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MLAD: Stata module to perform maximum likelihood using automatic differentiation

Paul Lambert ()
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Paul Lambert: University of Leicester

Statistical Software Components from Boston College Department of Economics

Abstract: mlad maximizes a log-likelihood function where the likelihood function is programmed in Python. This enables the gradients and Hessian matrix to be obtained using automatic differentiation and to take advantage of using multiple CPUs. With large datasets mlad tends to be substantially faster than ml and has the important advantage that you don't have to derive the gradients and the Hessian matrix analytically as these are obtained using automatic differentiation.

Language: Stata
Requires: Stata version 16.1
Keywords: maximum likelihood; Python; automatic differentiation (search for similar items in EconPapers)
Date: 2021-08-04, Revised 2024-05-02
Note: This module should be installed from within Stata by typing "ssc install mlad". The module is made available under terms of the GPL v3 (https://www.gnu.org/licenses/gpl-3.0.txt). Windows users should not attempt to download these files with a web browser.
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Downloads: (external link)
http://fmwww.bc.edu/repec/bocode/m/mlad.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/m/mlad_ll.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/m/mladutil.py program code (text/plain)
http://fmwww.bc.edu/repec/bocode/m/mlad.sthlp help file (text/plain)
http://fmwww.bc.edu/repec/bocode/m/mlad_utility.sthlp help file (text/plain)

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