Recursive forecasting alternative to looping.
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I am working on some out-of-sample forecasts where i utilize a recursive forecast method.
Currently I conduct the forecast using a loop, where i save the forecast values in a seperate vector, and add one observation everytime the loop runs.
In the code I run regressions for 3 return measures, and 7 seperate variables, yeilding 21 regressions.
However this is computational very heavy, since I also conduct bootstrapping to obtain p-values for my test statistics.
My in-sample tests runs fairly quickly in a similar code, so I expect the bottleneck is the loops that conducts the recursive forecasts.
Do any one have suggestions on how I could do this differently?
Is there an alternative way, instead of a loop, to conduct recursive foresating
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2013-9-21
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