# Why not using the R squared to measure forecast accuracy?

Why in literature usually the common accuracy measures like MAD, MSE, RMSE, MAPE ... are used. Why not using the $R^2$ (coefficient of determination)?

I was thinking about the difference: By using the MSE i can compare the average of the forecast. And when using $R^2$ I'll get a information about the variance.

Why the comparison of averages is mostly commonly used? Can anybody give me an hint?

In-sample $R^2$ is not a suitable measure of forecast accuracy because it does not account for overfitting. It is always possible to build a complicated model that will fit the data perfectly in sample but there are no guarantees such a model would perform decently out of sample.

Out-of-sample $R^2$, i.e. the squared correlation between the forecasts and the actual values, is deficient in that it does not account for bias in forecasts.

For example, consider realized values

$$y_{t+1},\dotsc,y_{t+m}$$

and two competing forecasts:

$$\hat{y}_{t+1},\dotsc,\hat{y}_{t+m}$$

and

$$\tilde{y}_{t+1},\dotsc,\tilde{y}_{t+m}.$$

Now assume that

$$\tilde{y}_{t+i}=c+\hat{y}_{t+i}$$

for every $i$, where $c$ is a constant. That is, the forecasts are the same except that the second one is higher by $c$. These two forecasts will generally have different MSE, MAPE etc. but the $R^2$ will be the same.

Consider an extreme case: the first forecast is perfect, i.e. $\hat{y}_{t+i}=y_{t+i}$ for every $i$. The $R^2$ of this forecast will be 1 (which is very good). However, the $R^2$ of the other forecast will also be 1 even though the forecast is biased by $c$ for every $i$.