Glossary:In-sample vs. out-of-sample forecasts

Statistical tests of a model's forecast performance are commonly conducted by splitting a given data set into an in-sample period, used for the initial parameter estimation and model selection, and an out-of-sample period, used to evaluate forecasting performance.

Empirical evidence based on out-of-sample forecast performance is generally considered more trustworthy than evidence based on in-sample performance, which can be more sensitive to outliers and data mining. Out-of-sample forecasts also better reflect the information available to the forecaster in "real time".

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