Least Squares Estimator
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Consistency of the structured total least squares estimator in a multivariate errors in variables model
... total least squares estimator, defined via a constrained optimization problem, is a generalization of the total least squares estimator when the data matrix and the applied ...
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The large deviation for the least squares estimator of nonlinear regression model based on WOD errors
... the least squares estimator in the nonlinear regression model are established, which extend the corresponding ones for independent errors and some dependent ...
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Using wavelets to obtain a consistent ordinary least squares estimator of the long memory parameter
... ordinary least squares estimator of the long-memory parameter from a fractionally integrated process that is an alternative to the Geweke and Porter-Hudak (1983) ...ordinary least ...
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Asymptotic Properties of the Weighted Least Squares Estimator Under Moments Restriction
... The aim of this work is to review the paper by Hellerstein & Imbens (1982) focusing on the use of auxiliary data and a formal derivation of the asymptotic properties of the underlying Weighted Least ...
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Two stage weighted least squares estimator of the conditional mean of observation driven time series models
... We simulated N = 1000 independent replications of length n = 500 and n = 2000 of INARCH(q) models, and compared the finite-sample performance of the following estimators: the PQMLE (1.7), the NBQMLE (1.8) with r=1, the ...
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Iterative Least Squares Estimator of Binary Choice Models: a Semi Parametric Approach
... The simulation results indicate that the estimator is, 1 easy-tocompute and fast, 2 insensitive to initial estimates, 3 appears to be \/-consistent and asymptotically normal, and, 4 bett[r] ...
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On the computation of the structured total least squares estimator
... We consider numerical methods for the solution of the optimization problem (5). One approach is to use standard algorithms for local optimization. The choice of the optimization method is inspired by the need to use as ...
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Empirical likelihood based inference in Poisson autoregressive model with conditional moment restrictions
... likelihood estimator, the least squares estimator and the weighted least squares estimator, which do not utilize the conditional moment ...
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Consistent estimation in an implicit quadratic measurement error model
... adjusted least squares estimator is derived that yields a consistent estimate of the parameters of an implicit quadratic measurement error ...consistent estimator for the measurement error ...
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Minimization of Error in Exponential Model Estimation via Jackknife Algorithm
... the least squares estimator, they proposed a class of weighted jackknife variance estimators for the least squares estimator by deleting any fixed number of observations at a ...
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On the Equivalence of the Weighted Least Squares and the Generalised Least Squares Estimators, with Applications to Kernel Smoothing
... The paper establishes the conditions under which the generalised least squares estima- tor of the regression parameters is equivalent to the weighted least squares estimator. The ...
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Restricted estimator in two seemingly unrelated regression model
... In practical regression analysis, researchers often encounter the problem of multicollinearity. In case of multicollinearity we know that when the correlation matrix has one or more small eigenvalues, the estimates of ...
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Adaptive Estimation of Heteroscedastic Money Demand Model of Pakistan
... kernel estimator under substantially more restrictive conditions on the data generating process, Robinson (1987) estimated the residual variances of unknown function of the explanatory variables by nearest ...
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A simple approach to inference in random coefficient models
... Carter and Yang (1986) derived the asymptotic distribution of the estimated generalized least squares estimator as either n, the number of experimental units, tends to infinity and/or as[r] ...
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Strong consistency of estimators in partially linear models for longitudinal data with mixing dependent structure
... For exhibiting dependence among the observations within the same subject, the paper considers the estimation problems of partially linear models for longitudinal data with the -mixing and r -mixing error structures, ...
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Estimation of bivariate linear regression data via Jackknife algorithm
... the least squares estimator, they proposed a class of weighted jackknife variance estimators for the least squares estimator by deleting any fixed number of observations at a ...
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A comparison of MLE method and OLSE for the estimation of modified Weibull distribution parameters by using the simulation
... In this paper, we study the Maximum Likelihood Estimation (MLE) and Ordinary Least Squares Estimator (OLSE) methods for estimation of the unknown parameters of the modified Weibull distribution. A ...
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Estimation for Constantinides-Ingersol Model with Small L´evy Noises from Discrete Observations
... the least squares method is used to obtain the explicit formula of the estimator and the estimation error is given as ...the least squares estimator is proved by applying the ...
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A Combination Method for Averaging OLS and GLS Estimators
... pretest estimator that has inferior properties, and its use can be harmful (see Danilov and Magnus, ...ordinary least-squares (OLS) estimator for linear regression models with homoscedastic ...
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The consistency for estimator of nonparametric regression model based on NOD errors
... the least squares estimator of b and the nonparametric estimator of g(t) based on NA samples, Hu [12] obtained the consistency and complete consistency for these esti- mations based on the ...
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