求助eviews检验内生性的详细步骤和操作方法

详细点，最好是PPT或者word版的。尽可能告诉时间序列和面板两种情况下的具体操作方法谢谢！

无论是时间序列还是面板，检验内生性的步骤是类似的。
首先用Eviews将因变量和自变量回归，得到回归方程的残差序列。
然后将残差序列作为一个自变量加入到回归方程中，再次进行回归。
如果残差序列的系数是显著不为零的，那么说明原方程存在内生性。

常见的解决办法就是寻找一个合适的工具变量。

有更详细的么？我记得时间序列检验内生性好像有个直接的hauseman检验，不知道有没有人知道怎么做》谢谢咯

How do I test endogeneity? How do I perform a Durbin–Wu–Hausman test?

Consider a regression

y = b0 + b1*z + b2*x3 + e

where z is endogenous.
Suppose that x1 and x2 are instrumental variables for z.
One should decide whether it is necessary to use an instrumental variable, i.e., whether a set of estimates obtained by least squares is consistent or not.
An augmented regression test can easily be formed by including the residuals of each endogenous right-hand side variable, as a function of all exogenous variables, in a regression of the original model.
We would first perform a regression

z = c0 + c1*x1 + c2*x2 + c3*x3 + u

to get residuals z_res, then perform an augmented regression:


y = d0 + d1*z + d2*x3 + d3*z_res + e

If d3 is significantly different from zero, then OLS is not consistent.



For example, let us assume that you wish to estimate


rent = b0 + b1*hsngval + b2*pcturban + e

where hsngval is endogenous amd pcturban is exogenous.
Instrumental variables for hsngval are:
faminc, reg2, reg3 and reg4. To test the endogeneity of hsngval,

(i) we first run a reduced form model, using all exogenous variables:

. regress hsngval faminc reg2-reg4 pcturban


Source |
SS
df
MS
Number of obs =
50
-------------+------------------------------
F(
5,
44) =
19.66

Model |
8.4187e+09
5
1.6837e+09
Prob > F
=
0.0000

Residual |
3.7676e+09
44
85626930.6
R-squared
=
0.6908
-------------+------------------------------
Adj R-squared =
0.6557

Total |
1.2186e+10
49
248700555
Root MSE
=
9253.5

------------------------------------------------------------------------------

hsngval |
Coef.
Std. Err.

t
P>|t|
[95% Conf. Interval]
-------------+----------------------------------------------------------------

faminc |
2.731324
.6818931
4.01
0.000
1.357058
4.105589

reg2 |
-5095.038
4122.112
-1.24
0.223
-13402.61
3212.533

reg3 |
-1778.05
4072.691
-0.44
0.665
-9986.019
6429.919

reg4 |
13413.79
4048.141
3.31
0.002
5255.296
21572.28

pcturban |
182.2201
115.0167
1.58
0.120
-49.58092
414.0211

_cons |
-18671.87
11995.48
-1.56
0.127
-42847.17
5503.438
------------------------------------------------------------------------------

(ii) Then, we save the residual from the above regression.
Call it “hsng_res”.
Then, include hsng_res in the main equation, and estimate the main equation by OLS.

. predict hsng_res, res

. regress rent hsngval pcturban hsng_res


Source |
SS
df
MS
Number of obs =
50
-------------+------------------------------
F(
3,
46) =
47.05

Model |
46189.1513
3
15396.3838
Prob > F
=
0.0000

Residual |
15053.9687
46
327.26019
R-squared
=
0.7542
-------------+------------------------------
Adj R-squared =
0.7382

Total |
61243.12
49
1249.85959
Root MSE
=
18.09

------------------------------------------------------------------------------

rent |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+----------------------------------------------------------------

hsngval |
.0022398
.0002681
8.36
0.000
.0017003
.0027794

pcturban |
.081516
.2438355
0.33
0.740
-.4092993
.5723313

hsng_res |
-.0015889
.0003984
-3.99
0.000
-.0023908
-.000787

_cons |
120.7065
12.42856
9.71
0.000
95.68912
145.7239
------------------------------------------------------------------------------

Then, we test the significance of the coefficient of the added residual.

. test hsng_res


( 1)
hsng_res = 0.0


F(
1,
46) =
15.91

Prob > F =
0.0002

The small p-value indicates that OLS is not consistent.



To perform an IV regression, run ivreg

. ivreg rent pcturban (hsngval = faminc reg2-reg4)

Instrumental variables (2SLS) regression


Source |
SS
df
MS
Number of obs =
50
-------------+------------------------------
F(
2,
47) =
42.66

Model |
36677.4033
2
18338.7017
Prob > F
=
0.0000

Residual |
24565.7167
47
522.674823
R-squared
=
0.5989
-------------+------------------------------
Adj R-squared =
0.5818

Total |
61243.12
49
1249.85959
Root MSE
=
22.862

------------------------------------------------------------------------------

rent |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+----------------------------------------------------------------

hsngval |
.0022398
.0003388
6.61
0.000
.0015583
.0029213

pcturban |
.081516
.3081528
0.26
0.793
-.5384074
.7014394

_cons |
120.7065
15.70688
7.68
0.000
89.10834
152.3047
------------------------------------------------------------------------------
Instrumented:
hsngval
Instruments:
pcturban faminc reg2 reg3 reg4
------------------------------------------------------------------------------
Note that the coefficients of the last two estimates are the same, however, the standard errors are different.

楼上的太深奥了看不懂。如果实在没有人回复，就把金币给第一个回复的朋友

这步骤不是很清楚吗? 连指令都给了。乱的部分是返回结果的例子，你看不看都行。

4楼给出的是伍德里奇的方法吧.假如一个模型有很多变量的话,这样检验确实挺麻烦的

phill讲得非常清楚。。。。。。。。。。。。

楼主，你设置的最佳答案里面这句话“首先用Eviews将因变量和自变量回归，得到回归方程的残差序列。”是错误的。 你应该选择4楼的答案啊，是正确的！！

phill 发表于 2011-2-12 09:01
How do I test endogeneity? How do I perform a Durbin–Wu–Hausman test?
Consider a regression

在eviews的第一步回归结果里怎样找到残差的值，请教