Maximum likelihood estimation of random effects models
作者： Trevor S. Breusch
DOI: 10.1016/0304-4076(87)90010-8
关键词: Applied mathematics 、 Expectation–maximization algorithm 、 Likelihood principle 、 Restricted maximum likelihood 、 Likelihood function 、 Mathematics 、 Estimation theory 、 Maximum likelihood sequence estimation 、 Monotonic function 、 Statistics 、 Random effects model
摘要: Abstract Iterated GLS has a remarkable property when applied to the random effects model in its usual parameterization. The values for parameter that measures relative variance, obtained through successive iterations, form monotonic sequence. This provides convenient checks multiple maxima of likelihood function and existence local maximum satisfies non-negativity condition.
elsevier.com
sci-hub.se 参考文章(4)
JerryA. Hausman, WilliamE. Taylor, Panel data and unobservable individual effects Journal of Econometrics. ,vol. 16, pp. 155- 155 ,(1981) , 10.1016/0304-4076(81)90085-3
W. Oberhofer, J. Kmenta, A General Procedure for Obtaining Maximum Likelihood Estimates in Generalized Regression Models Econometrica. ,vol. 42, pp. 579- 590 ,(1974) , 10.2307/1911792
G. S. Maddala, THE USE OF VARIANCE COMPONENTS MODELS IN POOLING CROSS SECTION AND TIME SERIES DATA Econometrica. ,vol. 39, pp. 341- 358 ,(1971) , 10.2307/1913349
Marc Nerlove, Further Evidence on the Estimation of Dynamic Economic Relations from a Time Series of Cross Sections Econometrica. ,vol. 39, pp. 359- 382 ,(1971) , 10.2307/1913350