Efficient Estimation of Bid-Ask Spreads from Open, High, Low,
and Close Prices
c(
person(given = "Emanuele", family = "Guidotti", email = "emanuele.guidotti@unine.ch", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-8961-6623")),
person(given = "David", family = "Ardia", role = c("ctb"), comment = c(ORCID = "0000-0003-2823-782X")),
person(given = "Tim", family = "Kroencke", role = c("ctb"), comment = c(ORCID = "0000-0001-8700-356X"))
)
Implements a novel estimation procedure of bid-ask spreads from open, high, low, and close prices
as described in Ardia-Guidotti-Kroencke <https://www.ssrn.com/abstract=3892335>.
Moreover, it provides an implementation of the estimators proposed in
Roll (1984) <doi:10.1111/j.1540-6261.1984.tb03897.x>,
Corwin-Schultz (2012) <doi:10.1111/j.1540-6261.2012.01729.x>,
and Abdi-Ranaldo (2017) <doi:10.1093/rfs/hhx084>.

Implements an efficient estimation procedure of bid-ask spreads from open, high, low, and close prices
as described in Ardia-Guidotti-Kroencke <https://www.ssrn.com/abstract=3892335>.
Moreover, it provides an implementation of the estimators proposed in
Roll (1984) <doi:10.1111/j.1540-6261.1984.tb03897.x>,
Corwin-Schultz (2012) <doi:10.1111/j.1540-6261.2012.01729.x>,
and Abdi-Ranaldo (2017) <doi:10.1093/rfs/hhx084>.

Inferences for bi- and trifactorial trial designs
This package makes global and multiple inferences for given bi- and trifactorial clinical trial designs using bootstrap methods and a classical approach.

This package makes global and multiple inferences for
given bi- and trifactorial clinical trial designs using
bootstrap methods and a classical approach.

c(person("Amrei", "Stammann", email = "amrei.stammann@hhu.de", role = c("aut", "cre")),
person("Daniel", "Czarnowske", email = "d.czarnowske@gmx.de", role = "aut"),
person("Florian", "Heiss", email = "florian.heiss@hhu.de", role = "aut"),
person("Daniel", "McFadden", email = "mcfadden@econ.berkeley.edu", role = "ctb"))
Estimates fixed effects binary choice models (logit and probit) with potentially many individual fixed effects and computes average partial effects. Incidental parameter bias can be reduced with a bias-correction proposed by Hahn and Newey (2004) <doi:10.1111/j.1468-0262.2004.00533.x>.
Amrei Stammann [aut, cre],
Daniel Czarnowske [aut],
Florian Heiss [aut],
Daniel McFadden [ctb]