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YOURCAST implements the methods for demographic forecasting discussed in:
Federico Girosi and Gary King. 2008. Demographic Forecasting. Princeton: Princeton University Press, http://gking.harvard.edu/files/abs/smooth-abs.shtml.Please read at least Chapter 1 of the book before attempting to use YOURCAST.
At its most basic, YOURCAST runs linear regressions, and estimates the usual quantities of interest, such as forecasts, causal effects, etc. The benefit of running YOURCAST over standard linear regression software comes from the improved performance due to estimating sets of regressions together in sophisticated ways.
YOURCAST avoids the bias that results from stacking datasets from separate cross-sections and assuming constant parameters, and the inefficiency that results from running independent regressions in each cross-section. YOURCAST instead allows you to tie the different regressions together probabilistically in ways consistent with what you know about the world and your data. The model allows you to have different covariates with different meanings measured in different cross-sections.
For example, one might assume that the separate time series regressions in neighboring (or ``similar'') countries are more alike. Our approach is fully Bayesian, but you need not assume as the standard Bayesian approach does that the coefficients (which are never observed) in neighboring countries are similar. YOURCAST makes it possible to assume instead that neighboring countries are similar in their values or trends in the expected value of the dependent variable. This approach is advantageous because prior knowledge almost always exists about the dependent variable (such as that the age profile of mortality looks like the Nike swoosh), and the expected value is always on the same metric even when including explanatory variables that differ in number or meaning in each country.
The power of YOURCAST to improve forecasts comes from allowing one to smooth in many sophisticated ways, in addition to across countries. You can thus decide whether to smooth over indices that are geographic, grouped versions of underlying continuous variables (such as age groups), time, or interactions among these. For example, you can assume that, unless contradicted by the data, forecasts should be relatively smooth over time, or that the forecast time trends should be similar in adjacent age groups, or even that the differences in time trends between adjacent age groups stay roughly similar as they vary over countries. The model works with time-series-cross-sectional (TS-CS) data but also data for which the time series varies over more than one cross-section (TS-CS-CS-CS... data such as log-mortality over time by age, country, sex, and cause). The specific notion of ``smoothness'' or ``similarity'' used in YOURCAST is also your choice. The assumptions made by the statistical model are therefore governed your choices, and the sophistication of those assumptions and the degree to which they match empirical reality are, for the most part, limited only by what you may know or are willing to assume rather than arbitrary choices embedded in a mathematical model. In our work, we have found that YOURCAST makes it possible to improve forecasts well beyond that possible with traditional regression (or autoregression) strategies, although of course we make no promises about the future except that your performance may vary.