Bài giảng Managerial Economics - Chapter 7 Demand Estimation & Forecasting

Chapter 7Demand Estimation & ForecastingDirect Methods of Demand EstimationConsumer interviewsRange from stopping shoppers to speak with them to administering detailed questionnairesPotential problemsSelection of a representative sample, which is a sample (usually random) having characteristics that accurately reflect the population as a wholeResponse bias, which is the difference between responses given by an individual to a hypothetical question and the action the individual takes when the situation actually occursInability of the respondent to answer accurately2Direct Methods of Demand EstimationMarket studies & experimentsMarket studies attempt to hold everything constant during the study except the price of the goodLab experiments use volunteers to simulate actual buying conditionsField experiments observe actual behavior of consumers3Empirical Demand FunctionsDemand equations derived from actual market dataUseful in making pricing & production decisionsIn linear form, an empirical demand function can be specified as4Empirical Demand FunctionsIn linear formb = Q/Pc = Q/Md = Q/PRExpected signs of coefficientsb is expected to be negativec is positive for normal goods; negative for inferior goodsd is positive for substitutes; negative for complements5Empirical Demand FunctionsEstimated elasticities of demand are computed as6Nonlinear Empirical Demand SpecificationWhen demand is specified in log-linear form, the demand function can be written as7Demand for a Price-SetterTo estimate demand function for a price-setting firm:Step 1: Specify price-setting firm’s demand functionStep 2: Collect data for the variables in the firm’s demand functionStep 3: Estimate firm’s demand using ordinary least-squares regression (OLS)8Time-Series ForecastsA time-series model shows how a time-ordered sequence of observations on a variable is generatedSimplest form is linear trend forecastingSales in each time period (Qt ) are assumed to be linearly related to time (t)9Linear Trend ForecastingIf b > 0, sales are increasing over timeIf b < 0, sales are decreasing over timeIf b = 0, sales are constant over time10Estimated trend lineA Linear Trend Forecast(Figure 7.1)SalesTimeQt19971998199920002001200220032004200520062007720121211Forecasting Sales for Terminator Pest Control (Figure 7.2)12Seasonal (or Cyclical) VariationCan bias the estimation of parameters in linear trend forecastingTo account for such variation, dummy variables are added to the trend equationShift trend line up or down depending on the particular seasonal patternSignificance of seasonal behavior determined by using t-test or p-value for the estimated coefficient on the dummy variable13Sales with Seasonal Variation(Figure 7.3)200420052006200714Dummy VariablesTo account for N seasonal time periodsN – 1 dummy variables are addedEach dummy variable accounts for one seasonal time periodTakes value of 1 for observations that occur during the season assigned to that dummy variableTakes value of 0 otherwise15Effect of Seasonal Variation(Figure 7.4)SalesTimeQttQt = a’ + bta’aQt = a + btc16Some Final WarningsThe further into the future a forecast is made, the wider is the confidence interval or region of uncertaintyModel misspecification, either by excluding an important variable or by using an inappropriate functional form, reduces reliability of the forecastForecasts are incapable of predicting sharp changes that occur because of structural changes in the market17