[求助]用SPSS怎样做异方差检验

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用Spss怎么样做异方差检验？

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关键词：异方差检验 方差检验 SPSS PSS 异方差 方差 检验 SPSS

among others, plot u_t_hat^2 against X1,t, you can also do ARCH test if conditional heteroscedasticity is suspected

我的数据是横截面数据，用不上ARCH检验，另外我的方程中有4个以上解释变量，如何用残差进行分析？多谢。

[此贴子已经被作者于2006-3-11 22:32:15编辑过]

If you do multiple regression, click "Plots" option, and plot your dependent variable against residuals. Or you can click "Save" option, and save "unstandardized residual", then plot each independent variable against "unstandardized residual". From the scatterplot, you can see if there is heteroscedasticity (异方差).

11

SPSS Web Books: Regression with SPSS
Chapter 2 - Regression Diagnostics

This chapter will explore how you can use SPSS to test whether your data meet the assumptions of linear regression. In particular, we will consider the following assumptions.

• Linearity - the relationships between the predictors and the outcome variable should be linear
• Normality - the errors should be normally distributed - technically normality is necessary only for the t-tests to be valid, estimation of the coefficients only requires that the errors be identically and independently distributed
• Homogeneity of variance (homoscedasticity) - the error variance should be constant
• Independence - the errors associated with one observation are not correlated with the errors of any other observation
• Model specification - the model should be properly specified (including all relevant variables, and excluding irrelevant variables)

http://www.ats.ucla.edu/stat/spss/webbooks/reg/chapter2/spssreg2.htm

When heteroscedasticity is mild, OLS standard errors behave quite well (Long and Ervin 2000). However, when heteroscedasticity is severe, ignoring it may bias your standard errors and p values. The direction of the bias depends on the pattern of heteroscedasticity: p values may be too large or too small.

Sometimes heteroscedasticity can be removed by a data transformation, such
as logging the dependent variable. This may also href="http://www.sociology.ohio-state.edu/ptv/faq/normality.htm">improve the
approximation to normality. Be careful, however, that your transformation
doesn't make the results hard to interpret.

Sometimes the form of the heteroscedasticity is clear and can be modeled. More commonly, though, heteroscedasticity is a nuisance that cannot be modeled because its source is not well understood. In this case, the classic correction for heteroscedasticity is the HC0 estimator proposed by Huber (1967) and White (1980). But although this estimator is correct in large samples, it is no better than OLS in small samples. MacKinnon and White (1985) discussed three improvements, HC1, HC2, and HC3. An evaluation by Long and Ervin (2000) suggests that HC3 is the best, especially in small samples.

It is possible to correct for heteroscedasticity using popular software:

1. In Stata, use the HC3 option in the REG command, e.g.,
   

   reg y x, hc3

   In small samples, this is better than the ROBUST option, which implements HC1.
   
2. In SAS, the ACOV option in the REG procedure implements the HC0 correction, e.g.:
   

   proc reg;
   model y = x / acov;
   run;

   However, ACOV only corrects the covariance matrix; it does not correct the standard errors. To get the corrected standard errors, take the square roots of the diagonal elements in the covariance matrix. Alternatively,Andrew Hayes has written a SAS macro that gives HC0 standard errors directly, and implements the improved small-sample corrections HC1, HC2, and HC3.
   
3. SPSS has not implemented any heteroscedasticity correction, but again Hayes has written an SPSS macro that implements the HC0, HC1, HC2, and HC3 corrections.
   
4. LIMDEP implements the HC0 correction. In addition, Bob Kaufman has written the following LIMDEP code for implementing the HC3 correction:

Title; Calculate MacKinnon and White's HC3 estimator of OLS Standard Errors $
Namelist; iv=one,list of predictors; dv=name of dependent variable$
Regress; lhs=dv; rhs=iv; res=resy$
Matrix; xpxinv=; bols=b$
Create; resysq=resy^2$
Create; hii=qfr(iv,xpxinv); hc3= resysq/ (1-hii) $
Matrix; varhc3=xpxinv*iv'[hc3]iv*xpxinv; stat(bols,varhc3)$

Now, how do you know if you should correct for heteroscedasticity? There are a number of tests for heteroscedasticity, so it seems natural to conduct a test, then use a correction if the test suggest heteroscedasticity. The trouble with this is that the tests often fail to detect heteroscedasticity, leading you to neglect the correction when it is actually needed. In simulations, Long and Ervin (2000) found that this possibility was quite serious. As a result, they recommended that "a test for heteroscedasticity should not be used to determine whether [an HC estimator] should be used." It is better to use an HC estimator whenever heteroscedasticity is suspected.

References

Greene, W.F. (1997). Econometric Analysis (3rd edition). New York: Prentice-Hall.

Hayes, A. F. & Cai, L. (in review). "Heteroscedasticity-robust moderated multiple regression using heteroscedasticity-consistent standard error estimates." Manuscript submitted for publication.

Huber, P.J. 1967. "The behavior of maximum likelihood estimates under non-standard conditions." Proceeding of the Fifth Berkeley Symposium on Mathematical Statistics and Probability 1: 221-233.

Long, J.S and L.H. Ervin, 2000, "Using Heteroscedasticity Consistent Standard Errors in the Linear Regression Model." The American Statistician 54:217-224.

MacKinnon, J.G. and H. White. 1985. "Some heteroskedasticity consistent covariance matrix estimators with improved finite sample properties." Journal of Econometrics, 29, 53-57.

White, Halbert. 1980. "A heteroskedastic-consistent covariance matrix estimator and a direct test of heteroskedasticity." Econometrica 48:817-838.

[此贴子已经被作者于2006-3-12 7:22:11编辑过]

多谢，那么已经检验出异方差，如果用加权最小二乘法估计时，权数用spss怎么估计？（用残差还是用1/x好），

good

hanszhu 发表于 2006-3-12 07:21
When heteroscedasticity is mild, OLS standard errors behave quite well (Long and Ervin 2000). Howeve ...

能不能翻译成中文