    # Which of the following is not considered a quantitative forecasting method?

If there are no data available, or if the data available are not relevant to the forecasts, then qualitative forecasting methods must be used. These methods are not purely guesswork—there are well-developed structured approaches to obtaining good forecasts without using historical data. These methods are discussed in Chapter .

Quantitative forecasting can be applied when two conditions are satisfied:

1. numerical information about the past is available;
2. it is reasonable to assume that some aspects of the past patterns will continue into the future.

There is a wide range of quantitative forecasting methods, often developed within specific disciplines for specific purposes. Each method has its own properties, accuracies, and costs that must be considered when choosing a specific method.

Most quantitative prediction problems use either time series data (collected at regular intervals over time) or cross-sectional data (collected at a single point in time). In this book we are concerned with forecasting future data, and we concentrate on the time series domain.

### Predictor variables and time series forecasting

Predictor variables are often useful in time series forecasting. For example, suppose we wish to forecast the hourly electricity demand (ED) of a hot region during the summer period. A model with predictor variables might be of the form \begin{align*} \text{ED} = & f(\text{current temperature, strength of economy, population,}\\ & \qquad\text{time of day, day of week, error}). \end{align*} The relationship is not exact — there will always be changes in electricity demand that cannot be accounted for by the predictor variables. The “error” term on the right allows for random variation and the effects of relevant variables that are not included in the model. We call this an explanatory model because it helps explain what causes the variation in electricity demand.

Because the electricity demand data form a time series, we could also use a time series model for forecasting. In this case, a suitable time series forecasting equation is of the form $\text{ED}_{t+1} = f(\text{ED}_{t}, \text{ED}_{t-1}, \text{ED}_{t-2}, \text{ED}_{t-3},\dots, \text{error}),$ where $$t$$ is the present hour, $$t+1$$ is the next hour, $$t-1$$ is the previous hour, $$t-2$$ is two hours ago, and so on. Here, prediction of the future is based on past values of a variable, but not on external variables which may affect the system. Again, the “error” term on the right allows for random variation and the effects of relevant variables that are not included in the model.

There is also a third type of model which combines the features of the above two models. For example, it might be given by $\text{ED}_{t+1} = f(\text{ED}_{t}, \text{current temperature, time of day, day of week, error}).$ These types of mixed models have been given various names in different disciplines. They are known as dynamic regression models, panel data models, longitudinal models, transfer function models, and linear system models (assuming that $$f$$ is linear). These models are discussed in Chapter .

An explanatory model is useful because it incorporates information about other variables, rather than only historical values of the variable to be forecast. However, there are several reasons a forecaster might select a time series model rather than an explanatory or mixed model. First, the system may not be understood, and even if it was understood it may be extremely difficult to measure the relationships that are assumed to govern its behaviour. Second, it is necessary to know or forecast the future values of the various predictors in order to be able to forecast the variable of interest, and this may be too difficult. Third, the main concern may be only to predict what will happen, not to know why it happens. Finally, the time series model may give more accurate forecasts than an explanatory or mixed model.

The model to be used in forecasting depends on the resources and data available, the accuracy of the competing models, and the way in which the forecasting model is to be used.

Which of the following is not a qualitative forecasting technique?

a. Surveys of consumer expenditure plans
b. Perspectives of foreign advisory councils
c. Consumer intention polling
d. Time-series analysis
• The first step in time-series analysis is to

a. perform preliminary regression calculations.
b. calculate a moving average.
c. plot the data on a graph.
d. identify relevant correlated variables.
• Forecasts are referred to as naive if they

a. are based only on past values of the variable.
b. are short-term forecasts.
c. are long-term forecasts.
d. generally result in incorrect forecasts.
• Time-series analysis is based on the assumption that

a. random error terms are normally distributed.
b. there are dependable correlations between the variable to be forecast and other independent variables.
c. past patterns in the variable to be forecast will continue unchanged into the future.
d. the data do not exhibit a trend.
• Which of the following is not one of the four types of variation that is estimated in time-series analysis?

a. Predictable
b. Trend
c. Cyclical
d. Irregular
• The cyclical component of time-series data is usually estimated using

a. linear regression analysis.
b. moving averages.
c. exponential smoothing.
d. qualitative methods.
• In time-series analysis, which source of variation can be estimated by the ratio-to-trend method?

a. Cyclical
b. Trend
c. Seasonal
d. Irregular
• If regression analysis is used to estimate the linear relationship between the natural logarithm of the variable to be forecast and time, then the slope estimate is equal to

a. the linear trend.
b. the natural logarithm of the rate of growth.
c. the natural logarithm of one plus the rate of growth.
d. the natural logarithm of the square root of the rate of growth.
• The use of a smoothing technique is appropriate when

a. random behavior is the primary source of variation.
b. seasonality is present.
c. data exhibit a strong trend.
d. all of the above are correct.
• The greatest smoothing effect is obtained by using

a. a moving average based on a small number of periods.
b. exponential smoothing with a small weight value.
c. the root-mean-square error.
d. the barometric method.
• The root-mean-square error is a measure of

a. sample size.
b. moving average periods.
c. exponential smoothing.
d. forecast accuracy.
• Barometric methods are used to forecast

a. seasonal variation.
b. secular trend.
c. cyclical variation.
d. irregular variation.
• A leading indicator is a measure that usually

a. changes at the same time and in the same direction as the general economy.
b. responds to a change in the general economy after a time lag.
c. changes in the same direction as the general economy before the general economy changes.
d. has all of the properties listed above.
• If 3 of the leading indicators move up, 2 move down, and the remaining 6 are constant, then the diffusion index is

a. 3/6 = 50%
b. 3/11 = 27%
c. 5/11 = 45%
d. 6/11 = 55%
• A single-equation econometric model of the demand for a product is a ________ equation in which the quantity demanded of the product is an ________ variable.

### Which of the following is quantitative method of forecasting?

Some of the quantitative methods of forecasting are:- Test Marketing 2. Time Series Analysis 3. Moving Average Method 4. Exponential Smoothing Method 5.

### What are the four quantitative forecasting methods?

While there are a wide range of frequently used quantitative budget forecasting tools, in this article we focus on the top four methods: (1) straight-line, (2) moving average, (3) simple linear regression, and (4) multiple linear regression.

### Which of the following is not qualitative forecasting method?

Time-series analysis is not a qualitative forecasting technique.

### Which of the following is not a quantitative technique?

• 