Demand Forecasting

Prof. Ravikesh Srivastava

A Case: Trouble at Amana*In 1967, Amana introduced the first microwave, and gained 60% of the market. A decade later, its market share declined to 6%. WHY?Mis-forecast: microwave will SUBSTITUTE for traditional ways of cookingMisunderstood the market: assumed that the market DOESNT care about the priceUnprepared for new entrants: Low priced Japanese competitors*The Amana Corporation is an American brand of household appliances. It was founded in 1934 by George Foerstner as The Electrical Equipment Co. in Middle Amana, Iowa, to manufacture commercial walk-in coolers. Amana home appliances is now owned by the Whirlpool Corporation.

Choosing Forecasting MethodForecast should beAccurateTimelyUnderstood by management

DataPooled data: mixture of cross-sectional and time series dataPanel data: follow a microeconomic unit over timeQuantitative data: continuous dataQualitative data: categorical data

Time Series DataTime-series data are data arranged chronologically, usually at regular intervalsA time series is a set of observations on the values that a variable takes at different times. Such data may be collected at regular time intervals, such as daily (stock prices), weekly (inflation, price index figures), monthly (CPI, Revenue collection), quarterly (GNP, GDP)Annually (government budgets, exports)Quinquennially ie. Every five years (census of manufacturer)Decennially (census of population)

Cross Sectional DataCross-sectional data are data on one or more variables collected at a single point in timeSuch as census of population conducted by govt of India every 10 years.The opinion polls conducted by several print & electronic media.Cross-sectional data have problems of Heterogeneity. Like some states in our country is having good growth whereas some is having too low. When we include heterogeneous units in a statistical analysis, the size or scale effect must be taken into account

Pooled/ Panel DataPooled/Panel Data has the dimensions of both time series and cross-sectionse.g. the daily prices of a number of blue chip stocks over two years.There is a special type of pooled data, the pooled or longitudinal data also called micro panel data, in which the same cross-sectional unit (say a family or a firm) is surveyed over time.

Sources of DataThe data used in empirical analysis may be collected by a governmental agencye.g. the Department of Statistics, Department of Commerce, Department of Economic affairs, Department of FinanceAn international agency like IMF, World Bank, ADB, DFIDPrivate organisation like CMIE, Capital line, ORG and others.

Forecasting StepsData collection- proper and accurate data, whether relevant with problem Data processing- data entry and detection of outlierData reduction or condensation- make meaningful and focused with our specific needsModel building and evaluation- fitting into appropriate model and minimizing errorModel extrapolation -the actual forecast Forecast Evaluation-comparing forecast value with actual historical values.

Types of Forecasting ModelsQualitative (technological) methods:Forecasts generated subjectively by the forecaster

Quantitative (statistical) methods:Forecasts generated through mathematical modeling

Forecasting During the Life CycleIntroductionGrowthMaturityDeclineSalesTime Quantitative models- Time series analysis- Regression analysisQualitative models- Executive judgment- Market researchSurvey of sales forceDelphi method

Qualitative Methods

Sheet1

TypeCharacteristicsStrengthsWeaknesses

Executive opinionA group of managers meet & come up with a forecastGood for strategic or new-product forecastingOne person's opinion can dominate the forecast

Market researchUses surveys & interviews to identify customer preferencesGood determinant of customer preferencesIt can be difficult to develop a good questionnaire

Delphi methodSeeks to develop a consensus among a group of expertsExcellent for forecasting long-term product demand, technological changes, and scientific advancesTime consuming to develop

Sheet2

Sheet3

Common Problems with Survey MethodProblems:1. Selection of a representative samplewhat is a good sample!2. Response biashow truthful can they be?3. Inability or unwillingness of the respondent to answer accurately

Statistical ForecastingTime Series Models:Assumes the future will follow same patterns as the past

Causal Models:Explores cause-and-effect relationshipsUses leading indicators to predict the futureE.g. Sales of durables depend on number of Indicators

Time Series Forecasts

Forecasting methodAmount of Historical DataData PatternForecast horizonSimple Moving Average6-12 months, weekly data often usedData should be stationaryShort to mediumWeighted Moving Average & Simple Exponential smoothing5-10 observation needed to startData should be stationaryshortExponential smoothing with Trend5-10 observation needed to startStationary and trendshortLinear Regression15 -20 observationsStationary, trend & Seasonality Short to medium

