Forecasting

Quick Summary of Forecasting

Forecasting is a method of projecting the financial performance of a company in the future. These forecasts are based on a number of assumptions. Milton Friedman a noted economist argued that economic models must never be judged on the validity of their assumptions but rather on their prediction’s accuracy. Forecasting has become a regular economic activity of governments after the 1960s. Long-run economic forecasting requires the involvement of the concerned central bank of a country.

All economic forecasting is done keeping product market, money market and labour market into consideration. Proper forecasting is vital in determining the proper behaviour of the money market. Since the impact of monetary policy is far fetched, all policy changes of a central bank are based on assumptions of inflated rates and not on the current price level. If projected inflation is higher than target, interest rates are raised. Conversely, when the interest rate is below target, rates are reduced.

Forecasting is the process of estimation in unknown situations. Prediction is a similar, but a more general term, and usually refers to estimation of time series, cross-sectional or longitudinal data. Risk and uncertainty are central to forecasting and prediction. In more recent years, Forecasting has evolved into the practice of Demand Planning in every day business forecasting for manufacturing companies. The discipline of demand planning, also sometimes referred to as supply chain forecasting embraces both statistical forecasting and consensus process.

Forecasting is commonly used in the discussion of time-series data.

Categories Of Forecasting Methods

Time-series methods

Time-series methods use historical data as the basis for estimating future outcomes.

• Moving average
• Exponential smoothing
• Extrapolation
• Linear prediction
• Trend estimation
• Growth curve
• Topics

Causal/Econometric Methods

Some forecasting methods use the assumption that it is possible to identify the underlying factors that might influence the variable that is being forecast. For example, sales of umbrellas might be associated with weather conditions. If the causes are understood, projections of the influencing variables can be made and used in the forecast.

• Regression analysis using linear regression or non-linear regression
• Autoregressive moving average (ARMA)
• Autoregressive integrated moving average (ARIMA)

e.g. Box-Jenkins

• Econometrics

Judgemental methods

Judgemental forecasting methods incorporate intuitive judgements, opinions and probability estimates.

• Composite forecasts
• Surveys
• Delphi method
• Scenario building
• Technology forecasting
• Forecast by analogy

Other Methods

• Simulation
• Prediction market
• Probabilistic forecasting and Ensemble forecasting
• Reference class forecasting

Forecasting Accuracy

The forecast error is the difference between the actual value and the forecast value for the corresponding period.

E_t = Y_t – F_t

where E is the forecast error at period t, Y is the actual value at period t, and F is the forecast for period t.

Measures of aggregate error:

Please note that the business forecasters and demand planners in the industry refer to the PMAD as the MAPE, although they compute this volume weighted MAPE. Difference between MAPE and WMAPE is explained in Calculating Demand Forecast Accuracy

Forecasting Process

There are two forecasting processes: top-down forecasting and bottom-up forecasting. In the top-down process, forecasting begins with a total assessment of an economy, and thereafter predictions of individual components like GDP (gross domestic product), per capita income, and per capita consumption are made. In the bottom-up process, forecasting begins at the lowest economic activity level, like production of number of steel sheets, and subsequently, all production processes are summed up. Presently, a combination of both processes is followed for proper forecasting.

Forecasting ideally begins from ‘exogenous’ sectors for which predictions are easier, because of their relative independent functional nature. International trade is one such economic sector.

Forecasting Institutes

Forecasts are made by many concerns and for different reasons. Central banks and governments need forecasting for predicting business cycle movements. Financial institutes need forecasts to plan their asset allocation, while manufacturers need appropriate forecasts for planning their investment and production.

Central banks and national forecasting agencies employing well-educated personnel and employing varied forecasting models are examples of reputed forecasting agencies.

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Definition Sources