I have a question about multivariate hypothesis testing in out-of-sample evaluations.

Generally, let`s asssume we want to predict three different stock returns in a 1-month ahead forecasting setting. We have collected five different features for linear regression appraoches. Furthermore, we already splitted our data in a initial training set, which grows by each prediction iteration.

Lets assume the following aspects:

  1. we firstly model a multivariate multiple predictive regression
  2. we secondly model a VARX model for the forecasting purpose

Finally, we end up with three time series of prediction error for each model (Error stock A, Error stock B and Error stock C). Thus, we can calculate the means squarred forecasting errors (MSFE) for each model and for each response.

My question is:

How can we jointly test (by simultaneously analyzing all three MSFE), which model is more accuracte in terms of predicting ALL stocks in the 1-month ahead setting?


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.