READ ONLINE Multiple Time Series Modeling Using the SAS VARMAX Procedure

Multiple Time Series Modeling Using the SAS
VARMAX Procedure
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Aimed at econometricians who have completed at least one course in time series modeling,
Multiple Time Series Modeling Using the SAS VARMAX Procedure will teach you the time series
analytical possibilities that SAS offers today. Estimations of model parameters are now performed
in a split second. For this reason, working through the identifications phase to find the correct
model is unnecessary. Instead, several competing models can be estimated, and their fit can be
compared instantaneously.Consequently, for time series analysis, most of the Box and Jenkins
analysis process for univariate series is now obsolete. The former days of looking at crosscorrelations
and pre-whitening are over, because distributed lag models are easily fitted by an
automatic lag identification method. The same goes for bivariate and even multivariate models, for
which PROC VARMAX models are automatically fitted. For these models, other interesting
variations arise: Subjects like Granger causality testing, feedback, equilibrium, cointegration, and
error correction are easily addressed by PROC VARMAX.One problem with multivariate modeling
is that it includes many parameters, making parameterizations unstable. This instability can be
compensated for by application of Bayesian methods, which are also incorporated in PROC
VARMAX. Volatility modeling has now become a standard part of time series modeling, because
of the popularity of GARCH models. Both univariate and multivariate GARCH models are
supported by PROC VARMAX. This feature is especially interesting for financial analytics in which
risk is a focus.This book teaches with examples. Readers who are analyzing a time series for the
first time will find PROC VARMAX easy to use readers who know more advanced theoretical time
series models will discover that PROC VARMAX is a useful tool for advanced model building.