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NAME:PhD defence G.B. Banava
METHOD:PUBLISH
BEGIN:VEVENT
DTSTART:20260511T134500
DTEND:20260511T151500
DTSTAMP:20260511T134500
UID:phd-defence-g-b-banava@8F96275E-9F55-4B3F-A143-836282E12573
CREATED:20260922T074132
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SUMMARY:PhD defence G.B. Banava
X-ALT-DESC;FMTTYPE=text/html: Targeted Estimation
in Heterogeneous Panel Data Models
Predicting more accura
tely by learning from comparable units
Data scientist Georgia
Banava investigated how statistical predictions for specific units, s
uch as a single country or a specific region, can be improved. While
standard econometric models often calculate an average effect across
all available data, Banava developed three new methods that focus spe
cifically on one particular unit. This makes it possible, for example
, to predict the GDP of the Netherlands very precisely, without leavi
ng valuable data from the rest of Europe unused.
Banava's resea
rch demonstrates that 'borrowing' information from comparable units l
eads to much more reliable, accurate, and stable estimates. Instead o
f analyzing each unit separately or simply averaging everything, her
methods make optimal use of all information while carefully accountin
g for differences between them. This approach offers major practical
benefits: for instance, hospitals can better evaluate the effectivene
ss of treatments per patient group, and smaller regions with limited
data can still make reliable predictions regarding unemployment or ec
onomic growth by learning from comparable regions.
More informa
tion on the thesis
DESCRIPTION: Targeted Estimation in Heterogeneous Panel Data Models Predicting more accurately by learning from comparable unitsDa
ta scientist Georgia Banava investigated how statistical predictions
for specific units, such as a single country or a specific region, ca
n be improved. While standard econometric models often calculate an a
verage effect across all available data, Banava developed three new m
ethods that focus specifically on one particular unit. This makes it
possible, for example, to predict the GDP of the Netherlands very pre
cisely, without leaving valuable data from the rest of Europe unused.
Banava's research demonstrates that 'borrowing' information from comp
arable units leads to much more reliable, accurate, and stable estima
tes. Instead of analyzing each unit separately or simply averaging ev
erything, her methods make optimal use of all information while caref
ully accounting for differences between them. This approach offers ma
jor practical benefits: for instance, hospitals can better evaluate t
he effectiveness of treatments per patient group, and smaller regions
with limited data can still make reliable predictions regarding unem
ployment or economic growth by learning from comparable regions.More
information on the thesis
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