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Chapter 5:

Demand Estimation and Forecasting

Demand Estimationand Forecasting

• Regression Analysis• Problems in Use of Regression Analysis• Subjects of Forecasts• Prerequisites of a Good Forecast• Forecasting Techniques

Data Collection

• Data for studies pertaining to countries, regions, or industries are readily available and reliable.

• Data for analysis of specific product categories may be more difficult to obtain.– Buy from data providers (e.g. ACNielsen, IRI)– Perform a consumer survey– Focus groups– Technology: Point-of-sale, bar codes, RFID

Regression Analysis

• Regression Analysis: A procedure commonly used by economists to estimate consumer demand with available data.– Cross-Sectional Data: provide information on

variables for a given period of time.– Time Series Data: give information about variables

over a number of periods of time.

Regression Analysis

• Regression equation: linear, additive– Y = a + b1X1 + b2X2 + b3X3 + b4X4

– Y: dependent variable, amount to be determined– a: constant value, y-intercept– Xn: independent, explanatory variables, used to

explain the variation in the dependent variable– bn: regression coefficients (measure impact of

independent variables)

Regression Analysis

• Regression Results– Negative coefficient shows that as the

independent variable (Xn) changes, the quantity demanded changes in the opposite direction.

– Positive coefficient shows that as the independent variable (Xn) changes, the quantity demanded changes in the same direction.

– Magnitude of regression coefficients is measured by elasticity of each variable.

Regression Analysis

• Statistical evaluation of regression results– t-test: test of statistical significance of each

estimated regression coefficient

– b: estimated coefficient– SEb: standard error of the estimated coefficient– Rule of 2: if absolute value of t is greater than 2,

estimated coefficient is significant at the 5% level– If coefficient passes t-test, the variable has a true

impact on demand

b̂SE

b̂ t

Regression Analysis

• Statistical evaluation of regression results– Coefficient of determination (R2): percentage of

variation in the dependent variable (Y) accounted for by variation in all explanatory variables (Xn)• Value ranges from 0.0 to 1.0• Closer to 1.0, the greater the explanatory power of the

regression equation

– F-test: measures statistical significance of the entire regression as a whole (not each coefficient)

Regression Results

• Steps for analyzing regression results– Check signs and magnitudes– Compute elasticity coefficients– Determine statistical significance

Subjects of Forecasts

• Gross Domestic Product (GDP)• Components of GDP– E.g. consumption expenditure, producer durable

equipment expenditure, residential construction• Industry Forecasts– Sales of products across an industry

• Sales of a specific product

Prerequisites of a Good Forecast

• A good forecast should:– be consistent with other parts of the business– be based on knowledge of the relevant past– consider the economic and political environment

as well as changes– be timely

Forecasting Techniques

• Factors in choosing the right forecasting technique:– Item to be forecast– Interaction of the situation with the characteristics

of available forecasting methods– Amount of historical data available– Time allowed to prepare forecast

Forecasting Techniques

• Expert opinion• Opinion polls and market research• Surveys of spending plans• Economic indicators• Projections• Econometric models

Forecasting Techniques

• Qualitative forecasting is based on judgments of individuals or groups.

• Quantitative forecasting utilizes significant amounts of prior data as a basis for prediction.

• Naïve forecasting projects past data without explaining future trends.

• Causal (or explanatory) forecasting attempts to explain the functional relationships between the dependent variable and the independent variables.

Forecasting Techniques

• Expert opinion techniques– Jury of executive opinion: Forecasts generated by

a group of corporate executives assembled together. The major drawback is that persons with strong personalities may exercise disproportionate influence.

– The Delphi Method: A form of expert opinion forecasting that uses a series of questions and answers to obtain a consensus forecast, where experts do not meet.

Forecasting Techniques

• Opinion polls: Sample populations are surveyed to determine consumption trends.– choice of sample is important– questions must be simple and clear

• Market research is closely related to opinion polling. – Market research will indicate “not only why the consumer

is or is not buying, but also who the consumer is, how he or she is using the product, and what characteristics the consumer thinks are most important in the purchasing decision.”

Forecasting Techniques

• Surveys of spending plans: seek information about “macro-type” data relating to the economy.

• Consumer intentions– Survey of Consumers, Survey Research Center,

University of Michigan– Consumer Confidence Survey, The Conference

Board• Inventories and sales expectations

Forecasting Techniques

• Economic Indicators: A barometric method of forecasting designed to alert business to changes in economic conditions.– Leading, coincident, and lagging indicators– One indicator may not be very reliable, but a

composite of leading indicators may be used for prediction.

