Estimated mean square error of prediction.

## Details

Computes the estimated mean square error of prediction for each model selected assuming that the values of the regressors are fixed and that the model is correct.

$$MSE((n + p) / n)$$

where \(MSE = SSE / (n - p)\), n is the sample size and p is the number of predictors including the intercept

## References

Akaike, H. (1969). “Fitting Autoregressive Models for Prediction.” Annals of the Institute of Statistical Mathematics 21:243–247.

Judge, G. G., Griffiths, W. E., Hill, R. C., and Lee, T.-C. (1980). The Theory and Practice of Econometrics. New York: John Wiley & Sons.

## Examples

```
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
ols_fpe(model)
#> [1] 7.949661
```