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Taken from J. Scott Armstrong, 1985, Long-Range Forecasting, 2nd ed., p. 487.

Some people are impressed by a high R2. There are simple things that can be done to raise R2; other than that, they are of no value. The most important thing is not to use these rules (please don't), but to be aware that others use them.

  1. Discard outliers after you examine the regression results.
  2. Aggregate the data, especially when it reduces sample size significantly.
  3. Experiment by trying lots of variables.
  4. Try different functional forms.
  5. Use stepwise regression and retain all coefficients with t statistics greater than 1.0 (Haitovsky, 1969).
  6. Include a lot of variables in the final equation.
  7. Use R2 rather than .

These rules should yield R2 values of over 99% for time series data and about 90% for cross-sectional data. My advice is that you should not use R2 for time series data. The dangers outweigh any potential benefits.