An F-statistic of 76,995.61 is not unusual in two-step system GMM, often arising from large samples or numerous instruments. Instead of focusing on the F-statistic, attention should be given to the instrument-to-sample size ratio and the Hansen/Sargan and autocorrelation tests to verify model specification and inference validity. To mitigate instrument proliferation, consider collapsing instruments or using only essential lags.

A very high F-statistic in a two-step system GMM estimation such as 76,995.61 can be a red flag rather than a confirmation of strong model performance. This inflated statistic often results from instrument proliferation, a common issue in GMM where too many instruments relative to the number of cross-sectional units cause overfitting and distort the finite-sample properties of the estimators. As a result, the F-statistic and other standard diagnostics can be misleading. It is important to check the number of instruments in relation to the number of groups; if the instrument count is excessively high, it undermines the power of the Hansen test and may bias the results. To address this, consider collapsing the instruments (as suggested by Roodman, 2009), limiting the lag structure, or using a subset of instruments based on theoretical relevance. Also, ensure the Hansen or Sargan test for overidentification and Arellano-Bond tests for serial correlation are correctly reported and interpreted. While the F-statistic indicates joint significance, its magnitude should be interpreted cautiously in GMM settings what matters more is whether your model is statistically valid and economically meaningful.

Check if any of your data need to be log,