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You can model panel data with xt.

Calculate durations, such as ages and other differences between datetimes.
Calculate relative dates, or dates from other dates, such as the previous or next birthday or anniversary relative to a reference date.
Extract individual components from datetime values and variables.

After VAR, you want a dynamic forecast.

The posterior density (shown in red) is more peaked and shifted to the left compared with the prior
distribution (shown in blue). The posterior distribution combined the prior information about with
intro — Introduction to Bayesian analysis 3
the information from the data, from which y = 0 provided evidence for a low value of and shifted
the prior density to the left to form the posterior density. Based on this posterior distribution, the
posterior mean estimate of is 2=(2 + 40) = 0.048 and the posterior probability that, for example,
< 0.10 is about 93%.
If we compute a standard frequentist estimate of a population proportion as a fraction of the
infected subjects in the sample, y = y=n, we will obtain 0 with the corresponding 95% confidence
interval (y �� 1.96
p
y (1 �� y)=n; y + 1.96
p
y (1 �� y)=n) reducing to 0 as well. It may be difficult
to convince a health policy maker that the prevalence of the disease in that city is indeed 0, given
the small sample size and the prior information available from comparable cities about a nonzero
prevalence of this disease.
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