PA 650 (WANG Chp 1) Revenue Forecasting
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Expert based quantitative and qualitative forecasting. Most popular method.
What is being forecast
length of forecast
Multiple Techniques, pick the best on for what you are forecasting
Simple Moving Average (SMA)
Calculate the average of revenues in the previous forecast periods to predict the next. It weighs all previous periods equally.
Exponential Smoothing (EXS)
Assigns different weights to the more recent data. Sum of the weights used needs to equal 100%.
Smoothing Constant or alpha
The weight applied to the data in the forecast.
Forecast is smaller than the actual number
The forecast is larger than the actual number
Transformation Moving Average (TMA)
Takes into account trends over time. In general it is more accurate than SMA and EXS over time
Trend occurs when the revenue shows a distinctive direction over time.
The revenues have been getting larger overtime. Positive trend.
The revenues have been getting smaller overtime. Negative trend.
The difference in the numbers over time. Year 2- Year 1 = X; Year 3 - Year 2=Y; Average of X, Y, Z added to the last year of data.
Regression Against Time
A relationship is established between revenue and the forecast period. Creates a y=mx+b line where y is revenue and x is time. No evidence shows that it is more accurate than TMA for trend data.
Forecasting techniques that use historical data. The historical data is called time-series data.
Quasi Casual Forecasting Model
Use when you have incomplete or skewed data. Use Revenue Predictors. Ex: when you know the size of your tax base and the tax rate you can estimate your tax revenue without having to base your forecast on historical data.
Absolute Percentage Error (APE)
Determines how accurate the forecast is. Absolute value (forecast value - actual value) divided by the actual value.
Mean Absolute Percentage Error (MAPE)
Determines how accurate the forecast is. Mean of Multiple APEs
An obvious deviation from the mainstream revenue trend. Outliers lead to inaccurate forecast.
Steps in Revenue Forcasting
- 1. Cleaning the Data
- 2. Choosing the Forecast Technique (Forecast Accuracy and Cost)
- 3. Forecasting
- 4. Monitoring Forecast Performance
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