class of indirect inference models. Our smoothing procedure makes use of importance sampling weights in estimation of the … auxiliary model on simulated data. The importance sampling weights are constructed from likelihood contributions implied by the …

, (2) to achieve consistent estimates by correcting for endogenous sampling, and (3) to identify average partial effects in …

first characterize the biases in both network statistics and estimates of network effects under non-random sampling …-classical measurement-error problems when applied as regressors. Apart from the sampling rate and the elicitation procedure, these biases … applied to network data collected via both designed and non-designed sampling procedures, does not require one to assume any …

optimal sampling frequency at which to estimate the parameters of a discretely sampled continuous-time model can be finite … that sampling as often as possible is optimal. But, more surprisingly, we also demonstrate that this is true even if one …