chunty

Alexis Popov, CFA, is analyzing monthly data. Popov has estimated the model xt = b0 + b1 × xt-1 + b2 × xt-2 + et. The researcher finds that the residuals have a significant ARCH process. The best solution to this is to:

A) re-estimate the model with generalized least squares.

B) re-estimate the model using only an AR(1) specification.

C) re-estimate the model using a seasonal lag.

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If the residuals have an ARCH process, then the correct remedy is generalized least squares which will allow Popov to better interpret the results.

chunty

Alexis Popov, CFA, has estimated the following specification: xt = b0 + b1 × xt-1 + et. Which of the following would most likely lead Popov to want to change the model’s specification?

A) Correlation(et, et-2) is significantly different from zero.

B) Correlation(et, et-1) is not significantly different from zero.

C) b0 < 0.

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If correlation(et, et-2) is not zero, then the model suffers from 2nd order serial correlation. Popov may wish to try an AR(2) model. Both of the other conditions are acceptable in an AR(1) mod

chunty

Alexis Popov, CFA, wants to estimate how sales have grown from one quarter to the next on average. The most direct way for Popov to estimate this would be:

A) an AR(1) model.

B) a linear trend model.

C) an AR(1) model with a seasonal lag.

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If the goal is to simply estimate the dollar change from one period to the next, the most direct way is to estimate xt = b0 + b1 × (Trend) + et, where Trend is simply 1, 2, 3, ....T. The model predicts a change by the value b1 from one period to the next.