Author: Antonis DemosPublisher:
ISBN:
Category :
Languages : en
Pages : 31
Book Description
An EM Algorithm for Conditionally Heteroskedastic Factor Models
Author: Antonis DemosAn em-based algorithm for conditionally heteroskedastic factor models
Author: Antonis DemosPublisher:
ISBN:
Category : Economics
Languages : en
Pages : 38
Book Description
An em-based algorith for conditionally heteroskedastic factor models
Author: Enrique SentanaMacroeconometrics
Author: Kevin D. HooverPublisher: Springer Science & Business Media
ISBN: 940110669X
Category : Business & Economics
Languages : en
Pages : 575
Book Description
Each chapter of Macroeconometrics is written by respected econometricians in order to provide useful information and perspectives for those who wish to apply econometrics in macroeconomics. The chapters are all written with clear methodological perspectives, making the virtues and limitations of particular econometric approaches accessible to a general readership familiar with applied macroeconomics. The real tensions in macroeconometrics are revealed by the critical comments from different econometricians, having an alternative perspective, which follow each chapter.
Identification, Estimation and Testing of Conditionally Heteroskedastic Factor Models
Author: Gabriele FiorentiniPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
We investigate the effects of dynamic heteroskedasticity on statistical factor analysis. We show that identification problems are alleviated when variation in factor variances is accounted for. Our results apply to dynamic APT models and other structural models. We also find that traditional ML estimation of unconditional variance parameters remains consistent if the factor loadings are identified from the unconditional distribution, but their standard errors must be robustified. We develop a simple preliminary LM test for ARCH effects in the common factors, and discuss two-step consistent estimation of the conditional variance parameters. Finally, we conduct a detailed simulation exercise.
A Spectral EM Algorithm for Dynamic Factor Models
Author: Gabriele FiorentiniPublisher:
ISBN:
Category : Econometric models
Languages : en
Pages : 36
Book Description
We introduce a frequency domain version of the EM algorithm for general dynamic factor models. We consider both AR and ARMA processes, for which we develop iterative indirect inference procedures analogous to the algorithms in Hannan (1969). Although our proposed procedure allows researchers to estimate such models by maximum likelihood with many series even without good initial values, we recommend switching to a gradient method that uses the EM principle to swiftly compute frequency domain analytical scores near the optimum. We successfully employ our algorithm to construct an index that captures the common movements of US sectoral employment growth rates.
Journal of Econometrics
Author: Bayesian Inference in the Social Sciences
Author: Ivan JeliazkovPublisher: John Wiley & Sons
ISBN: 1118771125
Category : Mathematics
Languages : en
Pages : 266
Book Description
Presents new models, methods, and techniques and considers important real-world applications in political science, sociology, economics, marketing, and finance Emphasizing interdisciplinary coverage, Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology and examines an array of topics in model formulation, estimation, and applications. The book presents recent and trending developments in a diverse, yet closely integrated, set of research topics within the social sciences and facilitates the transmission of new ideas and methodology across disciplines while maintaining manageability, coherence, and a clear focus. Bayesian Inference in the Social Sciences features innovative methodology and novel applications in addition to new theoretical developments and modeling approaches, including the formulation and analysis of models with partial observability, sample selection, and incomplete data. Additional areas of inquiry include a Bayesian derivation of empirical likelihood and method of moment estimators, and the analysis of treatment effect models with endogeneity. The book emphasizes practical implementation, reviews and extends estimation algorithms, and examines innovative applications in a multitude of fields. Time series techniques and algorithms are discussed for stochastic volatility, dynamic factor, and time-varying parameter models. Additional features include: Real-world applications and case studies that highlight asset pricing under fat-tailed distributions, price indifference modeling and market segmentation, analysis of dynamic networks, ethnic minorities and civil war, school choice effects, and business cycles and macroeconomic performance State-of-the-art computational tools and Markov chain Monte Carlo algorithms with related materials available via the book’s supplemental website Interdisciplinary coverage from well-known international scholars and practitioners Bayesian Inference in the Social Sciences is an ideal reference for researchers in economics, political science, sociology, and business as well as an excellent resource for academic, government, and regulation agencies. The book is also useful for graduate-level courses in applied econometrics, statistics, mathematical modeling and simulation, numerical methods, computational analysis, and the social sciences.
Dynamic Factor Models
Author: Siem Jan KoopmanPublisher: Emerald Group Publishing
ISBN: 1785603523
Category : Business & Economics
Languages : en
Pages : 685
Book Description
This volume explores dynamic factor model specification, asymptotic and finite-sample behavior of parameter estimators, identification, frequentist and Bayesian estimation of the corresponding state space models, and applications.
Computational Science - ICCS 2006
Author: Vassil N. AlexandrovPublisher: Springer
ISBN: 3540343865
Category : Computers
Languages : en
Pages : 1128
Book Description
This is Volume IV of the four-volume set LNCS 3991-3994 constituting the refereed proceedings of the 6th International Conference on Computational Science, ICCS 2006. The 98 revised full papers and 29 revised poster papers of the main track presented together with 500 accepted workshop papers were carefully reviewed and selected for inclusion in the four volumes. The coverage spans the whole range of computational science.