Demand Forecasting Prof. Ravikesh Srivastava Lecture-11

Forecasting Method Following factors, we consider to select Are forecast of specific details– Micro Forecast Is the future status of some overall—Macro Forecast Is forecast needed for some point in near future- a short term forecast For a point in the distant future– a long-term forecast

Choosing Forecasting Method Forecast should be Accurate Timely Understood by management

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

Forecasting Process Why is a forecast needed? Who will use the forecast, and what are their specific requirement? What data are available and will the data be sufficient to generate the needed forecast What will the forecast cost How accurate can we expect the forecast to be

Forecasting Methods Survey Methods Statistical Methods ExtrapolationLeading IndicatorEconometric Graphical Trend Smoothing Auto Regressive Regression Simultaneous Equation ARIMA SimpleExponential

Qualitative Forecasts Survey Techniques Macro economic indicators Expected Sales and Inventory Changes Consumers’ Expenditure Plans Opinion Polls Business Executives Sales Force Consumer Intentions

Time-Series Analysis Secular Trend Long-Run Increase or Decrease in Data Cyclical Fluctuations Long-Run Cycles of Expansion and Contraction Seasonal Variation Regularly Occurring Fluctuations Irregular or Random Influences

Trend Projection Linear Trend: S t = S 0 + b t b = Growth per time period Constant Growth Rate S t = S 0 (1 + g) t g = Growth rate Estimation of Growth Rate lnS t = lnS 0 + t ln(1 + g)

Seasonal Variation Ratio to Trend Method Actual Trend Forecast Ratio = Seasonal Adjustment = Average of Ratios for Each Seasonal Period Adjusted Forecast = Trend Forecast Seasonal Adjustment

Seasonal Variation Ratio to Trend Method: Example Calculation for Quarter 1 Trend Forecast for = (0.394)(17) = Seasonally Adjusted Forecast for = (18.60)(0.8869) = 16.50

Data Pooled data: mixture of cross-sectional and time series data Panel data: follow a microeconomic unit over time Quantitative data: continuous data Qualitative data: categorical data

Time Series Data Time-series data are data arranged chronologically, usually at regular intervals A 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 Data Cross-sectional data are data on one or more variables collected at a single point in time Such 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 Data Pooled/Panel Data has the dimensions of both time series and cross-sections e.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 Data The data used in empirical analysis may be collected by a governmental agency e.g. the Department of Statistics, Department of Commerce, Department of Economic affairs, Department of Finance An international agency like IMF, World Bank, ADB, DFID Private organisation like CMIE, ORG and others.

Accuracy of Data In questionnaire type surveys, the problem of non-response or partial response is serious problem Problem in selectivity bias of sample Sampling methods used in obtaining the data may vary so widely. Economic data are generally available at a highly aggregate level