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Long-term variance of AR(p)

UPDATE: I think I've figured it out. The Yule-Walker equations are the correct ones. When I was opening the expression of variance I was considering that $V[A+B] = V[A] + V[B]$. I was forgetting the ...
José Ivan's user avatar
0 votes

VAR model variable selection

Maybe it's a bit too late to your exam. I think it's natural that you can't find relationship for stocks, which have high noise to signal. My preliminary thought is that if you want to stick with ...
Huang Ching's user avatar
1 vote

Difference between zero-inflated model and zero-altered model

family = nbinom2() does not specify a zero-inflated model. It specifies a negative binomial distribution using the "quadratic parameterization" (hence ...
jbowman's user avatar
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3 votes
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When to include random-effects in zero-inflation model component?

Is it appropriate to specify random-effects (RE) in zero-inflation (ZI) component of the model? It certainly can be! So long as the zero inflation is not entirely determined by the unit for which ...
Demetri Pananos's user avatar
1 vote

Regarding explosive AR processes and stationarity

changing an explosive AR process to be in a future dependent form is pointless, real life does not operate that way Is there a reason we don't consider the case of $|\phi|>1$? Is it also because &...
ABCBAA's user avatar
  • 73
2 votes

Brockwell/Davis seem to say more persistence implies better predictability---do I have a counterexample?

As things stand, I cannot see how the prediction MSE for any process that regularly has an error term $\epsilon_t$ with variance $\sigma^2$ coming in, independently of what happened before, can ever ...
Christian Hennig's user avatar
2 votes
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Time series regression on mixed frequency overlapping data

There may be overlap between observations of the dependent variable or the regressors. If the observations of the dependent variable overlap (e.g. $y_t$ and $y_{t-1}$ measure partly the same thing), ...
Richard Hardy's user avatar
2 votes

Convergence of predictions of an autoregressive model

Autoregressive model you are testing generates outputs by taking linear combinations of the last two observations in the sequence. Such a model can be represented with the Linear State Space ...
Han's user avatar
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