Vector Autoregression (VAR) is often used for modeling sales of P items over time. VAR forecasts sales at time tnew using previous sales at tlag, coupled with attributes explaining those changes like price, distribution, and trend. We also model correlated sourcing among P items using a simulated population ~ Multivariate normal(α_lag, ∑). We show how to use conjoint experiments to inform ∑ and how that significantly improves predictions versus modeling ∑ from sales data alone.