Chapter 4 - Estimating and forecasting demand. This chapter is organized as follows. We begin by examining sources of information that provide data for forecasts. These include consumer interviews and surveys, controlled market studies, and uncontrolled market data. Next, we explore regression analysis, a statistical method widely used in demand estimation. Finally, we consider a number of important forecasting methods. | Chapter four Estimating and Forecasting Demand To count is a modern practice, the ancient method was to guess; and when numbers are guessed, they are always magnified. ~Samuel Johnson I know half the money I spend on advertising is wasted, but I can never find out which half. ~Lord Leverhulme of Unilever For Discussion Estimating Movie Demand Making movies is a risky business. Collecting data Consumer Surveys Survey Pitfalls New Coke Controlled Market Studies Uncontrolled Market Data Regression analysis Ordinary Least-Squares Regression Multiple Regression Interpreting Regression Statistics R-Squared Adjusted R-Squared The F-Statistic Figure four years of prices and quantities Regression analysis Interpreting Regression Statistics (cont.) Standard Errors of the Coefficients The t-Statistic Standard Error of the Regression Regression analysis Potential Problems in Regression Equation Specification Omitted Variables Multicollinearity Simultaneity and Identification Other Problems . | Chapter four Estimating and Forecasting Demand To count is a modern practice, the ancient method was to guess; and when numbers are guessed, they are always magnified. ~Samuel Johnson I know half the money I spend on advertising is wasted, but I can never find out which half. ~Lord Leverhulme of Unilever For Discussion Estimating Movie Demand Making movies is a risky business. Collecting data Consumer Surveys Survey Pitfalls New Coke Controlled Market Studies Uncontrolled Market Data Regression analysis Ordinary Least-Squares Regression Multiple Regression Interpreting Regression Statistics R-Squared Adjusted R-Squared The F-Statistic Figure four years of prices and quantities Regression analysis Interpreting Regression Statistics (cont.) Standard Errors of the Coefficients The t-Statistic Standard Error of the Regression Regression analysis Potential Problems in Regression Equation Specification Omitted Variables Multicollinearity Simultaneity and Identification Other Problems Figure Shifts in Supply and Demand Forecasting Time-Series Models Fitting a Simple Trend The Effect of Today on Tomorrow Forecasting Cable TV Subscribers The Demand for Toys Seasonal Variation The Housing Bubble Figure The Components of a Time Series Figure Fitting a Trend to a Time Series Figure Seasonal toy sales over Ten Years Forecasting Barometric Models Forecasting Performance Forecasting the Fate of Euro Disney Forecasting Accuracy Forecasting Performance Estimating Movie Demand Revisited Appendix to chapter 4 Regression Using Spreadsheets Simple Regression Step 1: Enter the data. Step 2: Call up the regression program. Step 3: Designate the columns of data to be used in the regression. Step 4: Inform the program where you want the output. Step 5: Run the regression. Multiple Regression Table Regression Data Figure Regression Data Table Simple regression results Table Multiple regression .
