华工2023计量期末试题
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A卷
I.Single choice Questions
The cross-sectional data is （ ）
A.Different units at a given point in time
B.Different units in different time
C.The same unit in different time
D.The same units at a given point in time
For the model
=
+
+
，the ordinary least squares means（ ）
A.
B.
C.
D.
For the model
+
+
+
+
( )
A.The model satisfies classcial assumption
B.The model suffers from perfect collinearity
C.
is correlated with
D.
For the model
is equal to
，the test statistic is ( )
A.The DW statistic
B.The F statistic
C.The LM statistic
D.The t statistic
If
is a consistent estimator of
，it implies that ( )
A.
for every
B.
for every
C.
for every
D.
for every
Which of the following statement about Beta coefficient is not right ( )
A.The Beta coefficient is obtained by replace
and each
with a standardized version
B.The Beta coefficient reflects the effect on standard deviation of
C.The Beta coefficient can not be estimated by OLS
D.The Beta coefficient can eliminate the impact of data scaling on estimators
If
A.1
B.2
C.5
D.4
In the time series regression
=
+
+
，serial correlation implies that( )
A.The usual OLS standard errors are invalid
B.Corr(
)=0 ( t≠s )
C.OLS is BLUE
D.The estimator of
is no longer consistent.
For the finite distributed lag model
+
.If z permanent increase on unit at time t，the long-run propensity is ( )
A.
B.
C.
D.
For the model
，if the value of DW statistic is equal to 1.92，it implies that ( )
A.The independent variable
is not significant
B.
C.
D.The model is misspecified
II.Judgment 判断并且改错，正确的不用修改，错误的需要更正。
1.For the model ，we have
=
2.Adding a regressor will increase adjusted R²
3.For the classic regression model ，we have
4.For the model ，under Assumption SLR.1 to SLR.3，we have
E()=
5.SSE=
6.Including one or more irrelevant variables in a multiple regression model or over specifying the model does not affect the unbiasedness of the OLS.
7.The OLS estimators are inconsistent if the model has heteroskedasticity estimators
8.The random walk is a stationary process
9.The R square of a multiple OLS is 0.7362，k=5 n=79 then the adjusted R square is 0.7362
10.
At 5% level we can reject about staff
III.Answer the following questions briefly
1.What are the Gauss-Markov Assumptions in MLR
2.Explain the consequence of causing by measurement error under explanatory variable
3.Why an MA(1) process is a stationary process？
4.What is the implicaton of in the following model？
5.Explain the Breusch-Godfrey LM test for serial correlation
IV.
1.According to a time series sample between consume(y) and income (x)，we got the following result by OLS (Standard error in parentheses)
we have n=57 R²=0.9328 DW=1.8403
（1）If the model has serial correlation，please calculate the correlation coefficients about residual approximately
（2）Please write down the quasi-differencing model with no serial correlation
2.Consider an equation to explain wage in terms of working experience its square and education
suppose the estimator of is negative ，how do you explain the partial effect of
on
（2）Suppose ，
is an independent identically distributed sequence with expectation Zero and variance
，prove that {
} is a stationary sequence
