of} \ r_p(t) \over N_{\rm s} +2} - {1\over 2},
\label{eq:excess}$$ which represents how much the share of product $p$ grows faster (slower) than the median of the simulated growth rates. The $T$ excess growth rates $\{R_p(t)|1962\leq t\leq 2000\}$ for a product $p$ will be uniformly distributed between $-1/2$ and $1/2$ if simulations yield a good probabilistic prediction for the empirical growth rates $r_p(t)$’s of the product $p$ [@marhuenda2005comparison]; it should not be possible to discern empirical values from the simulated ones. Let us sort $t$’s such that $R_p(t_1)\leq R_p(t_2)\leq \cdots \leq R_p(t_T)$. If $R_p(t)$’s are uniformly distributed between $-1/2$ and $1/2$, then we expect that $R_p(t_i)$’s will increase linearly with $i$ such that $R_p(t_i) = \bar{R}_i = i/(T+1) -1/2$. Deviations from this expectation can quantify the non-uniformity of $R_p(t)$’s and the failure of the model to predict the growth rates $r_p(t)$’ of product $p$ [@marhuenda2005comparison]. Therefore we define the unpredictability $U_p$ of product $p$ by $$U_p=T^{-1} \sum_{i=1}^T |R_p(t_i) - \bar{R}_i|,
\label{eq:Up}$$ where the $t_i$’s are sorted such that $R_p(t_i)\leq R_p(t_j)$ for all $i<j$ and $\bar{R}_i=\frac{i }{ T+1}-\frac{1}{ 2}$. $U_p$ of $T=38$ numbers from the uniform distribution may be larger than $0.094$ with probability $0.05$. Therefore we consider products with 