We provide estimation methods for panel nonseparable models based on low-rank factor structure approximations. The factor structures are estimated by matrixcompletion methods to deal with the computational challenges of principal component analysis in the presence of missing data. We show that...

We provide estimation methods for nonseparable panel models based on low-rank factor structure approximations. The factor structures are estimated by matrix-completion methods to deal with the computational challenges of principal component analysis in the presence of missing data. We show that...

This paper studies linear panel regression models in which the unobserved error term is an unknown smooth function of two-way unobserved fixed effects. In standard additive or interactive fixed effect models the individual specific and time specific effects are assumed to enter with a known...

We provide estimation methods for nonseparable panel models based on low-rank factor structure approximations. The factor structures are estimated by matrix-completion methods to deal with the computational challenges of principal component analysis in the presence of missing data. We show that...

This paper studies linear panel regression models in which the unobserved error term is an unknown smooth function of two-way unobserved fixed effects. In standard additive or interactive fixed effect models the individual specific and time specific effects are assumed to enter with a known...

We provide estimation methods for panel nonseparable models based on low-rank factor structure approximations. The factor structures are estimated by matrixcompletion methods to deal with the computational challenges of principal component analysis in the presence of missing data. We show that...