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1 vote

Selection of best VARX model using VAR() in R

If you are interested in FED interest rate, I would look at how well the different models are able to predict it out of sample, namely, what is the expected loss from the (one-step-ahead) forecast ...
Richard Hardy's user avatar
0 votes

What modeling approach should i use AR, MA, ARMA?

(Like you, I worked with the set from 2010 through 2020--11 years.) If you difference the series just once, non-seasonally, you stabilize the mean price across time, with rsq = .0003. I don't see a ...
rolando2's user avatar
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0 votes

AIC versus cross validation in time series: the small sample case

Hyndman & Athanasopoulos "Forecasting: Principles and Practice" (3rd edition) suggests AIC for short time series. Section 13.7 states: However, with short series, there is not enough ...
Richard Hardy's user avatar
0 votes

Is mutual exclusivity important for an A/B test for an audience selection method?

In my opinion, I would avoid this approach. If you start to move around people, e.g. discarding users that you don't like and re-sampling again, I would not consider this as random anymore. At the end,...
LevG's user avatar
  • 183
2 votes

Model selection and estimation for pseudo out-of-sample forecasting

The whole idea of pseudo out-of-sample forecasting is that each forecast must be constructed without using any information that would not have been available at the time. This means that you would ...
Ben's user avatar
  • 123k
2 votes

Interpreting AIC relative likelihoods ( qpcR::akaike.weights() )

Generally: Relative likelihoods are relative. There is no reason that I can see for them to add to anything and setting one of them to 1 is arbitrary, but useful, as it lets us get a sense of the size ...
Peter Flom's user avatar
  • 117k

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