1,721,356 research outputs found
The Dynamic, the Static, and the Weak: Factor Models and the Analysis of High‐Dimensional Time Series
No full textSeveral fundamental and closely interconnected issues related to factor models are reviewed and discussed: dynamic versus static
loadings, rate-strong versus rate-weak factors, the concept of weakly common component recently introduced by Gersing, the
irrelevance of cross-sectional ordering and the assumption of cross-sectional exchangeability, the impact of undetected strong
factors, and the problem of combining common and idiosyncratic forecasts. Conclusions all point to the advantages of the General
Dynamic Factor Model approach of Forni, Hallin, Lippi, and Reichlin over the widely used Static Approximate Factor Model
introduced by Chamberlain and Rothschild
Dynamic Factor Models: A Genealogy
No full textDynamic factor models have been developed out of the need of analyzing and forecasting time series in increasingly high dimensions. While mathematical statisticians faced with inference problems in high-dimensional observation spaces were focusing on the so-called spiked-model-asymptotics, econometricians adopted an entirely and considerably more effective asymptotic approach, rooted in the factor models originally considered in psychometrics. The so-called dynamic factor model methods, in two decades, has grown into a wide and successful body of techniques that are widely used in central banks, financial institutions, economic and statistical institutes. The objective of this chapter is not an extensive survey of the topic but a sketch of its historical growth, with emphasis on the various assumptions and interpretations, and a family tree of its main variants
Multiple-Attribute Lorenz Functions and Gini Indices: A Measure Transportation Approach
No full textCzech Science Foundation http://dx.doi.org/10.13039/501100001824Deustche Forschungsgemeinschaf
Groupe Ib : Mathématiques. Sur l'efficacité asymptotique uniforme des procédures de rangs signés multivariés par Davy Paindaveine Rapports des commissaires
No full textHallin Marc, Gutt Simone. Groupe Ib : Mathématiques. Sur l'efficacité asymptotique uniforme des procédures de rangs signés multivariés par Davy Paindaveine Rapports des commissaires. In: Bulletin de la Classe des sciences, tome 16, n°7-12, 2005. pp. 388-390
Groupe I : Mathématique. A general theory of S-convex stochastic orderings with applications in actuarial sciences, par Michel Denuit. Rapports des commissaires
No full textHallin Marc, Mawhin Jean. Groupe I : Mathématique. A general theory of S-convex stochastic orderings with applications in actuarial sciences, par Michel Denuit. Rapports des commissaires. In: Bulletin de la Classe des sciences, tome 11, n°7-12, 2000. pp. 471-475
Discussion of “local quantile regression” by Spokoiny, Wang, and Härdle
No full textSCOPUS: no.jinfo:eu-repo/semantics/publishe
Inferential theory for generalized dynamic factor models
Full text linkWe provide the asymptotic distributional theory for the so-called General or Generalized Dynamic Factor Model (GDFM), laying the foundations for an inferential approach in the GDFM analysis of high-dimensional time series. By exploiting the duality between common shocks and dynamic loadings, we derive the asymptotic distribution and associated standard errors for a class of estimators for common shocks, dynamic loadings, common components, and impulse response functions. We present an empirical application aimed at constructing a “core” inflation indicator for the U.S. economy, which demonstrates the superiority of the GDFM-based indicator over the most common approaches, particularly the one based on Principal Components
Dynamic factor models with infinite-dimensional factor spaces: One-sided representations
No full textFactor model methods recently have become extremely popular in the theory and practice of large panels of time series data. Those methods rely on various factor models which all are particular cases of the Generalized Dynamic Factor Model (GDFM) introduced in Forniet al. (2000). That paper, however, rests on Brillinger’s dynamic principal components. The corresponding estimators are two-sided filters whose performance at the end of the observation period or for forecasting purposes is rather poor. No such problem arises with estimators based on standard principal components, which have been dominant in this literature. On the other hand, those estimators require the assumption that the space spanned by the factors has finite dimension. In the present paper, we argue that such an assumption is extremely restrictive and potentially quite harmful. Elaborating upon recent results by Anderson and Deistler (2008a, b) on singular stationary processes with rational spectrum, we obtain one-sided representations for the GDFM without assuming finite dimension of the factor space. Construction of the corresponding estimators is also briefly outlined. In a companion paper, we establish consistency and rates for such estimators, and provide Monte Carlo results further motivating our approach
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