Managerial economics economic tools for todays decision makers 7th edtion by keat young and erfle chapter 05

5-5 Regression Analysis • Regression analysis: a procedure commonly used by economists to estimate consumer demand with available data Two types of regression: – cross-sectional: analyz

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Chapter 5

Demand Estimation and Forecasting

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Chapter Outline

• Regression analysis

• Limitation of regression analysis

• The importance of business forecasting

• Prerequisites of a good forecast

• Forecasting techniques

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• Explain basic smoothing methods of

forecasting, such as the moving average

and exponential smoothing

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Data Collection

• Statistical analyses are only as good as the accuracy and appropriateness of the sample

of information that is used.

• Several sources of data for business

analysis:

– buy from data providers (e.g ACNielsen, IRI)

– perform a consumer survey

– focus groups

– technology: point-of-sale data sources

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Regression Analysis

• Regression analysis: a procedure

commonly used by economists to estimate consumer demand with available data

Two types of regression:

– cross-sectional: analyze several variables for a single period of time

– time series data: analyze a single variable over multiple periods of time

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• Regression equation: linear, additive

eg: Y = a + b1X1 + b2X2 + b3X3 + b4X4

Y: dependent variable

a: constant value, y-intercept

Xn: independent variables, used to explain Y

bn: regression coefficients (measure impact

of independent variables)

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• Interpreting the regression results:

Coefficients:

– negative coefficient shows that as the

independent variable (Xn) changes, the variable (Y) changes in the opposite direction

– positive coefficient shows that as the

independent variable (Xn) changes, the dependent variable (Y) changes in the same direction

– The regression coefficients are used to compute the elasticity for each variable

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• Statistical evaluation of regression results:

– t-test: test of statistical significance of each

estimated coefficient (whether the coefficient is significantly different from zero)

t 

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• Statistical evaluation of regression results:

– ‘rule of 2’: if absolute value of t is greater than 2, estimated coefficient is significant at the 5%

level (for large samples-for small samples, need

to use a t table)– if coefficient passes t-test, the variable has a

significant impact on demand

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• Statistical evaluation of regression results

– R2 (coefficient of determination): percentage of variation in the variable (Y) accounted for by variation in all explanatory variables (Xn)

R 2 value ranges from 0.0 to 1.0 The closer to 1.0, the greater the explanatory power of the regression.

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• Statistical evaluation of regression results

– F-test: measures statistical significance of the

entire regression as a whole (not each coefficient)

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• Steps for analyzing regression results

– check coefficient signs and magnitudes

– compute elasticity coefficient

– determine statistical significance

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• Textbook example: Management lessons

from estimating demand for pizza

– demand for pizza affected by

1 price of pizza

2 price of complement (soda)

– managers can expect price decreases to lead to lower revenue

– tuition and location are not significant

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• Challenge 1: Identification problem:

– The estimation of demand may produce biased results due to simultaneous shifting of supply and demand curves

– Solution: use of advanced correction techniques, such as two-stage least squares and indirect

least squares may compensate for the bias

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• Challenge 2: Multicollinearity problem

– Two or more independent variables are highly

correlated, thus it is difficult to separate the effect each has on the dependent variable

– Solution: a standard remedy is to drop one of the closely related independent variables from the

regression

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• Challenge 3: Autocorrelation problem

– Also known as serial correlation, occurs when the dependent variable relates to the Y variable

according to a certain pattern– Note: possible causes include omitted variables,

or non-linearity; Durbin-Watson statistic is used

to identify autocorrelation– Solution: to correct autocorrelation consider

transforming the data into a different order of magnitude or introducing leading or lagging data

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Forecasting

• “Forecasting is very difficult, especially into the future.”

• Common subjects of business forecasts:

– gross domestic product (GDP)

– components of GDP

• examples: consumption expenditure, producer durable equipment expenditure, residential construction

– industry forecasts

• example: sales of products across an industry

– sales of a specific product

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Forecasting

• 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

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– amount of historical data available

– time allowed to prepare forecast

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Forecasting Techniques

• Six forecasting techniques

– expert opinion

– opinion polls and market research

– surveys of spending plans

– economic indicators

– projections

– econometric models

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• Approaches to forecasting

– qualitative forecasting is based on judgments

expressed by individuals or group

– quantitative forecasting utilizes significant

amounts of data and equations

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• Approaches to quantitative forecasting:

– nạve 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

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• Expert opinion techniques

– Jury of executive opinion: forecasts generated

by a group of corporate executives assembled together

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• Expert opinion techniques

– 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

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• Opinion polls: sample populations are

surveyed to determine consumption trends

– may identify changes in trends

– choice of sample is important

– questions must be simple and clear

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• Market research: is closely related to

opinion polling and 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

– characteristics the consumer thinks are most

important in the purchasing decision

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• Surveys of spending plans: yields

information about ‘macro-type’ data relating

to the economy, especially:

– consumer intentions

• examples: Survey of Consumers (University of Michigan), Consumer Confidence Survey (Conference Board)

– inventories and sales expectations

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• Economic indicators: a barometric

method of forecasting designed to alert

business to changes in conditions

– leading, coincident, and lagging indicators

– composite index: one indicator alone may not be very reliable, but a mix of leading indicators may

be effective

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• Leading indicators predict 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

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• Additional leading indicators to predict

future economic activity

– 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

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• Coincident indicators identify trends in

current economic activity

– employees on nonagricultural payrolls

– personal income less transfer payments

– industrial production

– manufacturing and trade sales

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• Lagging indicators confirm swings in past

economic activity

– average duration of unemployment, weeks

– ratio, manufacturing and trade inventories to

sales– change in labor cost per unit of output,

manufacturing (%)

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• Additional lagging indicators confirm swings

in past economic activity

– 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

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• 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

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• Trend projections: a form of nạve

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

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• Compound growth rate: forecasting by

projecting the average growth rate of the

past into the future

– provides a relatively simple and timely forecast– appropriate when the variable to be predicted

increases at a constant percentage

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• Visual time series projections: plotting

observations on a graph and viewing the shape of the data and any trends

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• Time series analysis: a nạve method of

forecasting from past data by using least

squares statistical methods to identify

trends, cycles, seasonality, and irregular

movements

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– usually reasonably reliable in the short run

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• Time series components: seasonality

– need to identify and remove seasonal factors,

using moving averages to isolate those factors– remove seasonality by dividing data by seasonal factor

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• Time series components: trend

– to remove trend line, use least squares method– possible best-fit line styles:

straight line: Y = a + b(t) exponential line: Y = ab t

quadratic line: Y = a + b(t) + c(t) 2

– choose one with best R2

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• Time series components: cyclical, random

– isolate cyclical components by smoothing with a moving average

– random factors cannot be predicted and should

be ignored

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• 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

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• 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

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• Econometric models: causal or

explanatory models of forecasting

– regression analysis

– multiple equation systems

• endogenous variables: dependent variables that may influence other dependent variables

• exogenous variables: from outside the system, truly independent variables

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– I = Domestic inflation rate minus foreign inflation rate

– R = Domestic nominal interest rate minus foreign nominal

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Summary

• Regression analysis is a primary tool used by

businesses to understand demand

• Reliable input data and proper estimation and

evaluation are needed

• Forecasting is an important activity in many

organizations In business, forecasting is a

necessity

• This chapter summarized and discussed six of the major forecasting techniques used by businesses

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