Advanced Search
MyIDEAS: Login to save this paper or follow this series

An Evaluation Of Estimators For Censored Systems Of Equations Using Monte Carlo Simulation

Contents:

Author Info

  • Zhao, Yunfei
  • Marsh, Thomas L.
  • Li, Huixin

Abstract

This study makes an empirical comparison of estimators for censored equations using Monte Carlo simulation. The underlying data generation process is rarely known in practice. From the viewpoint of regression, both ordinary censoring rule and sample selection rule are logical rules of censoring. Furthermore, a mixed censoring rule is also possible to govern underlying data generation process. Therefore, it is valuable to examine whether estimators are robust to variations in the assumptions of censoring rules. Five estimators are examined, estimators for ordinary censoring rules include method of simulated scores, Bayesian estimation, and expectation maximization; estimators for sample selection rules include multivariate Heckman two-step method, and Shonkwiler - Yen two-step method. According to our findings, generally a substantial difference exists in the performance of estimators, and hence the choice of estimator appears to be of importance. Apart from difference in performance, estimates from all procedures are reasonably close to estimated parameters.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://purl.umn.edu/129166
Download Restriction: no

Bibliographic Info

Paper provided by Agricultural and Applied Economics Association in its series 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington with number 129166.

as in new window
Length:
Date of creation: 2012
Date of revision:
Handle: RePEc:ags:aaea12:129166

Contact details of provider:
Postal: 555 East Wells Street, Suite 1100, Milwaukee, Wisconsin 53202
Phone: (414) 918-3190
Fax: (414) 276-3349
Email:
Web page: http://www.aaea.org
More information through EDIRC

Related research

Keywords: Monte Carlo Simulation; Method of Simulated Scores; Bayesian Estimation; Expectations Maximization; Two-Step Estimation; Consumer/Household Economics; Demand and Price Analysis; Research Methods/ Statistical Methods;

This paper has been announced in the following NEP Reports:

References

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
as in new window
  1. Ho-Chuan Huang, 2001. "Bayesian analysis of the SUR Tobit model," Applied Economics Letters, Taylor & Francis Journals, vol. 8(9), pages 617-622.
  2. Harald Tauchmann, 2005. "Efficiency of two-step estimators for censored systems of equations: Shonkwiler and Yen reconsidered," Applied Economics, Taylor & Francis Journals, vol. 37(4), pages 367-374.
  3. V A Hajivassiliou & DL McFadden, 1997. "The Method of Simulated Scores for the Estimation of LDV Models," STICERD - Econometrics Paper Series /1997/328, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  4. J. Scott Shonkwiler & Steven T. Yen, 1999. "Two-Step Estimation of a Censored System of Equations," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(4), pages 972-982.
  5. Lennart Flood & Urban Gr�sjo, 2001. "A Monte Carlo simulation study of Tobit models," Applied Economics Letters, Taylor & Francis Journals, vol. 8(9), pages 581-584.
Full references (including those not matched with items on IDEAS)

Citations

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:ags:aaea12:129166. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (AgEcon Search).

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.