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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:High Dimensional Stochastic Regression with Latent
Factors\,Endogeneity and Nonlinearity - Yao\, Q (
London School of Economics)
DTSTART;TZID=Europe/London:20140114T141000
DTEND;TZID=Europe/London:20140114T145000
UID:TALK49860AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/49860
DESCRIPTION:We consider a multivariate time series model which
represents a high dimensional vector process as a
sum of three terms: a linear regression of some o
bserved regressors\, a linear combination of some
latent and serially correlated factors\, and a vec
tor white noise. We investigate the inference with
out imposing stationary conditions on the target m
ultivariate time series\, the regressors and the u
nderlying factors. Furthermore we deal with the th
e endogeneity that there exist correlations betwee
n the observed regressors and the unobserved facto
rs. We also consider the model with nonlinear regr
ession term which can be approximated by a linear
regression function with a large number of regress
ors. The convergence rates for the estimators of r
egression coefficients\, the number of factors\, f
actor loading space and factors are established un
der the settings when the dimension of time series
and the number of regressors may both tend to inf
inity together with the sample size. The proposed
method is illustrated with both simulated and real
data examples.\n
LOCATION:Seminar Room 1\, Newton Institute
CONTACT:Mustapha Amrani
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