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Author: Jushan Bai Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
In this paper we develop some econometric theory for factor models of large dimensions. The focus is the determination of the number of factors (r), which is an unresolved issue in the rapidly growing literature on multifactor models. We first establish the convergence rate for the factor estimates that will allow for consistent estimation of r. We then propose some panel criteria and show that the number of factors can be consistently estimated using the criteria. The theory is developed under the framework of large cross-sections (N) and large time dimensions (T). No restriction is imposed on the relation between N and T. Simulations show that the proposed criteria have good finite sample properties in many configurations of the panel data encountered in practice.
Author: Jushan Bai Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
In this paper we develop some econometric theory for factor models of large dimensions. The focus is the determination of the number of factors (r), which is an unresolved issue in the rapidly growing literature on multifactor models. We first establish the convergence rate for the factor estimates that will allow for consistent estimation of r. We then propose some panel criteria and show that the number of factors can be consistently estimated using the criteria. The theory is developed under the framework of large cross-sections (N) and large time dimensions (T). No restriction is imposed on the relation between N and T. Simulations show that the proposed criteria have good finite sample properties in many configurations of the panel data encountered in practice.
Author: Jushan Bai Publisher: Now Publishers Inc ISBN: 1601981449 Category : Business & Economics Languages : en Pages : 90
Book Description
Large Dimensional Factor Analysis provides a survey of the main theoretical results for large dimensional factor models, emphasizing results that have implications for empirical work. The authors focus on the development of the static factor models and on the use of estimated factors in subsequent estimation and inference. Large Dimensional Factor Analysis discusses how to determine the number of factors, how to conduct inference when estimated factors are used in regressions, how to assess the adequacy pf observed variables as proxies for latent factors, how to exploit the estimated factors to test unit root tests and common trends, and how to estimate panel cointegration models.
Author: Marc Hallin Publisher: 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.
Author: Robert A. Korajczyk Publisher: ISBN: Category : Languages : en Pages : 47
Book Description
An important issue in applications of multifactor models of asset returns is the appropriate number of factors. Most extant tests for the number of factors are valid only for strict factor models, in which diversifiable returns are uncorrelated across assets. In this paper we develop a test statistic to determine the number of factors in an approximate factor model of asset returns, which does not require that diversifiable components of returns be uncorrelated across assets. We find evidence for one to six pervasive factors in the cross-section of New York Stock Exchange and American Stock Exchange stock returns.
Author: Publisher: ISBN: Category : Languages : en Pages : 41
Book Description
We propose a refinement of the criterion by Bai and Ng [2002] for determining the number of static factors in factor models with large datasets. It consists in multiplying the penalty function times a constant which tunes the penalizing power of the function itself as in the Hallin and Lika [2007] criterion for the number of dynamic factors. By iteratively evaluating the criterion for different values of this constant, we achieve more robust results than in the case of fixed penalty function. This is shown by means of Monte Carlo simulations on seven data generating processes, including heteroskedastic processes, on samples of different size. -- Approximate factor models ; Information criterion ; Number of factors
Author: Michael P. Clements Publisher: 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.
Author: Mario Forni Publisher: Oxford University Press ISBN: 9780198288008 Category : Business & Economics Languages : en Pages : 264
Book Description
Through careful methodological analysis, this book argues that modern macroeconomics has completely overlooked the aggregate nature of the data. In Part I, the authors test and reject the homogeneity assumption using disaggregate data. In Part II, they demonstrate that apart from random flukes, cointegration unidirectional Granger causality and restrictions on parameters do not survive aggregation when heterogeneity is introduced. They conclude that the claim that modern macroeconomics has solid microfoundations is unwarranted. However, some important theory-based models that do not fit aggregate data well in their representative-agent version can be reconciled with aggregate data by introducing heterogeneity.
Author: Jushan Bai
Author: Jushan Bai
Author: 
Author: Jushan Bai
Author: Marc Hallin
Author: Robert A. Korajczyk
Author: 
Author: Jörg Breitung
Author: Michael P. Clements
Author: Mario Forni
Author: Gregory Connor