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SUMMARY:High Dimensional Stochastic Regression with Latent Factors\,Endoge
 neity and Nonlinearity - Yao\, Q (London School of Economics)
DTSTART:20140114T141000Z
DTEND:20140114T145000Z
UID:TALK49860@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:We consider a multivariate time series model which represents 
 a high dimensional vector process as a sum of three terms: a linear regres
 sion of some observed regressors\, a linear combination of some latent and
  serially correlated factors\, and a vector white noise. We investigate th
 e inference without imposing stationary conditions on the target multivari
 ate time series\, the regressors and the underlying factors. Furthermore w
 e deal with the the endogeneity that there exist correlations between the 
 observed regressors and the unobserved factors. We also consider the model
  with nonlinear regression term which can be approximated by a linear regr
 ession function with a large number of regressors. The convergence rates f
 or the estimators of regression coefficients\, the number of factors\, fac
 tor loading space and factors are established under 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 wi
 th both simulated and real data examples.\n
LOCATION:Seminar Room 1\, Newton Institute
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