Dynamic Factor Models

Dynamic Factor Models

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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.Survey and financial market data, for instance, are available right at the end of the respective month, while data on economic activity are usually ... (2011) report that differences in the timing of data releases among individual series have large effects on their marginal predictive gains. ... Kalman filtering and smoothing handles unbalanced data sets in an efficient way. ... and xL;stAˆA‰ks1a„41 from the pre-sample ranging until 2000 Q4 using stepwise elimination as described in Section 2.

Title:Dynamic Factor Models
Author: Thomas B. Fomby, Juan Carlos Escanciano, Eric Hillebrand, Ivan Jeliazkov, R. Carter Hill
Publisher:Emerald Group Publishing - 2015-11-24

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