Using Datastream or IBES for forecast accuracy

Not long ago a student contacted me with regard to the subject of forecasts. Forecast accuracy is defined as the absolute difference between the consensus analysts’ forecasts and actual earnings per share divided by the firm share price at the beginning of the quarter. Differences between actual and forecast earnings can be considered “surprise data”.
His question concerned the variables that he got through Datastream & IBES and which variables should be used. The answer I came up with (with the help of the Thomson helpdesk) was the following:

Datastream has direct data types for the Surprise earnings but this is only available for companies that have quarterly data coverage. Important in this case is, that only the current values of these data types are held – no history is maintained. It is also very difficult to calculate the Surprise data manually because of Datstream padding function.
Regardless of whether you decide to use IBES or Datastream data types, you should not use data types from both sources in the same analysis! Only use IBES data types or only use Datastream data types.

To answer the questions on using actual EPS and EPS forecasts and prices the analysis could be done using the following IBES data types:
EPSI1YR = Earnings Per Share End Date of Quarterly Period INT1
EPSI1MN = Earnings Per Share Mean INT1
I0IND = EPS Last Rep Int Period Indicator
I0EPS = EPS Reported Interim EPS (INT1)
IBP = PRICE (IBES)

Using the output from IBES (through Datastream) you have to be careful when comparing EPS data as all data should be for the exact same quarter. See below for an example with colours to indicate the quarter data to be compared:

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