谁知道stata中怎么用hausman test检验一组数据的正态性

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如题

我想检验一组数据是否是正态分布,已经计算出该数据的四个样本矩方程,但是具体在stata上怎么做hausman test?

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关键词：hausman ausman Stata test tata 数据 检验 test hausman Stata

我不知道hausman test可以检验你说的那个啊

[此贴子已经被作者于2007-3-2 15:40:16编辑过]

可以假设数据三、四阶矩不是有效的

是不是类似于skewness and kurtosis test?

你的先知道hausman到底检验什么才能做啊

hausman的原来是什么，下面是stata中的hausman的检验，和你讲的不同，你还是先去看看书把

Hausman specification test

hausman name-consistent [name-efficient] [, constant alleqs skipeqs(eqlist)
equations(matchlist) sigmamore force tconsistent(string)
tefficient(string) df(#) ]

name-consistent and name-efficient are names under which estimation results were
saved via estimates store. A period (.) may be used to refer to the last estimation
results, even if these were not already stored. Not specifying name-efficient is
equivalent to specifying the last estimation results (.).

Description

hausman performs Hausman's (1978) specification test. To use hausman, one has to
perform the following steps.

(1) obtain an estimator that is consistent whether or not the hypothesis is true;
(2) store the estimation results under a name-consistent using estimates store;
(3) obtain an estimator that is efficient (and consistent) under the hypothesis
that you are testing, but inconsistent otherwise;
(4) store the estimation results under a name-efficient using estimates store;
(5) use hausman to perform the test

hausman name-consistent name-efficient [, options]

The order of computing the two estimators may be reversed. You have to be careful
though to specify to hausman the models in the order "always consistent" first and
"efficient under H0" second. It is possible to skip storing the second model and
refer to the last estimation results by a period (.).

hausman may be used in any context. The order in which you specify the regressors
in each model does not matter, but it is your responsibility to assure that the
estimators and models are comparable, and satisfy the theoretical conditions (see
(1) and (3) above).

Options

constant specifies that the estimated intercept(s) are to be included in the model
comparison; by default they are excluded. The default behavior is appropriate
for models where the constant does not have a common interpretation across the
two models.

alleqs specifies that all of the equations in the models be used to perform the
Hausman test; by default only the first equation is used.

skipeqs(eqlist) specifies in eqlist the names of equations to be excluded from the
test. Equation numbers are not allowed in this context as it is the equation
names, along with the variable names, that are used to identify common
coefficients.

equations(matchlist) specifies, by number, the pairs of equations that are to be
compared, and should have the syntax

#c:#e [, #c:#e[, ...]]

where #c (#e) is an equation number of the always-consistent (efficient under
H0) estimator. For instance equations(1:1); equations(1:1,2:2); or
equations(1:2). If equations() is not specified, equations are matched on
equation names.

equations() handles the situation when one estimator uses equation names and the
other does not. For instance, equations(1:1) means equation 1 is to be tested
against equation 1. equations(1:1, 2:2) means equation 1 is to be tested
against equation 1 and equation 2 against equation 2. equations(1:2) means
equation 1 of the always consistent estimator is to be tested against equation 2
of the efficient-under-H0 estimator. If equations() is specified, options
alleqs and skipeqs are ignored.

sigmamore allows you to specify that the (co)variance matrices used in the test be
based on a common estimate of disturbance variance (sigma2), namely the variance
from the efficient estimator. This option provides a proper estimate of the
contrast variance for so-called tests of exogeneity and over-identification in
instrumental variables regression. This option can only be specified when both
estimators save e(sigma) or e(rmse).

force specifies that the Hausman test is performed even though the assumptions of
the Hausman test seem not to be met; e.g., because the estimators were
p-weighted.

tconsistent(str) and tefficient(str) are formatting options. They allow you to
specify the headers of the columns of coefficients that default to the names of
the models. These options will be primarily of interest to programmers.

df(#) specifies the degrees of freedom for the Hausman test. The default is the
matrix rank of the variance of the difference between the coefficients of the
two estimators.

多谢您了

我看了半天发现我把hansen's test和hausman's test搞混了

多谢你的帮助,我再试试