Risk Function of Zellner's Extended Melo Estimators and Some Monte Carlo Results

Journal of Quantitative Economics, Vol. 16, No. 2, July 2001, 1-18

Posted: 31 Mar 2013

See all articles by Sukesh Ghosh

Sukesh Ghosh

Independent

Tony S. Wirjanto

University of Waterloo - School of Accounting and Finance; University of Waterloo, Department of Statistics & Actuarial Science

Date Written: 2000

Abstract

In this paper we theoretically derive the risk of Zellner's extended minimum expected loss function estimator. Using artificial data, we then calculate the risks of known nested estimators that include simple minimum expected loss function, two stage least squares and ordinary least squares. The resulting rankings of these estimators are compared to those generated via a simple Monte Carlo experiment. Both the analytical and the Monte Carlo results suggest that the extended minimum expected loss function estimator performs remarkably well when there is a positive degree of simlutaneity in the system of equations.

Keywords: Extended minimum expected loss function estimator, Risks, Monte Carlo Experiment

JEL Classification: C13, C15

Suggested Citation

Ghosh, Sukesh and Wirjanto, Tony S., Risk Function of Zellner's Extended Melo Estimators and Some Monte Carlo Results (2000). Journal of Quantitative Economics, Vol. 16, No. 2, July 2001, 1-18, Available at SSRN: https://ssrn.com/abstract=2241820

Sukesh Ghosh

Independent ( email )

Tony S. Wirjanto (Contact Author)

University of Waterloo - School of Accounting and Finance ( email )

200 University Avenue West
Waterloo, Ontario N2L 3G1
Canada
519-888-4567 x35210 (Phone)

HOME PAGE: http://https://uwaterloo.ca/statistics-and-actuarial-science/people-profiles/tony-wirjanto

University of Waterloo, Department of Statistics & Actuarial Science ( email )

200 University Avenue West
Waterloo, Ontario N2L 3G1
Canada
519-888-4567 x35210 (Phone)
519-746-1875 (Fax)

HOME PAGE: http://math.uwaterloo.ca/statistics-and-actuarial-science/people-profiles/tony-wirjanto

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