Skip to main content Accessibility help
×
Hostname: page-component-848d4c4894-nr4z6 Total loading time: 0 Render date: 2024-04-30T16:57:53.085Z Has data issue: false hasContentIssue false

10 - Using information on the moments of disturbances to increase the efficiency of estimation

Published online by Cambridge University Press:  05 June 2012

Thomas E. MacUrdy
Affiliation:
Stanford University
Cheng Hsiao
Affiliation:
University of Southern California
Kimio Morimune
Affiliation:
Kyoto University, Japan
James L. Powell
Affiliation:
University of California, Berkeley
Get access

Summary

Foreword

This chapter was originally written in 1981, with a draft distributed as a National Bureau of Economic Research Working paper (No. 2, May 1982). Although this study was never publicly disseminated in another form, several articles published in the 1980s and 1990s cited and built upon its findings. No paper of mine better reflects just how much Takeshi has influenced my education and research. It is a sentimental favorite of mine for this reason and, thus, is an especially fitting offering for my contribution to Takeshi's Festschrift. The chapter that follows – a modest revision of the NBER Working paper completed in June 1982 – is the version I have sent upon receiving requests for the paper. To preserve its authenticity, the following text incorporates nothing more than editorial changes.

Subsequent work has, of course, expanded and refined many of the results presented in this 1982 paper. The Afterword at the end of this chapter summarizes these developments, attempting to place the 1982 findings presented here into the context of the current literature.

Finally, an appendix follows the Afterword summarizing the results of a small Monte Carlo analysis that investigates the practical applicability of the estimation approach proposed in this chapter. The absence of such evidence made the results of the original work of theoretical interest, but of little functional use. Contrary to many researchers' suspicions, these Monte Carlo findings suggest that substantial efficiency gains may be attainable by implementing these estimation procedures.

Type
Chapter
Information
Nonlinear Statistical Modeling
Proceedings of the Thirteenth International Symposium in Economic Theory and Econometrics: Essays in Honor of Takeshi Amemiya
, pp. 281 - 320
Publisher: Cambridge University Press
Print publication year: 2001

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×