主  題：    Adaptive Estimation in Multiple Time Series with Independent Component Errors (with L. Taylor)

內(nèi)容簡(jiǎn)介： This paper develops methodology for semiparametric econometric models for multiple time series of possibly high dimension N, for example VAR models with innovations having nonparametric distributional form . The objective is to obtain precise estimates of unknown parameters (which characterize autocorrelations and cross-autocorrelations) without fully parameterizing other distributional features, while imposing a degree of parsimony to mitigate a curse of dimensionality. The innovations vector is modelled as a linear transformation of independent but possibly non-identically distributed random variables, whose distributions are nonparametric. In such circumstances, Gaussian pseudo-maximum likelihood estimates of the parameters are typically √n-consistent, where n denotes series length, but asymptotically inefficient unless the innovations are in fact Gaussian. Our "adaptive" parameter estimates, are asymptotically as first-order efficient as maximum likelihood estimates based on correctly-specified parametric innovations distributions.  The adaptive estimates use nonparametric estimates of score functions (of the elements of the underlying vector of independent random variables) which involve truncated expansions in terms of basis functions; these have advantages over the kernel-based score function estimates used in most adaptive estimation literature. Our parameter estimates are also √n-consistent and asymptotically normal. A Monte Carlo study of finite sample performance, employing a variety of parameterizations, distributions and choices of N, is reported.

報(bào)告人：Peter Robinson    Tooke Professor

時(shí)  間：2016-06-27    09:30

地  點(diǎn)：敏行樓102

舉辦單位：經(jīng)濟(jì)與金融研究院