Author: C. HeijPublisher:
ISBN:
Category :
Languages : en
Pages : 6
Book Description
On the Consistency of Identification by Dynamic Factor Models
Author: C. HeijThe Oxford Handbook of Economic Forecasting
Author: Michael P. ClementsPublisher: OUP USA
ISBN: 0195398645
Category : Business & Economics
Languages : en
Pages : 732
Book Description
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.
Identification and Estimation of Dynamic Factor Models
Author: Jushan BaiDynamic Factor Models
Author: Jörg BreitungPublisher:
ISBN: 9783865580979
Category :
Languages : en
Pages : 29
Book Description
Identification of Static and Dynamic Factor Models
Author: Marcelle ChauvetPublisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 33
Book Description
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.
Time Series in High Dimension: the General Dynamic Factor Model
Author: Marc HallinPublisher: World Scientific Publishing Company
ISBN: 9789813278004
Category : Business & Economics
Languages : en
Pages : 764
Book Description
Factor models have become the most successful tool in the analysis and forecasting of high-dimensional time series. This monograph provides an extensive account of the so-called General Dynamic Factor Model methods. The topics covered include: asymptotic representation problems, estimation, forecasting, identification of the number of factors, identification of structural shocks, volatility analysis, and applications to macroeconomic and financial data.
The Generalized Dynamic Factor Model
Author: Mario ForniErrors-in-Variables Methods in System Identification
Author: Torsten SöderströmPublisher: Springer
ISBN: 3319750011
Category : Technology & Engineering
Languages : en
Pages : 495
Book Description
This book presents an overview of the different errors-in-variables (EIV) methods that can be used for system identification. Readers will explore the properties of an EIV problem. Such problems play an important role when the purpose is the determination of the physical laws that describe the process, rather than the prediction or control of its future behaviour. EIV problems typically occur when the purpose of the modelling is to get physical insight into a process. Identifiability of the model parameters for EIV problems is a non-trivial issue, and sufficient conditions for identifiability are given. The author covers various modelling aspects which, taken together, can find a solution, including the characterization of noise properties, extension to multivariable systems, and continuous-time models. The book finds solutions that are constituted of methods that are compatible with a set of noisy data, which traditional approaches to solutions, such as (total) least squares, do not find. A number of identification methods for the EIV problem are presented. Each method is accompanied with a detailed analysis based on statistical theory, and the relationship between the different methods is explained. A multitude of methods are covered, including: instrumental variables methods; methods based on bias-compensation; covariance matching methods; and prediction error and maximum-likelihood methods. The book shows how many of the methods can be applied in either the time or the frequency domain and provides special methods adapted to the case of periodic excitation. It concludes with a chapter specifically devoted to practical aspects and user perspectives that will facilitate the transfer of the theoretical material to application in real systems. Errors-in-Variables Methods in System Identification gives readers the possibility of recovering true system dynamics from noisy measurements, while solving over-determined systems of equations, making it suitable for statisticians and mathematicians alike. The book also acts as a reference for researchers and computer engineers because of its detailed exploration of EIV problems.
Author: Matteo Barigozzi