1. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 1 Chapter 5 Demand Estimation and Forecasting

2. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 2 Overview Regression analysis Hazards with use of regression analysis Subjects of forecasts Prerequisites of a good forecast Forecasting techniques

3. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 3 Data collection  Data for studies pertaining to countries, regions, or industries are readily available 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

4. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 4 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

5. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 5 Regression analysis  Regression equation: linear, additive eg: Y = a + b 1 X 1 + b 2 X 2 + b 3 X 3 + b 4 X 4 Y: dependent variable a: constant value, y-intercept X n : independent variables, used to explain Y b n : regression coefficients (measure impact of independent variables)

6. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 6 Regression analysis  Interpreting the regression results: coefficients: negative coefficient shows that as the independent variable (X n ) changes, the variable (Y) changes in the opposite direction positive coefficient shows that as the independent variable (X n ) changes, the dependent variable (Y) changes in the same direction magnitude of regression coefficients is a measure of elasticity of each variable

7. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 7 Regression analysis  Statistical evaluation of regression results: t-test: test of statistical significance of each estimated coefficient b = estimated coefficient SE b = standard error of estimated coefficient

8. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 8 Regression analysis  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  if coefficient passes t-test, the variable has a true impact on demand

9. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 9 Regression analysis  Statistical evaluation of regression results R 2 (coefficient of determination): percentage of variation in the variable (Y) accounted for by variation in all explanatory variables (X n )  R 2 value ranges from 0.0 to 1.0  the closer to 1.0, the greater the explanatory power of the regression

10. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 10 Regression analysis  Statistical evaluation of regression results F-test: measures statistical significance of the entire regression as a whole (not each coefficient)

11. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 11 Regression results  Steps for analyzing regression results check coefficient signs and magnitudes compute implied elasticities determine statistical significance

12. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 12 Regression results  Example: 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

13. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 13 Regression problems  Identification problem: the estimation of demand may produce biased results due to simultaneous shifting of supply and demand curves  solution: use advanced correction techniques, such as two-stage least squares and indirect least squares

14. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 14 Regression problems  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

15. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 15 Regression problems  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

16. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 16 Forecasting  Examples: common subjects of business forecasts: gross domestic product (GDP) components of GDP eg consumption expenditure, producer durable equipment expenditure, residential construction industry forecasts eg sales of products across an industry sales of a specific product

17. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 17 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

18. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 18 Forecasting techniques  Factors in choosing the right forecasting technique: item to be forecast interaction of the situation with the forecasting methodology amount of historical data available time allowed to prepare forecast

19. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 19 Forecasting techniques  Approaches to forecasting qualitative forecasting is based on judgments expressed by individuals or group quantitative forecasting utilizes significant amounts of data and equations

20. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 20 Forecasting techniques  Approaches to forecasting 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

21. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 21 Forecasting techniques  Six forecasting techniques expert opinion opinion polls and market research surveys of spending plans economic indicators projections econometric models

22. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 22 Forecasting techniques  Expert opinion techniques Jury of executive opinion: forecasts generated by a group of corporate executives assembled together Drawback: persons with strong personalities may exercise disproportionate influence

23. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 23 Forecasting techniques  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

24. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 24 Forecasting techniques  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

25. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 25 Forecasting techniques  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

26. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 26 Forecasting techniques  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

27. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 27 Forecasting techniques  Economic indicators: a barometric method of forecasting designed to alert business to changes in conditions  The Conference Board The Conference Board leading, coincident, and lagging indicators composite index: one indicator alone may not be very reliable, but a mix of leading indicators may be effective

28. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 28 Forecasting techniques  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

29. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 29 Forecasting techniques  Leading indicators 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

30. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 30 Forecasting techniques  Coincident indicators identify trends in current economic activity employees on nonagricultural payrolls personal income less transfer payments industrial production manufacturing and trade sales

31. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 31 Forecasting techniques  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 (%)

32. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 32 Forecasting techniques  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

33. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 33 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

34. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 34 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

35. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 35 Forecasting techniques  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 %

36. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 36 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

37. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 37 Forecasting techniques  Visual time series projections: plotting observations on a graph and viewing the shape of the data and any trends

38. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 38 Forecasting techniques  Time series analysis: a naïve method of forecasting from past data by using least squares statistical methods to identify trends, cycles, seasonality and irregular movements

39. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 39 Forecasting techniques  Time series analysis: Advantages: easy to calculate does not require much judgment or analytical skill describes the best possible fit for past data usually reasonably reliable in the short run

40. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 40 Forecasting techniques  Time series data can be represented as: Y t = f(T t, C t, S t, R t ) Y t = actual value of the data at time t T t = trend component at t C t = cyclical component at t S t = seasonal component at t R t = random component at t

41. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 41 Forecasting techniques  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

42. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 42 Forecasting techniques  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 R 2

43. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 43 Forecasting techniques  Time series components: cycle, noise isolate cycle by smoothing with a moving average random factors cannot be predicted and should be ignored

44. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 44 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

45. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 45 Forecasting techniques  Moving average: average of actual past results used to forecast one period ahead E t+1 = (X t + X t-1 + … + X t-N+1 )/N E t+1 = forecast for next period X t, X t-1 = actual values at their respective times N = number of observations included in average

46. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 46 Forecasting techniques  Exponential smoothing: allows for decreasing importance of information in the more distant past, through geometric progression E t+1 = w · X t + (1-w) · E t w = weight assigned to an actual observation at period t

47. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 47 Forecasting techniques  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

48. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 48 Forecasting techniques  Example: econometric model Suits (1958) forecast demand for new automobiles ∆R = a 0 + a 1 ∆Y + a 2 ∆P/M + a 3 ∆S + a 4 ∆X R = retail sales Y = real disposable income P = real retail price of cars M = average credit terms S = existing stock X= dummy variable

49. Chapter FiveCopyright 2009 Pearson Education, Inc. Publishing as Prentice Hall. 49 Global application  Example: forecasting exchange rates GDP interest rates inflation rates balance of payments