In-Sample and Out-of-sample forecasting accuracy I am currently doing my college final project. I forecasted national soybeans yield and used MAPE to calculate the in-sample and out-of-sample forecasting accuracy. The MAPE results showed that the in-sample forecasting accuracy is higher than the out-of-sample accuracy. Does in-sample forecasting results always supposed to have a higher accuracy than the out-of-sample forecasting accuracy? and why is that? I can not seem to find the explanation in my text book, so if you happen to know about in-sample and out-of-sample forecasting accuracy, please help me >< thank you so much!^^

In-sample accuracy is almost always higher than out-of-sample accuracy, simply because in-sample is the data you are fitting your model to. So this is no reason for concern. (To the contrary, if your out-of-sample accuracy were higher than in-sample, I would wonder whether something is broken.)
This is part of the reason why in-sample accuracy is very rarely reported on. It's not a good guide to out-of-sample accuracy, and optimizing in-sample accuracy can lead to overfitting.
You may find Forecasting: Principles and Practice (2nd ed.) by Athanasopoulos & Hyndman
and
Forecasting: Principles and Practice (3rd ed.) by Athanasopoulos & Hyndman
interesting reading.
