This is known as in-sample forecast. It estimated the model using all available data and then comparing it to the model's fixed values to the actual realizations. But, this method is known to attract an overly positive picture of the model's forecasting ability since common fitting algorithms tend to take pains to avoid big prediction errors and are also inclined to overfitting (mistaking noise for signal in the data).
