Sem-parametric Bootstrapping - Repeated Model Simulation

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Eddy360
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Joined: Thu Jun 09, 2022 1:39 am

Sem-parametric Bootstrapping - Repeated Model Simulation

Postby Eddy360 » Tue Jun 28, 2022 9:20 pm

Hi,

I'm trying to develop a program that can produce forecast confidence intervals for model averages, using bootstrapped residual series to generate data for estimation. My method is currently:

1. Estimate the models on the original data Y and extract the residuals and fitted values.
2. Resample the residuals n times (e.g. 1000 times) and generate n new Y* series by adding the resampled residuals to the fitted values.
3. Re-estimate the models n times on Y*.
4. In each n re-estimation, generate the forecast ahead using the original Y series but with the newly estimated coefficients.
5. Save the forecasts and repeat to build up a distribution of forecasts.

My problem is in step 4. I've managed to estimate the models on the generated data but I can't figure out a way to then apply those coefficients to a different data set (the actual/original data). Forecasting with the generated series is incorrect as each repetition's forecasts will originate at a different starting point and the distribution will be much wider than what it should be.

I thought there might be a way to code this using a 'model' with different scenarios but it seems that scenarios can only apply to exogenous variables. Is there a way to apply an estimated model to different data for forecasting? Hoping there is a much simpler routine built in than needing to save the coefficients and manually specifying the equations which can get messy with different model classes (E.g. MIDAS, VAR, etc).

EViews Gareth
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Joined: Tue Sep 16, 2008 5:38 pm

Re: Sem-parametric Bootstrapping - Repeated Model Simulation

Postby EViews Gareth » Wed Jun 29, 2022 8:38 am

If you've estimated an equation, and then forecast from that equation, the forecast will use whatever data is currently in the series. So if you where to do something like:
  • Generate Y and X
  • Estimate equation
  • Change data in X
  • Forecast from equation
The forecast would use the new data in X, not the data that was used to estimate with.

Not sure if that helps or not.
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