Results 251 to 260 of about 91,736 (294)

Asymptotics for EBLUPs: Nested Error Regression Models

open access: yesJournal of the American Statistical Association, 2021
In this paper we derive the asymptotic distribution of estimated best linear unbiased predictors (EBLUPs) of the random effects in a nested error regression model. Under very mild conditions which do not require the assumption of normality, we show that asymptotically the distribution of the EBLUPs as both the number of clusters and the cluster sizes ...
Ziyang Lyu, A.H. Welsh
core   +5 more sources

Prediction in an Unbalanced Nested Error Components Panel Data Model

Journal of Forecasting, 2013
ABSTRACTThis paper derives the best linear unbiased predictor for an unbalanced nested error components panel data model. This predictor is useful in many econometric applications that are usually based on unbalanced panel data and have a nested (hierarchical) structure.
Badi H Baltagi, Alain Pirotte
exaly   +2 more sources

On Testing Linear Hypothesis in a Nested Error Regression Model

Communications in Statistics - Theory and Methods, 2010
Consider the problem of testing the linear hypothesis on regression coefficients in the nested error regression model. The standard F-test statistic based on the ordinary least squares (OLS) estimator has the serious shortcoming that its type I error rates (sizes) are much larger than nominal significance levels, because the covariance matrix of data ...
Tatsuya Kubokawa
exaly   +2 more sources

BLUP in the nested panel regression model with serially correlated errors

Computational Statistics and Data Analysis, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Myoungshic Jhun, Seuck Heun Song
exaly   +2 more sources

Prediction in a spatial nested error components panel data model

International Journal of Forecasting, 2014
Abstract This paper derives the Best Linear Unbiased Predictor (BLUP) for a spatial nested error components panel data model. This predictor is useful for panel data applications that exhibit spatial dependence and a nested (hierarchical) structure. The predictor allows for unbalancedness in the number of observations in the nested groups.
Badi H Baltagi, Alain Pirotte
exaly   +2 more sources

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