In-sample, out-of-sample analysis

The in-sample, out-of-sample process consists of using a portion of the available data (the in-sample data) to develop and train a model or conduct analysis. Subsequently, the developed model is tested using a separate portion of the data (known as out-of-sample data) that was not utilized during the training phase. This helps assess the robustness and generalizability of the model or analysis beyond the data it was trained on. The goal is to ensure that the insights or predictions derived from the in-sample data apply to new, unseen data.

In this section, we are going to do the following:

• Modify EasyLanguage scripts for the in-sample, out-of-sample analysis
• Run an example of in-sample, out-of-sample validation

Modifying EasyLanguage scripts for in-sample, out-of-sample purposes

To run an in-sample, out-of-sample validation, you need to modify the EasyLanguage code, as follows:

Input:StartDate_(0),EndDate_(1191231);
Input...