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Dynamic Factor Models
Advances in Econometrics
Synopsis
Dynamic factor models (DFM) constitute an active and growing area of research, both in econometrics, in macroeconomics, and in finance. Many applications lie at the center of policy questions raised by the recent financial crises, such as the connections between yields on government debt, credit risk, inflation, and economic growth. This volume collects a key selection of up-to-date contributions that cover a wide range of issues in the context of dynamic factor modeling, such as specification, estimation, and application of DFMs. Examples include further developments in DFM for mixed-frequency data settings, extensions to time-varying parameters and structural breaks, for multi-level factors associated with subsets of variables, in factor augmented error correction models, and in many other related aspects. A number of contributions propose new estimation procedures for DFM, such as spectral expectation-maximization algorithms and Bayesian approaches. Numerous applications are discussed, including the dating of business cycles, implied volatility surfaces, professional forecaster survey data, and many more.
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What Reviewers Are Saying
Editors Hillebrand and Koopman present students, academics, researchers, and professionals working in a wide variety of contexts with a collection of academic and expert contributions on the use of dynamic factor models (DFM) in the study of econometrics, macroeconomics, and finance. The editors have organized the contributions that make up the main body of their text in four parts devoted to methodology, factor structure and specification, instability, and nowcasting and forecasting. Eric Hillebrand is a faculty member of Aarhus University, Denmark. Siem Jan Koopman is a faculty member of VU University, The Netherlands. -- Annotation (c)2016 * (protoview.com) *