This paper considers factor estimation from heterogenous data, where some of the variables are noisy and only weakly informative for the factors. To identify the irrelevant variables, we search for zero rows in the loadings matrix of the factor model. To sharply separate these irrelevant...

Imagine an impoverished region that becomes eligible for a generous transfer programme (the treatment). Imagine difference-in-differences analysis (DiD)-a before-and-after comparison of the income-level handicap-shows that the handicap has risen. Most observers would conclude to the policy's...

Imagine a region suffering from a widening income gap that becomes eligible for a generous transfer programme (the treatment). Imagine difference-in-differences analysis (DD) — a before-and-after comparison of the income-level difference — shows that the handicap has risen. Most observers...

Discrete choice models and qualitative response models are intimately related. Usually both are based on a logistic specification. In many applications the two approaches have been combined leading to a 'discrete choice model including socio-economic explanatory variables'. The paper desdribes...

In the general linear errors-in-variables model the main results have been derived under the assuption that the measurement errors are uncorrelated. However, as recognized by Bekker, Kapteyn and Wansbeek (BKW) (1997) and Lach (1993) this is often a problematic assumption to maintain in empirical...