


where is the parameter estimate at step k, and is a parameter (called step size) which partly determines the particular algorithm. For the BHHH algorithm λk is determined by calculations within a given iterative step, involving a line-search until a point βk+1 is found satisfying certain criteria. In addition, for the BHHH algorithm, Q has the form

and A is calculated using

In other cases, e.g. Newton–Raphson, can have other forms. The BHHH algorithm has the advantage that, if certain conditions apply, convergence of the iterative procedure is guaranteed.[citation needed]

See also[edit]


  1. ^ Berndt, E.; Hall, B.; Hall, R.; Hausman, J. (1974). "Estimation and Inference in Nonlinear Structural Models" (PDF). Annals of Economic and Social Measurement. 3 (4): 653–665. 

Further reading[edit]

  • Gill, P.; Murray, W.; Wright, M. (1981). Practical Optimization. London: Harcourt Brace. 
  • Harvey, A. C. (1990). The Econometric Analysis of Time Series (Second ed.). Cambridge: MIT Press. pp. 137–138. ISBN 0-262-08189-X. 
  • Luenberger, D. (1972). Introduction to Linear and Nonlinear Programming. Reading, Massachusetts: Addison Wesley. 
  • Sokolov, S. N.; Silin, I. N. (1962). "Determination of the coordinates of the minima of functionals by the linearization method". Joint Institute for Nuclear Research preprint D-810, Dubna.