Forecasting Techniques• Leading Indicators predict changes in future economic activity

– Average hours, manufacturing– Initial claims for unemployment insurance– Manufacturers’ new orders for consumer goods and materials– Vendor performance, slower deliveries diffusion index– Manufacturers’ new orders, nondefense capital goods– Building permits, new private housing units– Stock prices, 500 common stocks– Money supply, M2– Interest rate spread, 10-year Treasury bonds minus federal funds– Index of consumer expectations

Forecasting Techniques• Coincident Indicators identify peaks and troughs in economic

activity– Employees on nonagricultural payrolls– Personal income less transfer payments– Industrial production– Manufacturing and trade sales

• Lagging Indicators confirm upturns and downturns in economic activity– Average duration of unemployment, weeks– Ratio, manufacturing and trade inventories to sales– Change in labor cost per unit of output, manufacturing (%)– Average prime rate charged by banks– Commercial and industrial loans outstanding– Ratio, consumer installment credit outstanding to personal income– Change in consumer price index for services

Forecasting Techniques

• General rule of thumb: if, after a period of increases, the leading indicator index sustains three consecutive declines, a recession (or a slowing) will follow.

• Economic indicators have predicted each recession since 1948.

Forecasting Techniques

• Economic Indicators Drawbacks– Leading indicator index has forecast a recession

when none ensued. – A change in the index does not indicate the

precise size of the decline or increase.– The data are subject to revision in the ensuing

months.

Forecasting Techniques

• Trend projections: A form of naïve forecasting that projects trends from past data without taking into consideration reasons for the change.– Compound growth rate– Visual time series projections– Least squares time series projection

Forecasting Techniques

• Compound growth rate: Forecasting by projecting the average growth rate of the past into the future. – Calculate the constant growth rate using available

data, then project this constant growth rate into the future.

– Provides a relatively simple and timely forecast– Appropriate when the variable to be predicted

increases at a constant percentage

Forecasting Techniques

• General compound growth rate formula:

E = B(1+i)n

• E = final value• n = years in the series• B = beginning value• i = constant growth rate

Forecasting Techniques

• Visual Time Series Projections: plotting observations on a graph and viewing the shape of the data and any trends.

Forecasting Techniques

• Time series analysis: A naïve method of forecasting from past data by using least squares statistical methods.

• Data collected of a number of periods usually exhibit certain characteristics:– Trends– Cyclical fluctuations– Seasonal fluctuations– Irregular movements

Forecasting Techniques

Yt = f(Tt, Ct, St, Rt)• Yt = Actual value of the data at time t• Tt = Trend component at t• Ct = Cyclical component at t• St = Seasonal component at t• Rt = Random component at t• Additive form: Yt = Tt + Ct + St + Rt

• Multiplicative form: Yt = (Tt)(Ct)(St)(Rt)

Forecasting Techniques

• Must decompose the time series into its four components– Remove seasonality– Compute trend– Isolate cycle– Cannot do anything with random component

Forecasting Techniques

• Seasonality: need to identify and remove seasonal factors, using moving averages to isolate those factors.

• Remove seasonality by dividing data by seasonal factor

Forecasting Techniques

• Trend Line: use least squares method• Possible best-fit line styles:– Straight Line: Y = a + b(t)– Exponential Line: Y = abt

– Quadratic Line: Y = a + b(t) + c(t)2

Forecasting Techniques

• Cycle and Random Elements– Random factors cannot be predicted and should

be ignored– Isolate cycle by smoothing with a moving average

Forecasting Techniques

• Smoothing Techniques– Moving Average– Exponential Smoothing

• Work best when:– No strong trend in series– Infrequent changes in direction of series– Fluctuations are random rather than seasonal or

cyclical

Forecasting Techniques

• Moving Average: average of actual past results used to forecast one period ahead

Et+1 = (Xt + Xt-1 + … + Xt-N+1)/N

• Et+1 : forecast for next period• Xt, Xt-1 : actual values at their respective times• N: number of observations included in average

Forecasting Techniques

• Exponential Smoothing: allows for decreasing importance of information in the more distant past, through geometric progression

Et+1 = w·Xt + (1-w) · Et

• w: weight assigned to an actual observation at period t

Forecasting Techniques

• Econometric Models: causal or explanatory models of forecasting– Regression analysis– Multiple equation systems• Endogenous variables: comparable to dependent

variables of single-equation model, but may influence other endogenous variables• Exogenous variables: from outside the system, truly

independent variables