Fixed Effects and Bias Due to the Incidental Parameters Problem in the Tobit Model

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Tobit models in strategy research: Critical issues and applications (2019, background): “For details see Greene (2004) and Lancaster (2000).”
The Structure of the Product Space and the Evolution of Comparative Advantage (2007, background): “16 As illustrated by Greene (2004), the maximum likelihood estimator with fixed effects sizes suffers from an incidental parameters problem which biases the results when groups are small, as is our case, so we can not use a probit estimation.”
The Impact of Added Cultural Distance and Cultural Diversity on International Expansion Patterns: A Penrosean Perspective (2011, unknown): “…choice models with fixed effects is biased and inconsistent, but it has also been shown that there is essentially no bias in the slope estimators of a Tobit model and a bias in the estimator of the disturbance variance is small and can be neglected with a panel length of 5 or more (Greene, 2004).”
Syndication, Interconnectedness, and Systemic Risk (2018, unknown): “, Green [24]).”
The Role of Risk Management in Mergers and Merger Waves (2011, unknown): “12 While the Tobit model accommodates the dependent variable being bounded on (0,1), it cannot be combined with industry fixed effects (Greene, 2004).”
Political Uncertainty and IPO Activity: Evidence from U.S. Gubernatorial Elections (2017, methodology): “However, as Greene (2004) points out, the problem is much less severe for the Tobit model than for binary choice models such as logit.”