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Comparative performance of the limited information techniques in a two equation structural model

The samples with which we deal in practice are rather small. seldom exceeding 80 observations and frequently much smaller. 'Thus, it is of great interest to inquire into the properties of estimators for the typical sample sizes encountered in practice. The performances of three simultaneous estimati...

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Published: 2007
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LEADER 00000njm a2000000a 4500
001 oai:repository.ui.edu.ng:123456789/7637
042 |a dc 
720 |a Adepoju, A. A.  |e author 
260 |c 2007 
520 |a The samples with which we deal in practice are rather small. seldom exceeding 80 observations and frequently much smaller. 'Thus, it is of great interest to inquire into the properties of estimators for the typical sample sizes encountered in practice. The performances of three simultaneous estimation method using a model consisting of a mixture of an identified and over identified equations with correlated error terms and compared. The result of the Monte Carlo study revealed that the Two Stage least Squares (2SLS) and the Limited Information Maximum Likelihood (LIML) estimates are similar and in most cases identical in respect of the just-identified equation. The Total Absolute Biases (TAB) of 2SLS and LIML revealed asymptotic behavior under (upper triangular matrix) P1 while those of Ordinary Least Squares (OLS) exhibited no such behavior. For both upper and lower triangular matrices (P, and P2), 2SI.S estimates showed asymptotic behavior in the middle interval. The OI.S is the only stable estimator with a stable behavior of Root Mean Square Error (RM3F.) as its estimates increase (decrease) consistently for equation 1(equation 2) for P, (for P2). 
024 8 |a ui_ art_adepoju_comparative_2007 
024 8 |a Science Focus 12 (2) 2007. Pp. 122- 120 
024 8 |a http://ir.library.ui.edu.ng/handle/123456789/7637 
653 |a Monte carlo 
653 |a Identification 
653 |a Mutual correlation 
653 |a Estimation 
653 |a Random deviates 
245 0 0 |a Comparative performance of the limited information techniques in a two equation structural model