GlossaryEx-Post Analysis

Ex-Post Analysis

Ex-Post Analysis refers to the evaluation of forecasting accuracy by comparing predicted values with actual outcomes after the fact.

It is used to assess the performance of forecasting models and does not capture real-time decision-making or predictive capabilities. Unlike ex-ante analysis, which focuses on predictions before outcomes are known, ex-post analysis provides a retrospective assessment.

How Ex-Post Analysis Works

The process involves several steps:

  1. Data Collection: Gather actual outcome data for the period under review.
  2. Comparison: Compare predicted values against actual outcomes to identify discrepancies.
  3. Evaluation: Analyze the differences to assess the accuracy and reliability of the forecasting model.

Strengths and Limitations

Ex-Post Analysis is informative for evaluating model accuracy and identifying areas for improvement. However, it is misleading if used to predict future outcomes. Complementary metrics like ex-ante analysis provide forward-looking insights.

Ex-Post Analysis in Commodity Forecasting

In commodity markets, ex-post analysis is applied to assess the accuracy of price forecasts for assets like oil and copper. By identifying past prediction errors, analysts can refine models to improve future forecasting accuracy.

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