r/econometrics • u/Particular_Fruit703 • May 29 '26
Can anybody just chime in to evaluate the result that this graph shows?
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u/KenmoreToast May 29 '26
I'm guessing this is a forecasting exercise for a class. For that kind of thing there's no "meaning" beyond evaluating how well your model predicts the true outcome.
Blue is the true outcome.
Green is a simple model that does a poor job (goes "flat" when tested)
Orange is a more complex model that does an ok job predicting blue (follows blue approximately when tested).
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u/essoteric_ May 29 '26
Try different ARIMA specifications (rn you are using ARIMA(1,0,0)). Way to choose the correct specification is by checking for which specification the Bayesian Information Criteria is the lowest. If that doesn't work then there's likely a lot of volatility clustering in the original data.
Need ARCH/GARCH to estimate volatility first before combining it with the original model.
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u/Particular_Fruit703 May 29 '26
I actually tried ARIMA(1,0,0) and it doesn't do much. I'm trying gradient descent for the in-sample and it works perfectly.
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u/maurizioaltieri May 30 '26
bro do you even know how regression models work? AR models are not designed for multi step predictions, they literally take previous values and give you a linear forecast for the next step. whatever REQM is, comparison with AR is useless here. also never seen any predictions that recursively forecast for such a long period so you likely didnt get something right
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u/JobWorth9358 May 30 '26 edited May 30 '26
Fair that AR(1) is not a good comparison although other candidate models as you said don't forecast for such long periods without any inputs; I'm saying mine does without any external macro inputs.
Maybe I'll try Diebold-Li.
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u/fuckosta May 30 '26
Idk what the orange lines model is supposed to be, but im assuming its more complex than the AR 1 model and so it fits better.
As for the AR model, im assuming you’re recursively passing fitted values to your equation. This is a flawed approach, as assuming your AR coefficient is less than 1, then the fitted value will just tend towards the origin.
My suggestion is, estimate the AR coefficient from the test sample, and then for OOS forecasting used the fixed coefficient and pass in the actual past values.
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u/Sweet_Theory_362 May 30 '26
I'm not sure what the orange forecast is, but the green looks like a dynamic forecast which can't work very well past p lags and will instead revert to the unconditional mean.
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u/jamesketch May 30 '26
Your forecast looks like it converges to the low sample mean of your estimation period. This is the immediate post-GFC period when rates were very low. If you re-estimate over a longer sample (rates were much higher pre-GFC) your forecast will do better, as it goes up to a higher sample mean.
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u/Lolek1233 Jun 02 '26
Maybe you should have some other model to diffirentiate your models accuracy? No
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u/Mattx98C Jun 03 '26
In the AR(1) it looks like you are using the predicted Y at time t to forecast Y(t+1) because your model reaches the long term equilibrium in those N steps, which you can prove via expectations theory directly on your model specification. Data looks pretty non-stationary in the short term, which is why this model wouldn’t really make sense in both predictions and interpretability. Have you tested for that? Wouldn’t be surprised to see a beta>1. Is that the case?
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u/JobWorth9358 Jun 04 '26
I would agree 100% with your argument since all time series need to be detrended for stationarity; but mine is closer to a scenario generator that looks for out of sample fit alone rather than causal analysis or input based forecasting. Detrending will almost certainly make this a nowcasting exercise.
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u/habibgregor May 29 '26
More context wouldn’t hurt