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Found 10 papers, 0 papers with code
Dynamic Factor Models: a Genealogy
Dynamic factor models have been developed out of the need of analyzing and forecasting time series in increasingly high dimensions.
Asymptotic equivalence of Principal Components and Quasi Maximum Likelihood estimators in Large Approximate Factor Models
Finally, we give some alternative sets of primitive sufficient conditions for mean-squared consistency of the sample covariance matrix of the factors, of the idiosyncratic components, and of the observed time series, which is the starting point for Principal Component Analysis.
Hierarchical DCC-HEAVY Model for High-Dimensional Covariance Matrices
We introduce a new HD DCC-HEAVY class of hierarchical-type factor models for conditional covariance matrices of high-dimensional returns, employing the corresponding realized measures built from higher-frequency data.
Quasi Maximum Likelihood Estimation of High-Dimensional Factor Models: A Critical Review
We review Quasi Maximum Likelihood estimation of factor models for high-dimensional panels of time series.
Multidimensional dynamic factor models
This paper generalises dynamic factor models for multidimensional dependent data.
On Estimation and Inference of Large Approximate Dynamic Factor Models via the Principal Component Analysis
Finally, we give some alternative sets of primitive sufficient conditions for mean-squared consistency of the sample covariance matrix of the factors, of the idiosyncratic components, and of the observed time series, which is the starting point for Principal Component Analysis.
Modelling Large Dimensional Datasets with Markov Switching Factor Models
We study a novel large dimensional approximate factor model with regime changes in the loadings driven by a latent first order Markov process.
Factor Network Autoregressions
We propose a factor network autoregressive (FNAR) model for time series with complex network structures.
Inference in heavy-tailed non-stationary multivariate time series
We propose a novel methodology which does not require any knowledge or estimation of the tail index, or even knowledge as to whether certain moments (such as the variance) exist or not, and develop an estimator of the number of stochastic trends $m$ based on the eigenvalues of the sample second moment matrix of $y_{t}$.
Quasi Maximum Likelihood Estimation of Non-Stationary Large Approximate Dynamic Factor Models
This paper considers estimation of large dynamic factor models with common and idiosyncratic trends by means of the Expectation Maximization algorithm, implemented jointly with the Kalman smoother.
