Econometric analysis of financial and economic time series - Part B

	Author(s):	Dek Terrell, Thomas Fomby, R. Carter Hill
	Collection:	Advances in Econometrics
	Publisher:	JAI Press
	Year:	2006
	Language:	English
	Pages:	379 pages
	Size:	3.06 MB
	Extension:	PDF

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[content title="Description"]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
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[content title="About the author"]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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