# What is the problem with using R-squared in time series models?

I have read that using R-squared for time series is not appropriate because in a time series context (I know that there are other contexts) R-squared is no longer unique. Why is this? I tried to look this up, but I did not find anything. Typically I do not place much value in R-squared (or Adjusted R-Squared) when I evaluate my models, but a lot of my colleagues (i.e. Business Majors) are absolutely in love with R-Squared and I want to be able to explain to them why R-Squared in not appropriate in the context of time series.

• Google search: "spurious regression in econometrics". Or check out Granger and Newbold's paper. Others may supply more details in answers. Jun 8, 2014 at 1:18
• @Richard Hardy could you please elaborate on "If we take sample R2 as a measure of its population counterpart, it breaks down under integrated time series.". Jun 13, 2019 at 20:47
• Depending on the order of integration, population or true $R^2$ might not even be defined. Also, autocorrelation leads to spurious $R^2$ Sep 22, 2021 at 11:45

Some aspects of the issue:

If somebody gives us a vector of numbers $\mathbf y$ and a conformable matrix of numbers $\mathbf X$, we do not need to know what is the relation between them to execute some estimation algebra, treating $y$ as the dependent variable. The algebra will result, irrespective of whether these numbers represent cross-sectional or time series or panel data, or of whether the matrix $\mathbf X$ contains lagged values of $y$ etc.

The fundamental definition of the coefficient of determination $R^2$ is

$$R^2 = 1 - \frac {SS_{res}}{SS_{tot}}$$

where $SS_{res}$ is the sum of squared residuals from some estimation procedure, and $SS_{tot}$ is the sum of squared deviations of the dependent variable from its sample mean.

Combining, the $R^2$ will always be uniquely calculated, for a specific data sample, a specific formulation of the relation between the variables, and a specific estimation procedure, subject only to the condition that the estimation procedure is such that it provides point estimates of the unknown quantities involved (and hence point estimates of the dependent variable, and hence point estimates of the residuals). If any of these three aspects change, the arithmetic value of $R^2$ will in general change -but this holds for any type of data, not just time-series.

So the issue with $R^2$ and time-series, is not whether it is "unique" or not (since most estimation procedures for time-series data provide point estimates). The issue is whether the "usual" time series specification framework is technically friendly for the $R^2$, and whether $R^2$ provides some useful information.

The interpretation of $R^2$ as "proportion of dependent variable variance explained" depends critically on the residuals adding up to zero. In the context of linear regression (on whatever kind of data), and of Ordinary Least Squares estimation, this is guaranteed only if the specification includes a constant term in the regressor matrix (a "drift" in time-series terminology). In autoregressive time-series models, a drift is in many cases not included.

More generally, when we are faced with time-series data, "automatically" we start thinking about how the time-series will evolve into the future. So we tend to evaluate a time-series model based more on how well it predicts future values, than how well it fits past values. But the $R^2$ mainly reflects the latter, not the former. The well-known fact that $R^2$ is non-decreasing in the number of regressors means that we can obtain a perfect fit by keeping adding regressors (any regressors, i.e. any series' of numbers, perhaps totally unrelated conceptually to the dependent variable). Experience shows that a perfect fit obtained thus, will also give abysmal predictions outside the sample.

Intuitively, this perhaps counter-intuitive trade-off happens because by capturing the whole variability of the dependent variable into an estimated equation, we turn unsystematic variability into systematic one, as regards prediction (here, "unsystematic" should be understood relative to our knowledge -from a purely deterministic philosophical point of view, there is no such thing as "unsystematic variability". But to the degree that our limited knowledge forces us to treat some variability as "unsystematic", then the attempt to nevertheless turn it into a systematic component, brings prediction disaster).

In fact this is perhaps the most convincing way to show somebody why $R^2$ should not be the main diagnostic/evaluation tool when dealing with time series: increase the number of regressors up to a point where $R^2\approx 1$. Then take the estimated equation and try to predict the future values of the dependent variable.

• Good explanation but then why this this is added as a standard output of software in statistical package
– user86409
Aug 20, 2015 at 11:49
• @brijesh Regression-tradition, I would say. Aug 20, 2015 at 13:12
• Great answer! However, it contains little information that is particular to time series. Prediction vs. in-sample fit applies to other data types probably as much as for time series. On the other hand, one key aspect that is particular to time series is missing. I mean regressing integrated variables. If we take sample $R^2$ as a measure of its population counterpart, it breaks down under integrated time series. (I could write this up as an answer but do not have the time right now.) Apr 24, 2018 at 9:17

Some extra comments to the post above. When dealing with time-series an R squared (or adjusted R^2) would always be greater if explanatory variables were not differenced. However, when it goes to out-of-time fit, the error term would be significantly higher for non-differenced time series. This happens because of trends presented in the data and generally well-known issue. But it is a good way showing why this measure should probably, be last on the list when choosing most appropriate time-series model.