Specification And Estimation Of Bayesian Dynamic Factor Models
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Author | : Laura Jackson Young |
Publisher | : |
Total Pages | : 46 |
Release | : 2019 |
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ISBN | : |
We compare methods to measure comovement in business cycle data using multi-level dynamic factor models. To do so, we employ a Monte Carlo procedure to evaluate model performance for different specifications of factor models across three different estimation procedures. We consider three general factor model specifications used in applied work. The first is a single- factor model, the second a two-level factor model, and the third a three-level factor model. Our estimation procedures are the Bayesian approach of Otrok and Whiteman (1998), the Bayesian state space approach of Kim and Nelson (1998) and a frequentist principal components approach. The latter serves as a benchmark to measure any potential gains from the more computationally intensive Bayesian procedures. We then apply the three methods to a novel new dataset on house prices in advanced and emerging markets from Cesa-Bianchi, Cespedes, and Rebucci (2015) and interpret the empirical results in light of the Monte Carlo results.
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Release | : 2015 |
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Author | : Siem Jan Koopman |
Publisher | : Emerald Group Publishing |
Total Pages | : 685 |
Release | : 2016-01-08 |
Genre | : Business & Economics |
ISBN | : 1785603523 |
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.
Author | : Michael P. Clements |
Publisher | : OUP USA |
Total Pages | : 732 |
Release | : 2011-07-08 |
Genre | : Business & Economics |
ISBN | : 0195398645 |
Greater data availability has been coupled with developments in statistical theory and economic theory to allow more elaborate and complicated models to be entertained. These include factor models, DSGE models, restricted vector autoregressions, and non-linear models.
Author | : Hairong Song |
Publisher | : |
Total Pages | : 268 |
Release | : 2009 |
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Author | : Sylvia Kaufmann |
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Total Pages | : |
Release | : 2013 |
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Author | : Markus Pape |
Publisher | : |
Total Pages | : 0 |
Release | : 2015 |
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Author | : Piotr Białowolski |
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Total Pages | : 0 |
Release | : 2014 |
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Author | : Giovanni Petris |
Publisher | : Springer Science & Business Media |
Total Pages | : 258 |
Release | : 2009-06-12 |
Genre | : Mathematics |
ISBN | : 0387772383 |
State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms. The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online. No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.
Author | : Jörg Breitung |
Publisher | : |
Total Pages | : 40 |
Release | : 2016 |
Genre | : |
ISBN | : |
Factor models can cope with many variables without running into scarce degrees of freedom.