7 research outputs found
Spillover bias in multigenerational income regressions
Intergenerational persistence estimates are susceptible to several well-documented biases arising
from income measurement, and it has become standard practice to construct income measures to
mitigate these. However, remaining bias can lead to a spurious grandparent coefficient estimate in
multigenerational regressions, a recent focus of the mobility literature. We show with theory and
simulations that even using a 30-year income average can result in a small positive spurious
grandfather coefficient estimate. We further propose an IV approach, showing that it is not
susceptible to this spillover bias in simplified settings and that it can provide bounds on the
parameters in a more general scenario. With administrative data from Norway, we reveal a positive
spillover bias in the grandfather coefficient estimates, and the combined evidence from our OLS and
IV approaches suggest the preferred small positive OLS estimate could still be upward biased
Spillover bias in multigenerational income regressions
Intergenerational persistence estimates are susceptible to several well-documented biases arising
from income measurement, and it has become standard practice to construct income measures to
mitigate these. However, remaining bias can lead to a spurious grandparent coefficient estimate in
multigenerational regressions, a recent focus of the mobility literature. We show with theory and
simulations that even using a 30-year income average can result in a small positive spurious
grandfather coefficient estimate. We further propose an IV approach, showing that it is not
susceptible to this spillover bias in simplified settings and that it can provide bounds on the
parameters in a more general scenario. With administrative data from Norway, we reveal a positive
spillover bias in the grandfather coefficient estimates, and the combined evidence from our OLS and
IV approaches suggest the preferred small positive OLS estimate could still be upward biased
Distributed control of plug-in hybrid electric vehicles on a smart grid
This paper explores the effect of a distributed control system for the charging of plug-in hybrid electric vehicles utilizing an agent-based approach in a smart grid. The vehicles are regarded as additional loads in addition to a primary forecasted load and use information transfer with the grid to make their charging decisions. MATLAB was used as the simulation tool to design the control strategy and simulate its effect on a power grid. The findings of this study are that the charging behavior and peak loads on the grid can be reduced by use of this distributed control strategy