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

Chapter Demand Estimation and Forecasting Chapter Outline • • • • • Regression analysis Limitation of regression analysis The importance of business forecasting Prerequisites of a good forecast Forecasting techniques Copyright ©2014 Pearson Education, Inc All rights reserved 5-2 Learning Objectives • Understand the importance of forecasting in business • Know how to specify and interpret a regression model • Describe the major forecasting techniques used in business and their limitations • Explain basic smoothing methods of forecasting, such as the moving average and exponential smoothing Copyright ©2014 Pearson Education, Inc All rights reserved 5-3 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 Copyright ©2014 Pearson Education, Inc All rights reserved 5-4 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 Copyright ©2014 Pearson Education, Inc All rights reserved 5-5 Regression Analysis • 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 Copyright ©2014 Pearson Education, Inc All rights reserved of independent variables) 5-6 Regression Analysis • 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 Copyright ©2014 Pearson Education, Inc All rights reserved 5-7 Regression Analysis • Statistical evaluation of regression results: – t-test: test of statistical significance of each estimated coefficient (whether the coefficient is significantly different from zero) bˆ t= b= estimated coefficient SE Seb = standard error ofbˆ estimated coefficient Copyright ©2014 Pearson Education, Inc All rights reserved 5-8 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 (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 Copyright ©2014 Pearson Education, Inc All rights reserved 5-9 Regression Analysis • Statistical evaluation of regression results – R (coefficient of determination): percentage of variation in the variable (Y) accounted for by variation in all explanatory variables (Xn) R2 value ranges from 0.0 to 1.0 The closer to 1.0, the greater the explanatory power of the regression Copyright ©2014 Pearson Education, Inc All rights reserved 5-10 Forecasting Techniques • General compound growth rate formula: E/B = (1+i)n E n B i = = = = final value years in the series beginning value constant growth rate Copyright ©2014 Pearson Education, Inc All rights reserved 5-38 Forecasting Techniques • Visual time series projections: plotting observations on a graph and viewing the shape of the data and any trends Copyright ©2014 Pearson Education, Inc All rights reserved 5-39 Forecasting Techniques • An Example in Which the Constant Compound Growth Rate Approach Would Be Misleading Copyright ©2014 Pearson Education, Inc All rights reserved 5-40 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 Copyright ©2014 Pearson Education, Inc All rights reserved 5-41 Forecasting Techniques • Time series analysis: – – – – 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 Copyright ©2014 Pearson Education, Inc All rights reserved 5-42 Forecasting Techniques • Time series data can be represented as: 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 Copyright ©2014 Pearson Education, Inc All rights reserved 5-43 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 Copyright ©2014 Pearson Education, Inc All rights reserved 5-44 Forecasting Techniques • Time series components: trend – – to remove trend line, use least squares method possible best-fit line styles: straight line: exponential line: quadratic line: – choose one with best R Y = a + b(t) Y = abt Y = a + b(t) + c(t)2 Copyright ©2014 Pearson Education, Inc All rights reserved 5-45 Forecasting Techniques • Time series components: cyclical, random – isolate cyclical components by smoothing with a moving average – random factors cannot be predicted and should be ignored Copyright ©2014 Pearson Education, Inc All rights reserved 5-46 Forecasting Techniques • Smoothing techniques – Moving average • The larger the number of observations in the average, the greater the smoothing effect – Exponential smoothing • Allows for the decreasing importance of information in the more distant past Copyright ©2014 Pearson Education, Inc All rights reserved 5-47 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 N = actual values at their respective times = number of observations included in average Copyright ©2014 Pearson Education, Inc All rights reserved 5-48 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 Copyright ©2014 Pearson Education, Inc All rights reserved 5-49 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 Copyright ©2014 Pearson Education, Inc All rights reserved 5-50 Forecasting Techniques • Illustration of a model of the relationship between the domestic currency and a foreign currency: Et = a + bIt + cRt + dGt – – – – – – where E = Exchange rate of a foreign currency in terms of the domestic currency I = Domestic inflation rate minus foreign inflation rate R = Domestic nominal interest rate minus foreign nominal interest rate G = Domestic growth rate of GDP minus the growth rate of foreign GDP t = Time period a, b, c, d Regression coefficients Copyright ©2014 Pearson Education, Inc All rights reserved 5-51 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 Copyright ©2014 Pearson Education, Inc All rights reserved 5-52 ... from estimating demand for pizza – demand for pizza affected by price of pizza price of complement (soda) – – managers can expect price decreases to lead to lower revenue tuition and location are... Objectives • Understand the importance of forecasting in business • Know how to specify and interpret a regression model • Describe the major forecasting techniques used in business and their limitations... reserved 5-19 Forecasting Techniques • Factors in choosing the right forecasting technique: – item to be forecast – interaction of the situation with the forecasting methodology the value and costs