Econometric analysis of financial and economic time series - Part B
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Dek Terrell, Thomas Fomby, R. Carter Hill | |||
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Advances in Econometrics | |||
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JAI Press | |||
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2006 | |||
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English | |||
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379 pages | |||
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3.06 MB | |||
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The editors are pleased to offer the
following papers to the reader in recognition and appreciation of the
contributions to our literature made by Robert Engle and Sir Clive Granger,
winners of the 2003 Nobel Prize in Economics. The basic themes of this part of
Volume 20 of Advances in Econometrics are time varying betas of the capital
asset pricing model, analysis of predictive densities of nonlinear models of
stock returns, modelling multivariate dynamic correlations, flexible seasonal
time series models, estimation of long-memory time series models, the
application of the technique of boosting in volatility forecasting, the use of
different time scales in GARCH modelling, out-of-sample evaluation of the 'Fed
Model' in stock price valuation, structural change as an alternative to long
memory, the use of smooth transition auto-regressions in stochastic volatility
modelling, the analysis of the "balanced-ness" of regressions analyzing
Taylor-Type rules of the Fed Funds rate, a mixture-of-experts approach for the
estimation of stochastic volatility, a modern assessment of Clive's first
published paper on Sunspot activity, and a new class of models of
tail-dependence in time series subject to jumps. *This Series: Aids in the
diffusion of new econometric techniques *Emphasis is placed on expositional
clarity and ease of assimilation for readers who are unfamiliar with a given
topic of a volume *Illustrates new concepts
Dek Terrell is a Professor at
Louisiana State University.Dr. Tom Fomby is an applied econometrician whose
broad research interests include macroeconomics (including the validation of
leading indicators for growth in national and regional economics with the
Federal Reserve Bank of Dallas), the analysis of social networks and their
impacts on food insecurity of children (with a Hunger Center Grant), predictive
analytics as applied to crime prevention (with the Dallas Police Department and
the SMU Computer Science Department), count and panel models as applied to
epidemiological data (with the University of Texas Southwestern Medical Center),
growth aftermaths of natural disasters (with the World Bank), and analysis of
return dependence via multivariate copulas (with the Federal Reserve Bank of
Dallas.
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