Time Series Analysis ARIMA Models 3

Question 1

What does 'invertibility' mean in the context of an MA model?

Accepted Answer

The MA model can be expressed as an infinite AR model

Answer

The series can be reversed in time

Answer

The model parameters are negative

Answer

The forecast errors are zero

Question 2

Over-differencing a time series typically introduces which problem?

Accepted Answer

Negative autocorrelation at lag 1

Answer

Perfect stationarity

Answer

Trend reappearance

Answer

Heteroscedasticity

Question 3

What is a unit root in a time series?

Accepted Answer

A characteristic root equal to 1, indicating non-stationarity

Answer

A root that stabilizes variance

Answer

A seasonal component with period 1

Answer

A constant mean value

Question 4

In ARIMA modeling, what does 'd=2' imply?

Accepted Answer

The series was differenced twice to achieve stationarity

Answer

There are 2 AR terms

Answer

There are 2 MA terms

Answer

The seasonal period is 2

Question 5

Which metric penalizes model complexity more heavily than AIC?

Accepted Answer

BIC (Bayesian Information Criterion)

Answer

MAE

Answer

RMSE

Answer

R-squared

Question 6

What is the key difference between AR and MA components in ARIMA?

Accepted Answer

AR uses past observations; MA uses past forecast errors

Answer

AR uses future values; MA uses current values

Answer

AR is for stationary series; MA is for non-stationary

Answer

AR reduces variance; MA reduces bias