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I read this link and it is very helpful.

However, if log-returns are easier for time-aggregation, then why do economists work with discrete returns e.g. in GDP growth?

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I would say people usually use log-returns for continuous data (although no data is really continuous, not even tick data). And discrete returns when your data is discrete. In the case of GDP, you only get the data every 3 months, so that is as discrete as it gets.

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  • $\begingroup$ Thanks (+1), if I read the answer in the link I think it could also be because GDP is a portfolio of values added for different sectors and the answer in the link explains why discrete returns are used for portfolios. What do you think ? $\endgroup$ – user0009 Nov 8 '17 at 14:20
  • $\begingroup$ I guess this makes sense when your data are not all equally timed. I would like to see the math for that claim of 'fails for longer horizons', though. In short, I don't know. $\endgroup$ – python_enthusiast Nov 8 '17 at 15:14

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