Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reduced-Rank Covariance Estimation in Vector Autoregressive Modeling   We consider reduced-rank modeling of the white noise covariance matrix in a
large dimensional vector autoregressive (VAR) model. We first propose the
reduced-rank covariance estimator under the setting where independent
observations are available. We derive the reduced-rank estimator based on a
latent variable model for the vector observation and give the analytical form
of its maximum likelihood estimate. Simulation results show that the
reduced-rank covariance estimator outperforms two competing covariance
estimators for estimating large dimensional covariance matrices from
independent observations. Then we describe how to integrate the proposed
reduced-rank estimator into the fitting of large dimensional VAR models, where
we consider two scenarios that require different model fitting procedures. In
the VAR modeling context, our reduced-rank covariance estimator not only
provides interpretable descriptions of the dependence structure of VAR
processes but also leads to improvement in model-fitting and forecasting over
unrestricted covariance estimators. Two real data examples are presented to
illustrate these fitting procedures.

30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
1
The categories of this paper are: cs
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol