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Exam 12: Time Series Analysis and Forecasting

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Question 31

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Which of the following is not a method for dealing with seasonality in data

A) Winter's exponential smoothing model
B) deseasonalizing the data,using any forecasting model,then reseasonalizing the data
C) multiple regression with lags for the seasons
D) multiple regression with dummy variables for the seasons

E) B) and D)
F) A) and B)

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Question 32

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As is the case with residuals from regression,the forecast errors for nonregression methods will always average to zero

A) True
B) False

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Question 33

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A linear trend means that the time series variable changes by a:

If we use a value close to 1 for the smoothing constant $a$ in a simple exponential smoothing model,then we expect the model to respond very slowly to changes in the level.

Models such as moving average,exponential smoothing,and linear trend use only:

To calculate the five-period moving average for a time series,we average the values in the two preceding periods,and the values in the three following time periods.

If a time series exhibits an exponential trend,then a plot of its logarithm should be approximately linear.

In a random walk model the

Simple exponential smoothing is appropriate for a series without a pronounced trend or seasonality.

The moving average method is perhaps the simplest and one of the most frequently-used extrapolation methods.

A time series is any variable that is measured over time in sequential order.

The trend line $\hat { Y } = 0.65 + 0.005 t$ was calculated from quarterly data for 2000 - 2004,where t = 1 for the first quarter of 2000.The trend value for the second quarter of the year 2005 is 0.75.

Correlogram is a bar chart of autocorrelation at different lags.

Econometric forecasting models,also called causal models,use regression to forecast a time series variable by using other explanatory time series variables.

A time series can consist of four different components: trend,seasonal,cyclical,and random (or noise).

You will always get more accurate forecasts by using more complex forecasting methods.

The moving average method can also be referred to as a (n) method.

The most common form of autocorrelation is positive autocorrelation,where large observations tend to follow large observations and small observations tend to follow small observations.

When using Holt's model,choosing values of the smoothing constant $\beta$ That are near 1 will result in forecast models which

Forecasting software packages typically report several summary measures of the forecasting error.The most important of these are MAE (mean absolute error),RMSE (root mean square error),and MAPE (mean absolute percentage error).