Version: January 26 Estimating complex production functions: The importance of starting values Mark Neal Presented at the 51st Australian Agricultural and Resource Economics Society Conference, Queenstown, New Zealand, 14-16 February, 2007. Postdoctoral Research Fellow Risk and Sustainable Management Group University of Queensland Room 519, School of Economics, Colin Clark Building (39) University of Queensland, St Lucia, QLD, 4072 Email:
[email protected] Ph: +61 (0) 7 3365 6601 Fax: +61 (0) 7 3365 7299 http://www.uq.edu.au/rsmg Acknowledgements Chris O’Donnell helpfully provided access to Gauss code that he had written for estimation of latent class models so it could be translated into Shazam. Leighton Brough (UQ), Ariel Liebman (UQ) and Tom Pechey (UMelb) helped by organising a workshop with Nimrod/enFuzion at UQ in June 2006. enFuzion (via Rok Sosic) provided a limited license for use in the project. Leighton Brough, tools coordinator for the ARC Centre for Complex Systems (ACCS), assisted greatly in setting up an enFuzion grid and collating the large volume of results. Son Nghiem provided assistance in collating and analysing data as well as in creating some of the figures. Page 1 of 30 Version: January 26 ABSTRACT Production functions that take into account uncertainty can be empirically estimated by taking a state contingent view of the world. Where there is no a priori information to allocate data amongst a small number of states, the estimation may be carried out with finite mixtures model. The complexity of the estimation almost guarantees a large number of local maxima for the likelihood function.