8
votes
Is it fair to say: "All Time Series data have some autocorrelation"?
The main reason for (auto)correlation is that the transition from one state to another one is not completely random, meaning that given a State Space of size $|S|$ the transition probability is non-...
- 841
6
votes
Accepted
Residual error covariance structure in longitudinal mixed models
Indeed, when you include functions of time in the design matrix of the random effects you account for serial correlations in the repeated measurements data. The more complex functions of time you ...
- 20.1k
6
votes
Is it fair to say: "All Time Series data have some autocorrelation"?
No, in the following sense. In finance, it is common to assume that autocorrelations are equal to zero for logarithmic returns on stocks. The assumption often turns out to be approximately true, in ...
- 64.9k
5
votes
Is it fair to say: "All Time Series data have some autocorrelation"?
Is it fair to say: "All Time Series data have some autocorrelation”?
NO
Here is an algorithm to produce a time series that lacks autocorrelation.
At $t=1$, draw a point from a distribution, ...
- 58.4k
5
votes
Accepted
Is the sample mean an unbiased estimator of population mean in the presence of autocorrelation?
Yes, autocorrelation (or spatial correlation or ...) do not destroy the unbiasedness of the sample mean as an estimator of population mean.
Expectation is a linear operator, so when you calculate the ...
- 74.7k
4
votes
Is it fair to say: "All Time Series data have some autocorrelation"?
I don't think it makes sense to say that any formal statistical model is "true" in reality; models are always idealisations, and what happens in reality is something different. Even the most ...
- 21.3k
3
votes
Is it fair to say: "All Time Series data have some autocorrelation"?
No
... probably a selection bias?
I assume you make additional assumptions about what a time series is.
A timeseries is a dataset, where time is an additional variable.
You can draw $N$ random numbers ...
- 1,117
2
votes
Accepted
R joincount with non-integer counts
Is this weight what is causing my expected counts to be non-integer?
The observed behaviour is indeed related to the weights in your adjacency matrix.
Should I be using non 0/1 weights with ...
- 57.1k
1
vote
Modelling count data with variable upper bound
The key issue here is that there are latent (discrete) count variable that potentially take values from 10 to 14 for each pack of cigarettes a person has. We can make simplifying assumptions like that ...
- 30.6k
1
vote
Accepted
standardized residuals GARCH
These are not $p$-values, these are estimated autocorrelations $\hat\rho(h)$ for $h=0,1,\dots,10$. $\hat\rho(0)=\widehat{\text{Corr}}(X_t,X_{t-0})=1$, obviously, thus the value of $1$ at lag $0$. The ...
- 64.9k
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