Respondent-driven sampling is a network sampling technique typically employed for hard-to-reach populations (for example, drug users, men who have sex with men, people with HIV). Similarly to snowball sampling, initial seed respondents recruit additional respondents from their network of...

Multiple migrant populations are an under-researched group, perhaps because they pose particular challenges for survey research. First, they represent what literature refers to as a hard-to-survey population (Tourangeau 2014). Second, multiple migrations require taking into account multiple...

Multiple migrant populations are an under-researched group, perhaps because they pose particular challenges for survey research. First, they represent what literature refers to as a hard-to-survey population (Tourangeau 2014). Second, multiple migrations require taking into account multiple...

Respondent-driven sampling is a network sampling technique typically employed for hard-to-reach populations (for example, drug users, men who have sex with men, people with HIV). Similarly to snowball sampling, initial seed respondents recruit additional respondents from their network of...

This paper studies inference on fixed effects in a linear regression model estimated from network data. We derive bounds on the variance of the fixed-effect estimator that uncover the importance of the smallest non-zero eigenvalue of the (normalized) Laplacian of the network and of the degree...

This paper studies inference on fixed effects in a linear regression model estimated from network data. An important special case of our setup is the two-way regression model, which is a workhorse method in the analysis of matched data sets. Networks are typically quite sparse and it is...

Factor structures or interactive effects are convenient devices to incorporate latent variables in panel data models. We consider fixed effect estimation of nonlinear panel single-index models with factor structures in the unobservables, which include logit, probit, ordered probit and Poisson...

The development and application of models, which take the evolution of network dynamics into account, are receiving increasing attention. We contribute to this field and focus on a profile likelihood approach to model time-stamped event data for a large-scale dynamic network. We investigate the...

Analyses of spatial or network data are now very common. Nevertheless, statistical inference is challenging since unobserved heterogeneity can be correlated across neighboring observational units. We develop an estimator for the variance-covariance matrix (VCV) of OLS and 2SLS that allows for...