Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.645349
Title: Stochastic trends in simultaneous equation systems
Author: Streibel, Mariane
Awarding Body: London School of Economics and Political Science (University of London)
Current Institution: London School of Economics and Political Science (University of London)
Date of Award: 1992
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Abstract:
The estimation of univariate and multiple regression models with stochastic trend components has been considered in the time domain and in the frequency domain. Such models assume as regressors weakly exogenous variables. However if the regression equations are part of a simultaneous equation system some of the regressors will no longer be weakly exogenous and estimators obtained by ignoring this fact will be inconsistent. One way of proceeding in such situations is to estimate the whole system, that is, to construct full information maximum (FIML) estimators. Alternatively, single equation estimators such as limited information maximum likelihood (LIML) can be constructed, as well as estimators based on the instrumental variable (IV) principle which possess the merit of consistency. As in the analogous situation in classical simultaneous equation systems, within this class of limited information estimators, LIML is asymptotically efficient. Hence it is appropriate to study the asymptotic properties of LIML and review the possibility of alternative consistent estimators, using LIML as a benchmark. The purpose of the thesis is thus: to examine the issues of identifiability when stochastic trends are present in simultaneous equation systems; -to examine the computational issues associated with FIML, LIML and various IV estimators in simultaneous equation systems with stochastic trends and derive the asymptotic properties in the frequency domain of these estimators; to compare the performance of IV and LIML via Monte Carlo experiments; to apply the methods to real data.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.645349  DOI: Not available
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