CBE Forecasting 3

Question 1

When the smoothing parameter α in exponential smoothing is set close to 1, the forecast:

Accepted Answer

Places almost all weight on the most recent observation

Answer

Heavily weights all past observations equally

Answer

Reacts slowly to recent changes

Answer

Is equivalent to a long moving average

Question 2

Autocorrelation in forecast residuals most directly indicates:

Accepted Answer

The model has not fully captured the data's time-series structure

Answer

Heteroskedasticity in the error terms

Answer

The independent variables are collinear

Answer

Structural breaks in the data

Question 3

A leading indicator is best defined as a variable that:

Accepted Answer

Tends to change before the economy as a whole

Answer

Moves in tandem with the business cycle

Answer

Changes after the economy has shifted

Answer

Measures current economic output levels

Question 4

In an ARIMA(p,d,q) model, the 'd' parameter represents:

Accepted Answer

The order of differencing applied to achieve stationarity

Answer

The number of autoregressive terms

Answer

The number of moving-average terms

Answer

The lag length selected by AIC

Question 5

Forecast bias is present when:

Accepted Answer

The mean forecast error is consistently positive or negative

Answer

Error variance increases with the level of the series

Answer

Residuals exhibit changing variance over time

Answer

The model overfits in-sample data

Question 6

Which scenario calls for multiplicative rather than additive decomposition?

Accepted Answer

Seasonal variation grows proportionally with the trend level

Answer

Seasonal swings are roughly constant in absolute size

Answer

The series has no trend component

Answer

Residuals are normally distributed

Question 7

The Akaike Information Criterion (AIC) is used in forecasting primarily to:

Accepted Answer

Compare models by balancing fit and parsimony

Answer

Test for unit roots in a time series

Answer

Measure the mean absolute forecast error

Answer

Assess the normality of residuals