Four levels of contamination ε were considered: 0 (clean data), . 10 , . 20 and . 30 . We took n = 50 as sample size, m = 20 as grid size, and p = q = 2 as model dimensions. Each case was replicated 1000 times. As measure of the estimation error we used the expected root integrated squared error E( ‖ ˆ β − β$_{0}$ ‖ ) , where ‖ ˆ β − β$_{0}$ ‖ 2 = ∫ 1 0 ∫ 1 0 { ˆ β ( s, t ) − β$_{0}$ ( s, t ) } 2 ds dt . The results are reported in Table 1, along with Monte Carlo standard errors. We see that for non-contaminated data ( ε = 0 ), there is no significant difference between metric and rank trimming for a given pair ( ν, α ) . The trimming proportion α has a larger impact on the estimator’s behavior than the degrees of freedom ν . For this reason we recommend choosing α adaptively, so as not to cut off too much good data. When ε > 0 , we see that metric trimming tends to outperform rank trimming for a given pair ( ν, α ) . Somewhat counterintuitively, estimators with ν = 5 tend to be more robust than those with ν = 1 for a given α ; the reason is that for this type of contamination, which affects ˆ Θ but not the ˆ φ$_{k}$ s or the ˆ ψ$_{l}$ s, t models with ν = 5 provide more accurate estimators of { U$_{i}$ } and { V$_{i}$ } than t models with ν = 1 (for other types of contamination this is no longer true, although t models with ν = 5 are still very robust; see Gervini (2009).) In general, then, the recommendation is to use t -model estimators with metrically trimmed weights and a trimming proportion chosen adaptively. The second series of simulations were designed to assess the finite-sample adequacy of the asymptotic Wald test. To this end we generated data as before, but with Θ$_{0}$ = O . Then Q = n vec( ˆ Θ ) T ˆ Ω − $^{1}$vec( ˆ Θ ) should approximately follow a χ 2 pq distribution, where Ω is the asymptotic covariance matrix of √$_{n}$$_{vec(}$ ˆ Θ ) . For GMt estimators, Ω is the “sandwich formula” given in Section 2.2; for the least-squa res estimator, Ω = E( RR $^{T}$) ⊗ { E( UU $^{T}$) } − $^{1}$. Table 2 reports the tail probabilities P( Q ≥ χ 2 pq, 1 − $_{α}$) for the usual values 10