抛砖引玉一下

如上图，左边的是prediction，右边的是forcast。
看资料云里雾里，不太清楚，好像是prediction是ols 最小化标准误后仍然存在的标准误，而forcast则是因为样本替换所产生的误差，即模型是基于已有数据得出，当将该模型应用于新的样本时，得到的预测值与真实值之间的误差。

说的不一定对，请大神继续指教

贴一个国外论坛的回答

There is only one difference between these two in time series. Forecasting pertains to out of sample observations, whereas prediction pertains to in sample observations. Predicted values (and by that I mean OLS predicted values) are calculated for observations in the sample used to estimate the regression. However, forecast is made for the some dates beyond the data used to estimate the regression, so the data on the actual value of the forecasted variable are not in the sample used to estimate the regression.

Residuals: Difference between the actual value of Y and its predicted value for observations in the sample.

Forecast error: Difference between future value of Y, which is not contained in the estimation sample, and the forecast of the future value.

Note : This was extracted from Introduction to Econometrics by Stock and Watson (p. 527)