network profile
| bio | website | |
|---|---|---|
| location | ||
| age | ||
| visits | member for | 1 year, 3 months |
| seen | Apr 4 at 18:20 | |
| stats | profile views | 13 |
A poor ignorant who humbly seeks to learn.
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Jan 15 |
comment |
Autoregressive model with exponential lags sorry for my delay. In your model your saying that a shock at time $t$ has a long impact in your time series. Your ACF decays very slowly. As you know you prefer a parsimonious model, in your model you have 1000 parameters to estimate! in ARFIMA model you have for the long memory only one: $d$. |
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Jan 8 |
awarded | Teacher |
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Dec 8 |
answered | Autoregressive model with exponential lags |
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Sep 17 |
awarded | Commentator |
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Sep 17 |
comment |
Estimation/identification simple problem Do you think I can impose a restriction on k? For example k>0 |
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Sep 17 |
comment |
Estimation/identification simple problem Mpiktas, thank you. So nonlinear regression model should be the way. |
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Sep 17 |
accepted | Estimation/identification simple problem |
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Sep 17 |
comment |
Estimation/identification simple problem @mpiktas just to allow forecasts |
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Sep 17 |
comment |
Estimation/identification simple problem @Chernick normal and independent and identically distributed |
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Sep 17 |
asked | Estimation/identification simple problem |
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May 12 |
awarded | Scholar |
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May 12 |
comment |
Some doubts about conditional expectation Thank you very much. So I know that a covariance matrix has to be positive definite. Can you provide me some reference on conditional expectation and variance for this kind of exercises? |
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May 12 |
accepted | Some doubts about conditional expectation |
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May 11 |
comment |
Some doubts about conditional expectation $Var(Z_t)=Var(X_t)+Var(Y_tX_t)+Var(Y_t)$ where $Var(Y_tX_t)=Var(Y_t)*Var(X_t)+COV(X,Y)$ therefore $Var(Z_t)=1 + 1 \times 4 + 2 + 4 = 11$ ? |
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May 11 |
revised |
Some doubts about conditional expectation added 6 characters in body |
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May 11 |
comment |
Some doubts about conditional expectation You're right my mistake. I fix it immediately. But I don't get the point on the variance. |
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May 11 |
revised |
Some doubts about conditional expectation added 120 characters in body |
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May 11 |
asked | Some doubts about conditional expectation |
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May 5 |
comment |
Parameter estimation from a Normal distribution Ok, I get your point. So I show that $E(\bar{X})=\frac{1}{T}\sum E(X_i)$ therefore $E(\bar{X})=\frac{1}{T}\sum \mu$ concluding $E(\bar{X})=\frac{1}{T}T \mu = \mu$. The same for the variance and also for the property. |
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May 4 |
comment |
Parameter estimation from a Normal distribution Dear Jbowman, thank you for your reply. What do you mean "at a sufficient level of detail", the sample mean is an unbiased estimator for the mean of the true population. So you're right that $\bar{X}=\frac{1}{T}\sum X_i$ but I don't see the reason to include in the proof. I think all we need for the proof is the properties of the expectation operator, variance and the sample mean distribution (asymptotic distribution of the estimator mean). Or Am I wrong? |
