Exam 12: Time Series Analysis and Forecasting

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.

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

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.

Correlogram is a bar chart of autocorrelation at different lags.

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.

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

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

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.

In a random walk model the

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

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

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

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

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

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

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).

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

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

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

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