[Forum SIS] Seminar, Roberta Varriale, ERRATA CORRIGE

[Marco Alfo']

We are glad to announce the seminar:

* friday*, November 29, 14.00 pm

 Room 34, Statistics Building, Sapienza Universita' di Roma

"Machine learning methods for estimating the employment status in Italy"

Roberta Varriale (ISTAT)

Abstract
In recent decades, National Statistical Institutes have focused on
producing official statistics by exploiting multiple sources of information
(multi-source statistics) rather than a single source, usually a
statistical survey. The growing importance of producing multi-source
statistics in official statistics has led to increasing investments in
research activities in this sector.
In this context, one of the research projects addressed by the Italian
National Statistical Institute (Istat) concerned the study of methodologies
for producing estimates on employment rates in Italy through the use of
multiple sources of information, survey data and administrative sources.
The data comes from the Labour Force (LF) survey conducted by Istat and
from several administrative sources that Istat regularly acquires from
external bodies. The “quantity” of information is very different: those
coming from administrative sources concern about 25 million individuals,
while those coming from the LF survey refer to an extremely limited number
(about 330,000) of individuals. The two measures do not agree on employment
status for about 6% of the units from the LF survey.
One proposed approach uses a Hidden Markov model to take into account the
deficiencies in the measurement process of both survey and administrative
sources. The model describes a measurement process as a function of a
time-varying latent state (in this case the employment category), whose
dynamics is described by a Markov chain defined over a discrete set of
states. At present, the implementation phase for the production process of
statistics on employment through the use of HM models is coming to an end
in Istat.
The present work describes the use of Machine Learning methods to predict
the individual employment status. This approach is based on the application
of decision tree and random forest models, that are predictive models,
usually used to classify instances of large amounts of data. In the work,
the obtained results will be described, together with their usefulness in
this application context. The models have been applied through the use of
the software R.
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