Exact and Stochastic Linear Restrictions

1995 ◽  
pp. 111-154
Author(s):  
Calyampudi Radhakrishna Rao ◽  
Helge Toutenburg
Keyword(s):  
Linear Restrictions

Testing linear restrictions on non-linear models

1984 ◽  
Vol 15 (1-2) ◽  
pp. 91-96
Author(s):  
K.R. Sawyer ◽  
M.C. Rosalsky
Keyword(s):  
Linear Models ◽  
Non Linear ◽  
Linear Restrictions ◽  
Testing Linear

Implied linear restrictions in the general Gauss-Markov model

1992 ◽  
Vol 30 (2) ◽  
pp. 237-248 ◽  
Author(s):  
Jerzy K. Baksalary ◽  
PawełR. Pordzik
Keyword(s):  
Markov Model ◽  
Linear Restrictions

scholarly journals Weaker Criteria and Tests for Linear Restrictions in Regression

Econometrica ◽  
1972 ◽  
Vol 40 (4) ◽  
pp. 689 ◽  
Author(s):  
T. D. Wallace
Keyword(s):  
Linear Restrictions

Estimation of Cointegration Vectors with Linear Restrictions

1993 ◽  
Vol 9 (1) ◽  
pp. 19-35 ◽  
Author(s):  
Pentti Saikkonen
Keyword(s):  
Generalized Least Squares ◽  
Simultaneous Equation ◽  
Equation Model ◽  
Asymptotic Distributions ◽  
Reduced Form ◽  
Simultaneous Equation Model ◽  
Test Procedures ◽  
Rank Condition ◽  
Efficient System ◽  
Linear Restrictions

A general approach for the estimation of cointegration vectors with linear restrictions is described. In the special case of zero restrictions, the cointegration relations of the paper are formally similar to the structural form of a traditional simultaneous equation model. The proposed estimation procedures require a conventional rank condition of identification but no exogeneity assumption. In place of exogenous variables there are series that are not cointegrated and can therefore describe the common trends in the system. The estimators of the paper are flexible and simple to use. They can be combined with several recent estimators developed for cointegration regressions which in the present context are formally similar to the reduced form of a simultaneous equation model. After the coefficient matrix of a cointegration regression has been estimated, the estimators of the paper can be obtained by simple generalized least squares. Both single equation estimators and more efficient system estimators are developed. The asymptotic distributions of the estimators are shown to be mixed normal so that Wald tests with asymptotic chi-square distributions under the null hypothesis can be obtained in the usual way. Convenient test procedures for checking the validity of overidentification restrictions are also provided.

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