st: Bivariate time series regression

["Lott, Jason" <jason.lott@yale.edu>]

Hello everyone,

This is my first Statalist posting, so please forgive me for any
errors or confusion.

I have two variables for which I am assessing a potential association:

(1) [Dependent variable]: Age-adjusted melanoma incidence rates for
years 1935 to 2007. This is count data.

(2) [Independent variable]: Yearly sunspot intensity numbers (a
measure of yearly solar flare activity) for years 1935 to 2007. This
is continuous data.

The hypothesis being tested is that peak sunspot activity is
associated with a future increase in yearly age-adjusted melanoma
incidence rates. Sunspot activity is cyclical, with peak activity
occurring approximately every 11 years. Yearly age-adjusted melanoma
incidence rates are proposed to depend on prior, lagged peaks.

I have performed some initial trend analyses of each variable, both of
which appear to be stationary.

My questions:

(1) Is choice of an ARIMAX model in Stata, with yearly (and lagged)
sunspot activity numbers as a dependent covariate appropriate?

(2) Does Stata have Poisson-based regression packages that can account
for autocorrelated dependent and independent time series variables?

As a neophyte health services researcher, I do not typically work with
time series data, and I apologize for my lack of sophistication. I am
more than happy to provide any additional information that may be
needed, and I appreciate any help or guidance that might be provided.

My goal is to account for both time series (and autocorrelated) aspects of both variables in a regression model, while also having the flexibility to introduce time-lags of my independent variable as additional covariates. 

Sincerely,

Jason Lott

Jason P. Lott, MD MSHP
Post-Doctoral Fellow
Robert Wood Johnson Clinical Scholars Program &
Department of Dermatology
Yale University School of Medicine


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