Methods of ForecastingNave ForecastingSimple MeanMoving AverageWeighted Moving AverageExponential Smoothing

Nave ForecastingNext period forecast = Last Periods actual:

Simple Average (Mean)Next periods forecast = average of all historical data

Simple Moving AverageForecast is the average of data from n periods prior to the forecast data point.Ft = At-1 + At-2 + At-3 + + At-n ORWhere:Ft = Forecast for the coming periodn= Number of periods to be averagedAt-1 = Actual occurrence in the past period

Weighted Moving AverageA weighted moving average allows any weights to be placed on each element, providing the sum of all weights equals 1. Ft = w1At-1 +w2 At-2 +w3 At-3 + + wn At-nWhere:w1 = Weight to be given to the actual occurrence for the period t-1w2 = Weight to be given to the actual occurrence for the period t-2wn = Weight to be given to the actual occurrence for the period t-nn= Number of periods in the Forecast wi = 1

Exponential Smoothing

Time-Series AnalysisSecular TrendLong-Run Increase or Decrease in DataCyclical FluctuationsLong-Run Cycles of Expansion and ContractionSeasonal VariationRegularly Occurring FluctuationsIrregular or Random Influences

Trend ProjectionLinear Trend: St = S0 + b t b = Growth per time periodConstant Growth Rate St = S0 (1 + g)t g = Growth rateEstimation of Growth Rate lnSt = lnS0 + t ln(1 + g)

Seasonal VariationRatio to Trend MethodAdjusted Forecast=Trend ForecastSeasonal Adjustment

Seasonal VariationRatio to Trend Method: Example Calculation for Quarter 1Trend Forecast for 2014.1 = 11.90 + (0.394)(17) = 18.60Seasonally Adjusted Forecast for 2014.1 = (18.60)(0.887) = 16.50

YearForecastActualActual/Forecast2010.112.2911.000.8952011.113.8712.000.8642012.115.4514.000.9062013.117.0215.000.881Average0.887

Forecast AccuracyForecasts are rarely perfectNeed to know how much we should rely on our chosen forecasting methodMeasuring forecast error:

Note that over estimated forecasts = negative errors and underestimated forecasts = positive errors

Tracking Forecast Error

Mean Absolute Deviation (MAD):A good measure of the actual error in a forecast

Mean Square Error (MSE):Penalizes extreme errors

Root Mean Square Error

Econometric ModelsSingle Equation Model of the Demand For Cereal (Good X)QX = a0 + a1PX + a2Y + a3N + a4PS + a5PC + a6A + eQX = Quantity of XPX = Price of Good XY = Consumer IncomeN = Size of PopulationPS = Price of MuffinsPC = Price of Milk A = Advertising e = Random Error

*Pooled data - mix of both elements e.g. profit rates for firms over a ten year period. e.g. profit (it) = f(conc, capital/labor ratio)Panel data - follow a household over time.Data can be quantitative - prices, income, money supply; orqualitative - male/female, married/unmarried.

Lots of problems with data - may be variables you would like to get but can't, may be rounding errors, may be low response in surveys. Results of research are only as good as the data you use. But have to try. Always state sources of data used, definitions, limitations, assumptions used, so your results can be interpreted correctly.

Need to look at probability and statistics because in estimatingeconometric models, we are concerned with the statisticalrelationships among variables, not deterministic as in sayclassical phsyics - law of gravity an exact relationship -F=k((mass1*mass2)/distance squared). In looking at statistical relationships we deal with random variables, or stochastic variables - variables that have probability distributions. (In fact, the dependent variable is assumed to be random, to have a probability distribution, but the independent variables are assumed to be fixed, have fixed values in repeated sampling - even though they may be intrinsically stochastic; note random=stochastic=probabilistic).Note also that we look at the dependence of one variable onanother, but this does not necessarily mean one variable causes another - although it may. We have to look at theory to figure out whether or not we can reasonably assume causation. (Take Philips curve - do high money wages lead to lower unemployment, does lower unemployment lead to high money wages, or does some other factor cause both?)

**********