Ex- post forecasting
Ex-post forecasting refers to a method of forecasting that evaluates the accuracy of past forecasts. In other words, it involves looking at how well a previous forecast predicted what actually happened. This method can be used to identify the strengths and weaknesses of a forecasting model or approach and can help to improve future forecasts. It can also be used to evaluate the performance of individuals or teams responsible for making the forecasts. By comparing past forecasts to actual outcomes, it is possible to determine whether the forecasts were accurate, and if not, what factors contributed to the errors. Additionally, ex-post forecasting can be used as a tool for post-mortem analysis of past decisions and actions in order to learn from mistakes and improve decision-making in the future.
Ex-post forecasting, also known as retrospective forecasting or backcasting, is a forecasting technique that involves examining past data to make predictions about past events or outcomes. It is a method used to evaluate the accuracy of previous forecasts or estimate what would have happened if different decisions or scenarios were considered in the past.
Here is a step-by-step guide on how to conduct ex-post forecasting:
1. Define the objective: Determine the purpose of the ex-post forecasting exercise. This could be evaluating the accuracy of previous forecasts, understanding the impact of different decisions, or estimating the performance under different scenarios.
2. Gather historical data: Collect relevant historical data that corresponds to the period you want to examine. This can include sales records, financial statements, market data, or any other data relevant to the specific area being forecasted.
3. Analyze the past forecast: Identify the previous forecast or decision that was made and evaluate its accuracy. Compare the forecasted values or expected outcomes with the actual observed values or outcomes. This can involve statistical analysis, such as calculating the mean absolute percentage error (MAPE) or other relevant error measures.
4. Identify factors that influenced the outcomes: Identify the key factors that influenced the actual outcomes or values. This could include factors such as market conditions, competitor actions, customer preferences, or any other relevant variables.
5. Consider alternative scenarios or decisions: Generate alternative scenarios or decisions that could have been made in the past. This involves changing one or more key variables or assumptions and estimating the corresponding outcomes. This can help understand the impact of different decisions or scenarios on the forecasted values.
6. Evaluate the alternative scenarios: Compare the results of the alternative scenarios with the actual outcomes. This can help assess the validity of the previous forecast and determine the relative impact of different decisions.
7. Learn from the insights: Reflect on the findings of the ex-post forecasting exercise and draw insights or lessons. This can help improve future forecasting efforts, enhance decision-making, or guide future strategies.
Remember that ex-post forecasting is a retrospective analysis and cannot predict future events or outcomes. It is primarily used for evaluation purposes and to learn from past experiences.