Results 211 to 220 of about 165,147 (263)

When are two-stage and three-stage least squares estimators identical?

Economics Letters, 1981
Abstract Necessary and sufficient conditions are derived for the numerical equivalence of the two-stage and three-stage least squares estimators in a linear simultaneous equations model. The conditions are easy to verify in any practical application.
Fiebig, D., Kapteyn, A.J.
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Jackknifing the two stage least squares estimator

Communications in Statistics - Simulation and Computation, 1984
A link is established between jackknifing smooth functions of regression parameter estimators in the general linear model and jackknifing the two stage least squares estimator in the simulta- neous equation model. Hinkley's weighted jackknife is explored and is shown to have a smaller variance than the standard jack- knife estimator in the context of ...
G. D. A. Phillips, B. P. McCabe
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Two- or three-stage least squares?

Computer Science in Economics and Management, 1988
Two elements enter the choice between 2 and 3SLS for full-system estimation: statistical efficiency and computational cost. 2SLS always has the computational edge, but 3SLS can be more efficient, a relative advantage that increases with the strength of the interrelations among the error terms. A measure of these interrelations is thus helpful in making
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Estimated Residual Covariance Matrix of Two Stage Least Squares and Computation of Three Stage Least Squares Estimates

International Economic Review, 1974
THE THREE STAGE LEAST SQUARES (3SLS), as proposed by Zellner and Theil [4], is a procedure for estimating simultaneously a complete system of linear stochastic structural equations. The estimilator is a straightforward application of Aitken's generalized least squares after a suitable transformation is applied to the system.
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Two-stage, least squares design of biorthogonal filter banks

IEE Proceedings - Vision, Image, and Signal Processing, 2000
A two-stage approach is employed for the design of a class of two-channel biorthogonal filter banks. The filter banks belong to the class HPFB (halfband pair filter bank) and are defined by two kernels. The parametric Bernstein polynomial is used to construct the kernels. The design of the free parameters of the Bernstein polynomial is achieved through
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two-stage least squares and the k-class estimator

1987
Two-stage least squares (TSLS) is a method of estimating the parameters of a single structural equation in a system of linear simultaneous equations. The TSLS estimator was proposed by Theil (1953a, 1961) and independently by Basmann (1957). The early work on simultaneous equation estimation was carried out by a group of econometricians at the Cowles ...
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Nonstandard Distributions in Two-Stage Least Squares.

1981
Abstract : In estimating the coefficient of an endogenous variable in a single equation of a system of linear equations, Anderson and Sawa (1973) expressed the distribution of the two-stage least-squares (TSLS) estimator as a doubly noncentral F distribution.
D. R. Jensen, Mark Marcucci
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Applying Two-Stage Least Squares

1987
In an earlier decade, research studies in economic education often specified a single-equation model with one dependent endogenous variable and several exogenous explanatory variables. Ordinary least squares (OLS) was used to estimate the equation and little attention was given to justifying the exogeneity of the regressors.
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Two-Stage Least Squares or Gradient Matching

2017
This chapter presents indirect methods of fitting parameters to ordinary differential equation models. Rather than solving the ODE, we instead obtain non-parametric estimates of the state trajectory and its derivative. This allows the right hand side of the ODE to be fit to the estimated derivatives, which is often numerically easier than the ...
James Ramsay, Giles Hooker
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The Bias of the Two-Stage Least Squares Estimator

Journal of the American Statistical Association, 1972
Abstract This article derives the bias of the two-stage least squares estimator to the order of T-2, T being the number of observations. It is found that when the degree of overidentification in the equation concerned is unity, the 2SLS bias is zero to this order of approximation.
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