Modeling of complex stochastic systems via latent factors
Palestrante: Hedibert F. Lopes (Chicago Booth)
HorÃrio: 11:00 horas
Dia: 30/01/2012
Local: Sala 314 - PavilhÃo Engenharia
Abstract:
Factor models, and related statistical tools for dimension
reduction, have been widely and routinely used in psychometric, item
response theory, geology, econometric and biological, amongst many other
fields, since the late 1960's when Karl G. Joreskog,
a Swedish statistician, proposed the first reliable numerical method
for maximum likelihood estimation (MLE) in factor analysis(Joreskog,
1969). Such developments happened, certainly not by chance, around the
same time the computer industry was experiencing
major advances.
From a Bayesian perspective, Martin and McDonald (1975) showed that MLE
suffers from several inconsistency issues (for instance, negative
idiosyncratic variances). Nonetheless, Bayesian researchers themselves
could not produce general algorithms for exact posterior
inference for factor models until the early 1990's when the computer
industry had another wave of majoradvances and Markov chain Monte Carlo
(MCMC) schemes were almost instantly customized for all fields cited
above.
In this talk, my goal is to illustrate how such advances, both in factor
modeling and statistical computing, have driven my own research in
financial econometrics, spatio-temporal modeling and macro- and
micro-economics, among others. This will be done by linking
my own work to current trends in modern Bayesian modeling of high
dimensional and data enriched problems.
Apoio: PPGEEA - ESALQ - USP