Exam 15: Time-Series Forecasting and Index Numbers

When a trucking firm uses the number of shipments for January of the previous year as the forecast for January next year, it is using a naïve forecasting model.

Naïve forecasting models have no useful applications because they do not take into account data trend, cyclical effects or seasonality.

Analysis of data for an autoregressive forecasting model produced the following tables: The actual values of this time series, y, were 228, 54, and 191 for May, June, and July, respectively.The forecast value predicted by the model for July is ___.

Two popular general categories of smoothing techniques are averaging models and exponential models.

Use of a smoothing constant value less than 0.5 in an exponential smoothing model gives more weight to ___.

The ratios of "actuals to moving averages" (seasonal indexes)for a time series are presented in the following table as percentages: The final (completely adjusted)estimate of the seasonal index for Q₁ is ___.

Using 2019 as the base year, the 2018 value of the Paasche Price Index is ___.

The following graph of a time-series data suggests a ___ trend.

The following graph of time-series data suggests a ___ trend.

Using a three-month moving average (with weights of 6, 3, and 1 for the most current value, next most current value and oldest value, respectively), the forecast value for October made at the end of September in the following time series would be___.

Fitting a linear trend to 36 monthly data points (January 2017 = 1, February 2017 = 2, March 2017 = 3, etc.)produced the following tables: The projected trend value for January 2020 is ___.

In exponential smoothing models, the value of the smoothing constant may be any number between ___.

Use of a smoothing constant value greater than 0.5 in an exponential smoothing model gives more weight to ___.

One of the main techniques for isolating the effects of seasonality is decomposition.

Jim Royo, manager of Billings Building Supply (BBS), wants to develop a model to forecast BBS's monthly sales (in $1,000's).He selects the dollar value of residential building permits (in $10,000)as the predictor variable.An analysis of the data yielded the following tables: Using $\alpha$ = 0.05 the critical value of the Durbin-Watson statistic, d_U, is ___.

Using 2016 as the base year, the 2018 value of a simple price index for the following price data is ___.

One of the ways to overcome the autocorrelation problem in a regression forecasting model is to transform the variables by taking the first-order differences.

A time series with forecast values and error terms is presented in the following table.The mean error (ME)for this forecast is ___.

Using a three-month moving average, the forecast value for November in the following time series is ___.

The forecast value for September was 10.6 and the actual value turned out to be 7.Using exponential smoothing with $\alpha$ = 0.20, the forecast value for October would be ___.