Questions for Critical Thinking

QUESTIONS FOR CRITICAL THINKING 3

Chapter 6:

a. Discussion Questions: 1.

(a). What is forecasting? Why is it so important in the management of business firms and other enterprises?

Forecasting is the attempt to predict the future of economic activity for a firm with the aim to reduce risk or uncertainty that the firm faces in its short-term operational decision making and in planning for its long-term growth. Forecasting is largely important because it helps the firm make decisions using macroforecasts of general economic activity as inputs for their microforecasts of the industry's and firm's demand and sales. In other words, forecasting helps the firm to decide its marketing strategy, sales forecast, needs for production, and helps to predict finances such as cash flow, profits, and the need for and cost of outside financing. Forecasting also helps the firm to make decisions on personnel based on those forecasts. Forecasting also assists in the long-term future of a firm by helping the firm make decisions on future expenditures for plant and equipment to meet the long-term growth plan and strategy of the firm (Salvatore, 2012, p. 218).

(b). What are the different types of forecasting?

There are many different types of forecasting. Salvatore (2012) points out that forecasting techniques have a very broad range from expensive to inexpensive to simple to very complex (p. 218). Some forecasting techniques are basically qualitative while others are quantitative. The text focuses on qualitative forecasts, time-series forecasts, forecasts based on smoothing techniques such as moving averages, barometric forecasts based on leading indicators, econometric forecasts based on econometric models, and input-output forecasting (Salvatore, 2012, p. 219).

(c). How can the firm determine the most suitable forecasting method to use?

Which forecasting method a firm chooses depends on several things according to Salvatore (2012):

1. the cost of preparing the forecast and the benefit that results from its use

2. the lead time in decision making

3. the time period of the forecast (short or long term)

4. the level of accuracy required

5. the quality and availability of the data

6. the level of complexity of the relationships to be forecast

The text also points out that in general, "the greater the level of accuracy required and the more complex the relationships to be forecast, the more sophisticated and expensive will be the forecasting exercise" (Salvatore, 2012, p. 219). By considering the advantages and disadvantages, and by understand the clear purpose for the forecast, managers can chose the method or combination of methods that best suit the firm.

b. Problems: 5, 6*, and 7**.

5. Using the index (with 1985=100) on housing starts in the United States per year from 1986 to 1997 given in the table below, forecast the index for 1998 using a three-year and a five-year moving average. Which of your estimates is better if the actual index of housing starts in the United States for 1998 is 163?

3-year Moving Average (MA) to forecast the index for 1998:

MA(3 years)=142+156+162

MA(3)=460

MA=460/3

MA=153.33

Forecast for 1998: 153

5-year Moving Average (MA) to forecast the index for 1998:

MA(5 years)=162+156+142+146+125

MA(5)=731

MA=731/5

MA=146.20

Forecast for 1998: 146

In order to calculate which forecast is more accurate, we must calculate RMSE.

RMSE=√(A-F)2/n

For 3 year forecast:

A=163

F=153

n=3 years

√(163-153)2/3

√(100/3)

RMSE=5.77

For 5 year forecast:

A=163

F=146

N=5

√(163-146)2/5

√(21)2/5

√441/5

√88.2

RMSE=9.39

Based on the above data, the 3 year moving average of 153 is closer to the actual index of housing starts in the U.S. for 1998 being at 163. Additionally, the Root Mean Square Error is smaller for the 3 year forecast at 5.77 than for the 5 year forecast at 9.39. Therefore, the 3 year average appears to be better in this case. However, as Salvatore (2012) points out, "the greater the number of periods used in the moving average, the greater is the smoothing effect because each new observation receives less weight" (p. 231).

6. (a)Forecast the index of housing starts in the United States in 1998 by exponential smoothing with w=.3 and w=.7

* For example, for w=0.3

Year A F (F=(w)A+(1-w)F taken from p. 232 of the text)

1986 116 128(=the average of the index from 1986-1997)

1987 122 124.40(=0.3x116+(1-0.3)x128)

1988 121 123.68(=.3x122+(1-.3)x124.40)

1989 121 122.88(=.3x121+(1-.3)x123.68)

Using the same formulas for w=.3, and w=.7 and with the forecast in the beginning of 128, here is a copy and paste from the formula in excel in the interest of saving time:

year actual forecast1 w=.3 forecast2 w=.7

1986 116.00 128.00 128.00

1987 122.00 124.40 119.60

1988 121.00 123.68 121.28

1989 121.00 122.88 121.08