Câu hỏi: Trong báo cáo kết quả hồi quy có thông tin * D:Heteroscedasticity *CHI-SQ(1) được dùng để kiểm định khuyết tật nào trong các khuyết tật sau:

A. Phương sai sai số thay đổi

B. Sai số ngẫu nhiên không theo quy luật chuẩn

C. Tự tương quan

D. Dạng hàm sai

Câu 1: The EG-ADF test:

A. Is the similar to the DF-GLS test

B. Is a test for cointegration

C. Has as a limitation that it can only test if two variables, but not more than two, are cointegrated

D. Uses the ADF in the second step of its procedure

Câu 2: Under the five extended least squares assumptions, the homoskedasticity-only tdistribution in this chapter:

A. has a Student t distribution with n-2 degrees of freedom

B. has a normal distribution

C. has a Student t distribution with n degrees of freedom

Câu 3: The advantage of using heteroskedasticity-robust standard errors is that:

A. that they are easier to compute than the homoskedasticity-only standard errors

B. they produce asymptotically valid inferences even if you do not know the form of the conditional variance function.

C. it makes the OLS estimator BLUE, even in the presence of heteroskedasticity

D. they do not unnecessarily complicate matters, since in real-world applications, the functional form of the conditional variance can easily be found

Câu 4: The order of integration:

A. can never be zero

B. is the number of times that the series needs to be differenced for it to be stationary

C. is the value of \({\phi _1}\) in the quasi difference \(\Delta {Y_t} – {\phi _1}{Y_{t – 1}}\)

D. depends on the number of lags in the VAR specification

Câu 5: Cho hàm hồi quy với ESS=560, RSS = 202 từ đó tìm được R2, kết quả nào đúng trong các kết quả sau:

A. = 0,0734908

B. = 0,78563

C. = 0,06785

D. = 0,737557

Câu 6: Asymptotic distribution theory is:

A. not practically relevant, because we never have an infinite number of observations

B. only of theoretical interest

C. of interest because it tells you what the distribution approximately looks like in small samples

D. the distribution of statistics when the sample size is very large