Detail práce

Least Absolute Shrinkage and Selection Operator Method

Autor: Bc. Jana Vorlíčková
Rok: 2017 - letní
Vedoucí: RNDr. Michal Červinka Ph.D.
Konzultant:
Typ práce: Bakalářská
Jazyk: Anglicky
Stránky: 48
Ocenění:
Odkaz: https://is.cuni.cz/webapps/zzp/detail/185493/
Abstrakt: The main intention of the thesis is to present several types of penalization
techniques and to apply them in economic analyses. We focus on penalized
least squares, with a main topic being the lasso. The penalization methods
are commonly employed to data sets with a relatively large number of the
variables as compared to the sample size. These methods simplify the model
by shrinkage of the estimates of the coefficient of the irrelevant variables towards
zero or they put these estimates equal to zero, i.e. they produce a
sparse solution. Namely, we present the following methods: ridge regression,
best subset selection problem, lasso and elastic net. We discuss several
applications of the lasso in the current economic and finance research and
hence present the lasso in more details. In the practical part of the thesis,
we analyze a real economic data using the elastic net, the ridge regression,
the lasso and the ordinary least squares method. We use the mean squared
error as the measure of performance of the respective method. The penalized
least squares methods surpass the ordinary least squares method, with
the elastic net being the best performing method.
Prosinec 2017
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Partneři

ČSOB
Deloitte
McKinsey & Company

Sponzoři

CRIF