Seminari di Statistica a Pavia

[Igor Pruenster <igor@eco.unipv.it>]

UNIVERSITA' DEGLI STUDI DI PAVIA
DIPARTIMENTO DI ECONOMIA POLITICA E METODI QUANTITATIVI
DIPARTIMENTO DI MATEMATICA "F.CASORATI"
DOTTORATO DI RICERCA IN MATEMATICA E STATISTICA
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			AVVISO DI SEMINARI

Giovedi' 3 giugno nell'aula del Consiglio di Facolta', Dipartimento di 
Economia Politica e Metodi Quantitativi (via San Felice 5, Pavia) si 
terranno i seguenti seminari.


Ore 16: 	FRANCESCO BILLARI  (UNIVERSITA' "L. BOCCONI", MILANO)

	EDUCATIONAL CAREERS AND THE TRANSITION TO MOTHERHOOD:
      A SIMULTANEOUS-HAZARD COMPARATIVE STUDY OF THEIR INTERRELATIONS *

Abstract:
We study the interrelationship between the educational trajectories of 
women and the transition to motherhood, disentangling the mutual influence 
(i.e. the impact of events in one trajectory on events in the other 
trajectory) from common factors simultaneously affecting both trajectories 
(i.e. aspirations and orientations). We argue that both institutional and 
cultural factors at the macro level matter in shaping this 
interrelationship. In order shed light on the empirical relevance of such 
macro-level factors, we adopt an international comparative approach. We 
apply simultaneous hazard models to individual-level data from Fertility 
and Family Surveys (FFS) on 11 Western European countries. We hypothesize 
that, within Western Europe, welfare regimes and long-standing cultural 
differences shape the interrelations differently in different countries. In 
our empirical analyses, we indeed find important international differences 
both in the mutual influence and in the importance of common factors for 
both processes.

* Joint work with Dimiter Philipov


Ore 16.45:	STEFANO SAMPIETRO (UNIVERSITA' "C. CATTANEO", CASTELLANZA)

	BAYESIAN ANALYSIS FOR MIXTURE OF AUTOREGRESSIVE COMPONENTS
		WITH APPLICATION TO FINANCIAL MARKET VOLATILITY

Abstract:
In this paper, we present a Bayesian analysis of a non-linear time series 
model. Specifically, we consider a finite mixture of normal distributions 
and we relax the usual assumption of conditionally independent observable 
variables. Parameter estimation and model selection are performed trough 
Markov chain Monte Carlo (MCMC) methods. Our analysis takes into account 
the stationarity conditions on the model parameters. Finally, an 
application to return volatility of financial markets will be illustrated.

		
Tutti gli interessati sono invitati a partecipare.

-- 
Igor Pruenster
Dip. Economia Politica e Metodi Quantitativi
Universita' degli Studi di Pavia
Via San Felice, 5 - 27100 Pavia
Tel.: +39 0382506213     Fax: +39 0382304226
http://economia.unipv.it/igor



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