Slide 1 Business and Economic Forecasting Chapter 5 Business and Economic Forecasting is a critical managerial activity which comes in two forms: Quantitative Forecasting Quantitative Forecasting +2.1047% +2.1047% Gives the precise amount or percentage Qualitative Forecasting Qualitative Forecasting Gives the expected direction Up, down, or about the same 2008 Thomson * South-Western
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Slide 1 Business and Economic Forecasting Chapter 5 Business and Economic Forecasting is a critical managerial activity which comes in two forms: lQuantitative.
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Slide 1
Business and Economic ForecastingChapter 5
Business and Economic Forecasting is a critical managerial activity which comes in two forms:
Quantitative Forecasting +2.1047%Quantitative Forecasting +2.1047% Gives the precise amount
or percentage
Qualitative ForecastingQualitative Forecasting Gives the expected direction Up, down, or about the same
2008 Thomson * South-Western
Slide 2
Managerial ChallengeExcess Fiber Optic
Capacity• High-speed data installation
grew exponentially• But adoption follows a typical
S-curve pattern, similar to the adoption rate of TV
• Access grew too fast, leading to excess capacity around the time of the tech bubble in 2001
• The challenge is to predict demand properly
1940 1960 1980 2000 2020
100% Color TVs
InternetAccess
Slide 3
The Significance of Forecasting• Both public and private enterprises operate under
conditions of uncertainty. • Management wishes to limit this uncertainty by predicting
changes in cost, price, sales, and interest rates. • Accurate forecasting can help develop strategies to
promote profitable trends and to avoid unprofitable ones. • A forecast is a prediction concerning the future. Good
forecasting will reduce, but not eliminate, the uncertainty that all managers feel.
Slide 4
Hierarchy of Forecasts• The selection of forecasting techniques depends in part on
the level of economic aggregation involved.
• The hierarchy of forecasting is:
• National Economy (GDP, interest rates, inflation, etc.)
»sectors of the economy (durable goods) industry forecasts (all automobile manufacturers)
> firm forecasts (Ford Motor Company)
»Product forecasts (The Ford Focus)
Slide 5
Forecasting CriteriaThe choice of a particular forecasting method depends on several criteria:
1.costs of the forecasting method compared with its gains
2.complexity of the relationships among variables
3.time period involved
4.lead time between receiving information and the decision to be made
5.accuracy needed in forecast
Slide 6
Accuracy of Forecasting• The accuracy of a forecasting model is measured by how
close the actual variable, Y, ends up to the forecasting variable, Y.
• Forecast error is the difference. (Y - Y)
• Models differ in accuracy, which is often based on the square root of the average squared forecast error over a series of N forecasts and actual figures
• Called a root mean square error, RMSE.
»RMSE = { (Y - Y)2 / N }
^
^
^
Slide 7
Quantitative Forecasting
• Deterministic Time Series » Looks For Patterns» Ordered by Time» No Underlying Structure
Direction of sales can be indicated by other variables.
TIME
Index of Capital Goods
peakPEAK Motor Control Sales
4 Months
Example: Index of Capital Goods is a “leading indicator”There are also lagging indicators and coincident indicators
Qualitative Forecasting10. Barometric Techniques
Slide 28
LEADING INDICATORS*» M2 money supply (-14.4)
» S&P 500 stock prices (-11.1)
» Building permits (-15.4)
» Initial unemployment claims (-12.9)
» Contracts and orders for plant and equipment (-7.3)
COINCIDENT INDICATORS» Nonagricultural payrolls
(+.8)» Index of industrial
production (-1.1)» Personal income less
transfer payment (-.4)
LAGGING INDICATORS» Prime rate (+17.9)
» Change in labor cost per unit of output (+6.4)
*Survey of Current Business, 1995 See pages 182-183 in the textbook
Time given in months from change
Slide 29
Handling Multiple IndicatorsDiffusion IndexDiffusion Index: Suppose 11 forecasters predict stock prices in 6 months, up or down. If 4 predict down and seven predict up, the Diffusion Index is 7/11, or 63.3%.
• above 50% is a positive diffusion index
Composite IndexComposite Index: One indicator rises 4% and another rises 6%. Therefore, the Composite Index is a 5% increase.
• used for quantitative forecasting
Slide 30
Qualitative Forecasting11. Surveys and Opinion Polling Techniques
• Sample bias--» telephone, magazine
• Biased questions--» advocacy surveys
• Ambiguous questions
• Respondents may lie on questionnaires
New Products have nohistorical data -- Surveyscan assess interest in newideas.
Survey Research Centerof U. of Mich. does repeatsurveys of households onBig Ticket items (Autos)
Survey Research Centerof U. of Mich. does repeatsurveys of households onBig Ticket items (Autos)
Common Survey Problems
Slide 31
Qualitative Forecasting12. Expert Opinion
The average forecast from several experts is a Consensus Forecast.» Mean» Median» Mode» Truncated Mean» Proportion positive or negative
Slide 32
EXAMPLES:
• IBES, First Call, and Zacks Investment -- earnings forecasts of stock analysts of companies
• Conference Board – macroeconomic predictions
• Livingston Surveys--macroeconomic forecasts of 50-60 economists
Individual economists tend to be less accurate over time than the ‘consensus forecast’.
Slide 33
13. Econometric Models• Specify the variables in the model
• Estimate the parameters » single equation or perhaps several stage methods
»Qd = a + b•P + c•I + d•Ps + e•Pc
• But forecasts require estimates for future prices, future income, etc.
• Often combine econometric models with time series estimates of the independent variable.
» Garbage in Garbage out
Slide 34
example • Qd = 400 - .5•P + 2•Y + .2•Ps
» anticipate pricing the good at P = $20
» Income (Y) is growing over time, the estimate is: Ln Yt = 2.4 + .03•T, and next period is T = 17.
• Y = e2.910 = 18.357
» The prices of substitutes are likely to be P = $18.
• Find Qd by substituting in predictions for P, Y, and Ps
• Hence Qd = 430.31
Slide 35
14. Stochastic Time Series• A little more advanced methods incorporate into time
series the fact that economic data tends to drift
yt = + yt-1 + t
• In this series, if is zero and is 1, this is essentially the naïve model. When is zero, the pattern is called a random walk.
• When is positive, the data drift. The Durbin-Watson statistic will generally show the presence of autocorrelation, or AR(1), integrated of order one.
• One solution to variables that drift, is to use first differences.
Slide 36
Cointegrated Time Series• Some econometric work includes several stochastic
variable, each which exhibits random walk with drift» Suppose price data (P) has positive drift» Suppose GDP data (Y) has positive drift» Suppose the sales is a function of P & Y
» Salest = a + bPt + cYt
» It is likely that P and Y are cointegrated in that they exhibit comovement with one another. They are not independent.
» The simplest solution is to change the form into first differences as in: Salest = a + bPt + cYt