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Titlebook: Micro-Econometrics; Methods of Moments a Myoung-jae Lee Book 2010Latest edition Springer-Verlag New York 2010 Micro-econometrics.econometri

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樓主: obesity
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發(fā)表于 2025-3-23 11:42:55 | 只看該作者
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發(fā)表于 2025-3-23 14:12:50 | 只看該作者
Myoung-jae Leeults, relatively few revisions were necessary for the English edition. They were made early in 1979 and affected about 5% of the text. The main revisions referred to the distance scale, the dlstribution of galaxies, the X-ray observations of clusters, the cosmic time evolution of quasars and radioga
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發(fā)表于 2025-3-23 21:39:40 | 只看該作者
elations.Shows the effects of shear viscosity, bulk viscosit.This thesis presents theoretical and numerical studies on phenomenological description of the quark–gluon plasma (QGP), a many-body system of elementary particles..The author formulates a causal theory of hydrodynamics for systems with net
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發(fā)表于 2025-3-24 00:36:18 | 只看該作者
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發(fā)表于 2025-3-24 03:53:36 | 只看該作者
M-Estimator And Maximum Likelihood Estimator (MLE),aximizing/minimizing some objective functions, but most of them are not written in closed forms; those estimators, called “Mestimators”, are reviewed here. Typically, the first-order conditions of M-estimators are moment conditions, and this links M-estimator to MOM estimator/test.
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發(fā)表于 2025-3-24 07:54:52 | 只看該作者
978-1-4899-8332-9Springer-Verlag New York 2010
17#
發(fā)表于 2025-3-24 13:37:36 | 只看該作者
Methods of Moments for Single Linear Equation Models,Method-of-moment (MOM) . is introduced here, whereas MOM for multiple linear equations will be examined in the next chapter. Least squares estimator (LSE) is reviewed to estimate the conditional mean (i.e., regression function) in a model with . regressors.
18#
發(fā)表于 2025-3-24 15:54:20 | 只看該作者
19#
發(fā)表于 2025-3-24 19:38:34 | 只看該作者
Parametric Methods for Single Equation LDV Models,Single equation models with limited dependent variables (LDV) are reviewed here whereas multiple equations with LDV will be reviewed in the next chapter.
20#
發(fā)表于 2025-3-25 00:23:37 | 只看該作者
Parametric Methods for Multiple Equation LDV Models,Going further from single equation models with limited dependent variables (LDV) in the preceding chapter, multiple equations with LDV’s are examined.
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